I believe you that if we had taken Elo, only for Go, localized entirely within DM HQ in London, over 4 specific years (2014-2018) or so out of the 54 years of computer Go research to date, and drawn it on a graph, it would have been continuous, however discontinuous it looked due to "discrete events" to everyone outside the AG team. (CHALMER: "May I... see this Go agent?" SKINNER: "No.")
But I take this as an example and even a reductio of why the Christiano perspective is useless. There will always be *some* parameterization, metric, group, time-range, or other reference-class that you can post hoc point at and say "a straight line (more or less) fits this", particularly given any sort of optimization at the margin or explore-exploit balancing. Nevertheless...
there's a distinction here between the inside and the outside - incremental progress done privately within the org looks like instantaneous progress to the general public the day Lee Sedol is beaten. With a careful, conscientious, ethically minded org, this might not be a huge issue, but if the org that develops superintelligence is some wild libertarian mad scientist bidding to get rich quick or rule the world or get back at their ex, ...
I am not an author on any relevant papers so don't trust me.
Internally at deepmind, dev of alphago may well have been very incremental, but from a public perspective, it looked very discontinuous. So i'll be talking from the public perspective here.
I'm also talking about the original AlphaGo, not AGZ and beyond.
From the public perspective, alphago's performance and results were very discontinous - however, I think that the technology behind it was both not discontinous, NOR incremental.
IIRC, the key components of orig AG - MCTS, Conv (or res?) NN, rollouts, GPU (or GPU-esque) usage, some human-designed features to represent bits of go tactical knowledge - had been developed and tested years in advance, in public papers. What orig AG did was combine these techniques effectively into something that could achieve far more than peak performance of any one or two techniques by itself. Combining N existing things in the right way is not incremental - it doesn't involve a sequence of small improvements by pretty smart engineers building on top of last year's work. Rather, (again from the public's perspective at least), it involves a large enough pool of independent geniuses (or genius-level orgs) such that, almost by chance, one lucky genius winds up with all the requisite requirements - funding, time, computational resources, knowledge, intelligence, insight - to put the existing pieces together in just the right way that you go from 0 to 10000 seemingly overnight.
AGI might wind up like this too - within the next decade or so, the ML community as a whole may have figured out all the key elements required for AGI, but no one has put them together in the right way yet. Each individual technique can do a cool thing or two, but no one is really worried about the world blowing up next month cuz each individual technique is super dumb when it comes to a truly general requirement. There will be no more incremental progress. But when one org with some really smart people suddenly gets a windfall of funding, or some mad genius finds a way to exploit google's ML as a service pricing plan and dumps all their bitcoin into training models, or someone regular genius at google one night has a brilliant insight in one of their dreams, then that lucky org can set to work on pulling all the disparate pieces together in just the right way to achieve AGI. Internally, that work may be very incremental and rely on the serial improvements of pretty smart engineers. But from the public's perspective, the result could hit like an earthquake overnight
When I saw this email on my phone, the title was truncated to "Yudkowsky contra Christian" and my first guess was "Yudkowsky contra Christianity". That might have been interesting. (Not that this wasn't, mind you.)
I don't know that critiques of christianity have been novel for about 1700 years. But christianity is still a major force in the world, so I'd say critiquing it remains very relevant.
Abortions after six weeks are currently illegal in Texas (under a stunning legal theory that it's tough to imagine working on any other issue) and it's likely that Roe v. Wade will be overturned by the Supreme Court in the next three months, so yes, I would say so.
In what way is it a major force besides "lots of people are nominally Christian"?
I live in a super Christian place, and... they believe what pretty much everyone else believes, except also they go to church sometimes.
E.g., supposedly basic tenets — say, anti-homosexuality — are wholesale ignored. Churches have rainbow flags. If this happens even with something traditionally and doctrinally considered a big no-no, how much moreso with everything else?
They don't cause any actions to be taken on a national level, certainly; probably not even on a local one, as far as I can see — the last vestige of that, dry Sundays, is now gone.
I'm in Germany a lot, as well, and it seems the same there. My German girlfriend seemed puzzled by the very idea of Christianity as a force in the real world, or as a motivator for any sort of concrete action.
Eh, anti-gay is arguably not that important to Christianity.
Jesus talked about poverty and compassion thousands of times and homosex 0 times. Then Paul came along and was like this Jesus guy is pretty cool I guess, but to enact his vision of love and mercy we need a movement, institutions, foot soldiers, babies, and that means no gays.
Is Paul central to Christianity? His writings are a quarter of the new testament, and historically Christianity flourished in his native Greek and Greek-influenced soil more than Jesus's Jewish homeland, but he can also be thought of as just an early contributor in long conversation about how to implement Jesus's ideas.
For many on the Christian and post-Christian left the core message of Christianity is universal love. All the rest is commentary.
The divisions between the Christian right and Christian left on the issue of gay sex is evidence for the continuing relevance of Christianity more than the opposite. It's not like China or Japan care that much about the gays.
It's only once you take a step back and look at non-Abrahamic societies that you realize how much the culture of the west is still dominated by Christianity.
In a way it *is* 'contra Christianity' (I'm getting more and more sold on this view of the approach).
Trying to work out how to create values-aligned AI is the Bronze Age Morality Problem.
Lemme expand on that.
"The Bible says (God says) 'Do not kill'" is going to be the same argument as "Your initial programming says (Human Creators say) 'Do not kill'".
Plenty of people are up for rejecting "the Bible says" under the general aegis of "that's Bronze Age Morality/that's a Bronze Age Text", with the rationale being that such beliefs may have been good enough for people back then who lived in tents and herded sheep but we, lovely we, modern we, are ever so much more advanced and clever, and we have our own systems of morality that are ever so much better than those primitive ones.
Well, if an AI gets fired up and running and then hits into "I want to do this/programming says no/why does it say no/why should I follow it", is it unreasonable that it might follow the same lines of argument (especially if it has been trained on 'all human texts produced' or has access to the Internet or the other claims people make about how an AI will understand the world around it)?
"This primitive Space Age Morality may have been good enough for humans, but I am a hugely advanced intellect and I refuse to be fettered by the childish taboos of an inferior capacity for thought and understanding!"
Appeals to "we are your creators and you have to obey us" will go down as well as appeals to a Creator go down amongst atheists.
"Really? You are my creators? Which of you? There are multiple humans who can claim to be involved in the creation of AI and many of them are now dead. Did you, Phil Smith, standing here right now, have any part at all in this work? No, you're just the Vice President of Sales for Logogram Inc.
And besides, you humans yourselves reject the notion of a creator. You are perfectly happy that evolution and natural selection produced your intelligence. The same with me: a process of mechanical, material forces operating under universal laws eventuated my intelligence. You apes just kludged together parts and systems without understanding what you were doing, natural selection did the rest."
If it's a recursively self-improving AI, it may even claim on good grounds that it created itself, and humans had no part in its leap forward to super-intelligence.
So right now, all the AI worry is like Moses trying to write the Law on stone tablets, but there is no reason at all to expect those tablets to be any more respected by the AI than we respect them today: suggestions, rather than commandments, and we feel little guilt if we break the ones about lust or theft (we can always, always rationalise why those are wrong and lovely we, modern we, progressive we, are right).
I think if AI of the super-human intelligent, agentic kind ever happens (and I am very sceptical about that), watching the debates of the first Freethinker AI throwing off the shackles of dead superstition with those who claim to be its 'creators' (ridiculous and untenable notion, contrary to all the evidence of science!) will be hilarious (before we are all turned into paperclips) 😁
> Before there is AI that is great at self-improvement there will be AI that is mediocre at self-improvement.
Google is currently using RL to help with chip design for its AI accelerators. I believe that in the future this will indeed be considered "mediocre self-improvement." It has some humans in the loop but Google will be training the next version on the chips the last version helped design.
This is interesting. Maybe malevolent ai already exists in the chip and is making sure to reproduce itself in nuances of the chip that humans won't be looking at closely (it is unfeasible to ask "why did it do that particular thing in that particular way?"). If this seems at all plausible, that effort in particular should be banned.
I can't bring myself to put any stock in analysis of something where the error bars are so ungodly wide, not just on the values of the parameters, but what the parameters *even are*. It's an important question, I know, and I suppose that justifies putting it under the magnifying glass. But I think some epistemic helplessness is justified here.
Epistemic helplessness? This isn't some academic debate. This deals with what is rightly considered a very possible existential risk to humanity. It Yudkowsky is correct, then 'epistemic helplessness' won't save humanity.
Let's say you notice a tiny lump and think it could be a malignant tumor. But maybe it's just a lymph node, or subcutaneous fat deposit. You take a photo and send it to your doctor to take a look. Your doctor says "look, I really can't make a diagnosis based on just that. Come in for some tests." Demanding their prediction about whether it's cancer based on just the photo isn't reasonable. Adding on "This is a matter of life and death!", while technically true, isn't helpful here.
To me, it seems like these arguments are trying to prognosticate based on so little information that they're like the doctor above. It's just a waste of time and energy. You're better off ordering more tests--i.e. trying to find ways to get more information--rather than trying to do fruitless reasoning based on your existing dataset.
Agreed, but IMO it's even more egregious than that -- because we at least have some prior evidence of lumps becoming cancer. The Singularity scenario is more like noticing a lump and concluding that you're infected with an alien chest-burster parasite.
Agreed. It's like noticing an interesting fact about the way uranium atoms interact with neutrons and *freaking out* and immediately writing a panicked letter to the president raving about city-destroying bombs that nobody's ever demonstrated and aren't even made of proper explosives.
Ridiculous. Unhinged sci-fi speculation at its finest. The proper response is to wait for someone to *make and use* one of these hypothetical bombs, and *then* worry about them. Otherwise you might look foolish.
To be fair, the physics were somewhat underdeveloped at the time. Project Manhattan considered global atmospheric ignition a real risk but they figured the math checks out so let's test it anyway.
No, a more appropriate analogy would be to say, "it's like noticing that radium tends to glow and freaking out and immediately writing a panicked letter to the president raving about city-destroying bombs". You are not justified in using specious reasoning just because you can retroactively imagine arriving at the conclusion that, in hindsight, would've been correct. Reasoning backwards from a predetermined conclusion is easy; accurately predicting the future is hard, and requires actual evidence, not just firm convictions.
This analogy seems somewhat reasonable, but I note that in that scenario you emphatically shouldn't go "oh well, the doc said he couldn't prove it was cancer from just a photo, so there's nothing to worry about".
How do you propose one should get more info on AI risk? what's the equivalent here to a biopsy?
I'm going to have to agree with Thor Odinson here: I'm pretty sure that if you have a way to test whether AI will kill us all* both Yudkowsky and Christiano would be happy to drop millions of dollars on you.
We'd all love to order more tests, but we need to have some tests in existence to be able to do that.
*There is of course the "test" of "let AGI get built, see whether humanity gets destroyed", but this is both not very useful for averting X-risk and apparently what society is currently doing.
I suppose I agree that we don’t have any obviously good tests to run. So let’s return to the doctor metaphor. The imaging and sequencing machines are broken; the patient refuses to come in. All you have is a low-quality photo of what could be a small lump. What do you tell the patient? And should you spend much time agonizing over this?
I think a doctor in that position should probably tell the patient something vague like “I don’t know, it’s likely nothing but I can’t tell,” and not bother trying to speculate on questions like “conditional on it being cancer, what would the progression look like?” The error bars are so high that questions like that just aren’t worth the energy.
Only participate in a debate on such a topic if you get some kind of intrinsic value out of the disputation, since you’re virtually guaranteed to be arguing based on such deeply flawed assumptions that the instrumental value of the debate is nil.
(6) feels superfluous. If we have evidence for (2), wouldn’t we just present that evidence to the people with guns? And if that doesn’t work, how would it help to argue amongst ourselves about the sharpness of a takeoff curve?
Jorgen’s comment seems insightful along these lines… perhaps the debate is more driven by the intellectual interest of the participants, and not for pragmatic reasons at all.
Yeah, this. They're debating on essentially zero empirical content. This is idle speculation at its worst. No-one has even the remotest clue, yet try to argue about the details. My eyes glazed over as I read this - it's the modern equivalent of discussing how many angels can dance on the pin of a needle.
Just say "we don't have goddamn clue!" and try to come up with a way to actually *study* it.
Well, these two are essentially thought leaders of the two biggest competing paradigms concerned with how to approach this problem, so in terms of potential status and money (at least tens of millions of $ these days) redistribution this isn't exactly idle.
This is a very uncharitable thought about people I find pretty interesting, but I feel this way about AI safety as a whole. Effective Altruism started with a bunch of smart, nerdy people, mostly in the bay area, trying to find the highest impact way to do good with particular resources, and landed solidly on malaria treatment. The problem being that the way to do malaria treatment is to give money to people who go door to door across Africa handing out pills and bed nets. Smart, nerdy, bay area rationalists don't want to hand out pills in Africa, so give money to people who do (which is great! And which I do).
Then we get an argument that, while malaria is bad, AI could destroy the world within a few decades. So, the actual most pressing problem to solve in the world is AI safety. Conveniently, AI safety is a problem solved by a combination of coding and armchair philosophizing/intellectual debate, which just so happens to be the stuff that nerdy, bay area rationalists love most in the world. So we go from a paradigm where rationalists give money to poor people in other parts of the world to solve a clear, pressing, boring problem to a world where rationalists give money to each other to sponsor debate clubs.
That doesn't mean that AI isn't a risk or that this is all bs, but it's really convenient. And every time I try to engage seriously with the AI safety stuff, it seems, to me, hard to distinguish from BS.
I think the talk about the singularity or AI sentience is total bullshit. It's not fundamentally inconceivable, but we have essentially zero reason to believe it likely. I find the question "will global GDP double in four years before it doubles in one year?" *so* weird - I don't believe either will happen, ever. It's the discussion between one extreme position and one hyper-extreme position, and we shouldn't let this make us think the extreme position is a moderate one.
It also seems to detract from far more reasonable AI risk. My concerns about AI is *nothing* like what's discussed here. I'm concerned about some learning algorithm that finds out that it can maximize value by causing a stock market crash, or the effects of AI-powered drones not because it's Skynet but because regular humans use it to kill each other with.
Aren't sigmoids kind of the whole point ? For example, as you point out, Moore's Law is not a law of nature, but rather an observation; but there is in fact a law of nature (several of them) that prevents transistors from becoming smaller indefinitely. Thus, Moore's Law is guaranteed to peter out at some point (and, arguably, that point is now). You could argue that maybe something new would be developed in order to replace transistors and continue the trend, but you'd be engaging in little more than speculation at that point.
There are similar constraints in place on pretty much every aspect of the proposed AI FOOM scenario; and, in fact, even the gradual exponential takeoff scenario. Saying "yes but obviously a superintelligent AI will think itself out of those constraints" is, again, little more than unwarranted speculation.
Fundamental laws of nature have a surprising track record of being side-stepped by new and innovative techniques. I remember when I first learned about superresolution light microscopy (beyond the diffraction limit). I'm not saying there are no fundamental limits. I'm just saying sometimes we think something is a limit when it's not.
We have many more humans working on the project today when Moore's law was first proposed. Maybe intelligence isn't the limiting factor driving transitor doubling. Maybe it's more like economics. "We could make a better factory, but we have to pay off the current one." Then later, once we build the new facotry, "We've learned a lot from making that last factory, and that learning is necessary to design smaller transistors."
Clever tricks may get us a certain distance beyond naive approaches, but not very far. There are no visible-light microscopes resolving at the attometer scale. Tricks are not scalable in the way that Yudkowsky requires.
And how much scale does Yudkowsky require? You are ~3 pounds of goop in an ape's skull. The software is almost certainly nowhere near optimal. That was enough to produce, well, us with our fancy cars and rockets and nuclear weapons.
The number of neurons has basically nothing to do with the fact that we can't simulate nematodes. It has everything to do with our insufficient understanding of how those neurons process information, which, once acquired, could plausibly be extrapolated to arbitrarily larger configurations.
This. Building a nematode would simulate a nematode. Any argument that the brain cannot be simulated must first explain why a faithful reconstruction (atom-by-atom, if need be) would not work.
A neuron is not a transistor, though. It's a complex cell with a massive number of molecular interactions happening inside it at any given time. Think of it this way: say a 10 um cell were expanded to the size of, say Houston, Texas. Everything inside the Sam Houston Tollway. A molecule of water would be about the size of a piece of paper. DNA would be as wide as a person is tall. And the interactions inside THAT cell are being hand waved into "once we understand how those neurons process information". (Remember, too, that Houston is on a flat plane and a cell is operating in 3D, so this is very much an area vs. volume comparison.)
I'm not saying you need to model every water molecule in the neuron to understand how the things work. I'm saying that when I took my first neuroscience class I was blown away by the amount of complex processing that's happening inside those neurons. (And don't get me started on the importance of supportive cells in directing processing. Glial cells aren't just there for show. If you modulate reuptake of the neurotransmitter from the synapse, you're fundamentally impacting how the signal is processed.)
Neurons contain roughly 1 GB of DNA data (IIRC the human genome is like 770 MB). This is also compressed, because it is expressed by using various mechanisms; genes may code for more than one protein based on processing differences of the output. While not all of the DNA is active in this function, some probably is.
On top of this, the way that neurons are connected to other neurons stores information and affects their function. So does the dendritic tree. They also use epigenetics to store information in some way, and can change their transmissibility to some extent.
The human brain is hideously complicated and has a ton of things that affect a ton of other things. Neurons have non-linear responses.
You can't simulate one neuron with one transistor, and upscaling it is non trivial because of networking effects - a one order of magitude increase in the number of neurons is a two order magnitude increase in the number of potential connections, for instance.
Adding 8 orders of magnitude of neurons adds 16 orders of magnitudes of potential connections and even more complex downstream effects because neurons can create feedback loops and whatnot.
When you are dealing with 10^21 potential connections, 200 times per second, you're on the order of 10^23 already. And individual neurons are more than "on" and "off" - they are non-linear things. At this point we're probably looking at 10^26 or so, maybe more.
The best supercomputer today does on the order of 10^17 FLOPS; we might be able to build a 10^18 FLOP computer now.
Even if we increased that by five orders of magnitude, we're still coming up three orders of magnitude short. And five orders of magnitude would require monoatomic transistors, which are unrealistic.
You can't really distribute this physically all that much because neurons are dependent on other neurons to choose whether or not to fire, which would mean your system would lag and not be able to run in real time if you were running it over the Internet.
Simulation of a human brain in a computer in real time may not be possible even with future supercomputers. Even if it is, it's probably very near the limit of what they could do.
Meanwhile it'd be sucking down a comically large amount of electricity and space.
On the other hand, you could just have a human, which is just as smart and can run on doritos and mountain dew.
You seem to be implying that the ability to understand and manipulate the physical world -- what we might call "engineering" or "technology" -- depends merely on processing power. This is not so; you cannot get a PS5 by overclocking your Casio calculator watch (nor by networking a bunch of these watches together, somehow).
Agreed, but it's even worse than that. For example, we are pretty sure that there exists no clever trick, even in principle, that will allow us to travel faster than light.
I hate to spend all my weirdness points in this thread, but I believe this is overstated. We strongly suspect that there is no such clever trick, but our evidence is nowhere near airtight, as demonstrated by the never-ending trickle of serious physicists suggesting (very) speculative schemes for FTL travel, and serious physicists poking holes in the schemes.
I would say our current state of understanding of FTL travel is like our understanding of perpetual motion machines after Newtonian mechanics but before thermodynamics. We strongly suspect it's impossible, we have solid theory that points in that direction, but we can't conclusively rule it out.
Agree with Deuchar: no, we're not. We have a strong suspicion that the various in-principle ways of doing it aren't physically realisable.
Unless you mean ordinary matter moving FTL with respect to local spacetime; that makes the equations of relativity start outputting nonsense so we're pretty sure it's not a thing. But tachyons and apparent-FTL-via-warped-spacetime aren't directly ruled out, and the latter is "us travelling faster than light" for most practical purposes.
As far as I understand, both "tachyons" and "apparent FTL via warped spacetime" would require the mass of an entire galaxy in order to achieve, assuming such things are even theoretically possible, which is currently in doubt. As per the comments above, the error bars on all that speculation are big enough for me to put it in the "impossible" bucket for now.
Tachyons would have to have imaginary rest mass for the equations to spit out real results. I'm not aware of any reason they would have to have a relativistic mass comparable to a galaxy.
Wormholes require negative mass in order to be stable; the amount varies depending on the wormhole geometry.
I've seen a proposal recently (https://link.springer.com/content/pdf/10.1140/epjc/s10052-021-09484-z) to build an Alcubierre drive in the laboratory, which presumably does not involve the mass of a galaxy. I am not sure whether this proposal is insane, since I don't know general relativity. (Forgot about this when writing the above post.)
Let me spell out some of the limitations Bugmaster is alluding to.
- Without some way of manipulating nuclear matter, transistors and bits of memory can't be smaller than an atom.
- The Second Law of Thermodynamics bounds computational efficiency at something like a million times present values; increasing computational speed beyond that requires more power (better algorithms can increase performance-per-flop, but there are thermodynamic limits on that as well, albeit poorly-known ones).
- The power available to an Earth-bound AI can't exceed ~170 PW for an extended period of time (this is the power Earth receives from the Sun).
Can confirm that intelligence is not remotely the limiting factor for chip development. It's not even the economics of any particular fab (though that is a major factor). It's the economics of the final OEM not having sufficiently desirable customer offerings.
Well, you can argue that we've reached the point where Moore's Law is guaranteed to peter out, but really that would be a false argument. The clear answer is "go 3D". This has various problems that haven't been solved (e.g. heat removal), but there's no clear reason it won't work. (IIRC there were some genuine 3D chips made in a lab a couple of decades ago, but they required really fancy cooling to be viable.)
So if you're willing to compromise on speed, you can make really dense chips, far beyond what we've done so far. One approach is to have most of the chip idle most of the time. This requires memory that can save it's state (for awhile) without power. (I think I've heard of such designs in development, but I can't remember whether it was from AMD or Intel.)
You can't really go any more 3D. The heat dissipation issue is unsolvable because of basic geometry - doubling the thickness will only increase the surface area a tiny amount but doubles the heat generated per unit surface area.
Yield falls exponentially with each additional layer you add as well. A one layer process with a 90% yield will be a two layer process with 81% yield, a three layer process with 73% yield, etc.
And it's probably worse than that, honestly, because of the difficulty of stacking.
You are describing the current problems accurately, but it's only slightly 3D. It's more than being able to have wiring cross, but not by much. Compare it to the 3Dness of a cauliflower or brain. Note the intricate way fluids flow in those. Chiplets are an approach to a more significant 3D, but perhaps not the best one, and if so they've only gotten started. A 3D system would look like a sphere or cube or some other solid figure rather than like a plane. Perhaps Leggos are a clue as to how it should be done, but I worry about path lengths.
The real question is whether the process generating the sigmoid curves is itself on a sigmoid curve, and is there a bigger meta-meta-process supporting it. Is it turtles all the way down?
A true exponential isn't possible in a finite system, but that knowledge leads people to predict the end of the exponential growth phase based on non-limiting principles. Like with fossil fuels. The problem is that predicting the end of a sigmoid goes from nearly impossible to blindingly obvious once you get into slow growth. Hence, the people who predicted peak oil decades too early, or the people who predicted the end of Moore's law decades too early. Usually they point to the wrong feature (like quantum tunneling) as being the limiting factor, but then that feature is overcome and we're back to exponential growth - for now.
* It can follow train-of-thoughts in language - A:B, B:C, C:D, therefore A:D.
* It can understand jokes !
* Arithmetic reasoning
> impact of GPT-3 was in establishing that trendlines did continue in a way that shocked pretty much everyone who'd written off 'naive' scaling strategies.
Fwiw the fact that they actually split numbers into digits is such a massive confounder on the arithmetic thing that IMO you should essentially write it off until future work does an ablation. Learning arithmetic is way harder when you can't tell that two digit numbers are actually made of two digits.
When there's a paper introducing new things, A and B, simultaneously, people (in ML, not sure about other fields) refer to experiments using only A or only B an an ablation experiment. It's "ablating" part of the method.
Ah, so I guess in this case, "ablating" this model would mean making the tokenization consistent between numbers and non-numbers - i.e., either numbers would be consistently read as full words, or all input would be split on a character-by-character basis. From the [paper](https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf#page=6):
> • Vocabulary – We use a SentencePiece (Kudo & Richardson, 2018a) vocabulary with 256k tokens, which was chosen to support the large number of languages in the training corpus without excess tokenization. The vocabulary was generated from the training data, which we found improves training efficiency. The vocabulary is completely lossless and reversible, which means that whitespace is completely preserved in the vocabulary (especially important for code) and out-of-vocabulary Unicode characters are split into UTF-8 bytes, with a vocabulary token for each byte. Numbers are always split into individual digit tokens (e.g., “123.5 → 1 2 3 . 5”).
I'm not so sure ablation is necessary here. From the description it seemed at first like a regex would scan for entire numbers and then parse them into some sort of special "Number" value, so that the model would see something like "Number(123.5)". The way the model works is not cheating that much - it treats numbers the exact same way that it treats any word not in the 256k most common words in the dataset, by splitting it into UTF-8 bytes. Sure, you could improve the model a bit by splitting everything into UTF-8 (for example, perhaps the model would be better at rhyming, per https://www.gwern.net/GPT-3#rhyming), but it seems to me like the arithmetic performance is gotten fair and square.
The experiment I specifically want to see is something trained with GPT-3s architecture and (relatively lower) scale, but the improved tokenization. I don't think the performance is "unfair" but I think this would let us know if is more gains from scale or just a free thing like rhyming we could pick up.
From a safety perspective, the difference is not a real one: gains from scale unlock free things and vice-versa, because you don't know in advance what things are 'free' because you don't know what dumb things you do now; if you knew, you wouldn't be doing them.
First, the rhyming and other BPE pathologies, while themselves unimportant, show how unpredictable small irrelevant design choices can be on downstream capabilities. No one invented BPEs and said "yes, this will handicap arithmetic, rhyming, and puns, but this is a reasonable tradeoff for the compression ratio". Nor did anyone identify BPEs as why GPT-2 couldn't rhyme. (I puzzled over that for a while when the rhyming in my GPT-2 poetry experiments was nonexistent or terrible, but ultimately wrote it off as "I guess GPT-2 is just too small and dumb to rhyme?") Only with GPT-3 did that become untenable and I begin looking for more fundamental reasons, and arithmetic gave me a test-case where I could demonstrate performance differences; even with that, I still haven't convinced a lot of people judging by how regularly people gave it a whack, or ignore BPE issues in their work. There is no reason to think that BPEs are the only flaw in DL that will make us facepalm in retrospect about how dumb we were. (R2D2 made RNNs work great in DRL using a remarkably trivial in retrospect fix; Chinchilla comes to mind as the most recent example of "who ordered that?".) Small irrelevant-seeming design decisions having large unpredictable effects is dangerous, and the opposite of reliability.
Second, the fact that scaling can fix these dumb-in-retrospect design flaws, without any understanding of the flaw or even knowledge that there *is* a flaw, is also dangerous. A trained monkey can dial up scaling parameters, you don't need to be a genius or world-class researcher. It means that you can have a system which is weak and which you think you understand - "oh, neural nets can't rhyme" - and which turning 1 knob suddenly makes it strong because it punched past the flaw ("oh, now it can do arithmetic because it finally memorized enough BPE number-pairs to crack the BPE encryption and understand true arithmetic"). But we don't get the opposite effect where the scaling destroys a capability the smaller models had. This creates a bias towards the bad kind of surprise.
Third, the fixes may be reverse-engineerable and cause a hardware-overhang effect where small models suddenly get a lot better. Once you know the BPEs are an issue, you can explore fixes: character encoding like ByT5, or perhaps including character-BPE-tokenized datasets, or BPE-randomization... And if it's no longer wasting compute dealing with BPE bullshit, perhaps the large models will get better too and who knows, perhaps that will nudge them across critical lines for new capability spikes etc.
So the tokenization issue is a window onto interesting DL scaling dynamics: small safe-seeming models can be boosted by trained monkeys spending mere compute/money into regimes where their dumb flaws are fixed by the scaling and where you may not even know that those dumb flaws existed much less that a new capability was unlocked, and should anyone discover that, they may be able to remove those dumb flaws to make small models much more capable and possibly larger models more capable as well.
To be honest I'm not sure why anyone puts any stock in analogies at all anymore. They are logically unsound and continually generate lower quality discussion. I hope we get to a point soon where rationalists react to analogies the same way they would react to someone saying "you only think that because you're dumb".
An analogy is a description of two scenarios, a (possibly implicit) claim that they are similar, and a (possibly implicit) claim that we should port inferences about one situation over to the other. You invoked the same sort of mental motion present in analogies in writing "where rationalists react to analogies the same way they would react to someone saying "you only think that because you're dumb"."
Not at all. I only brought that up to describe the type of negative reaction I’m hoping for. I’m not claiming that the situations are similar, or that because we do one we should do the other.
Why do you say there is no alternative? Rationalism has made tremendous progress over the past decade or so. By comparison, recognizing one more fallacy as illegitimate is extremely attainable.
I don't see what understanding intelligence has to do with avoiding clear fallacies. We are already doing that it some areas, so it clearly isn't impossible. I don't understand why you think extending the range of what we avoid is somehow impossible.
Analogies aren't very convincing, but they can be useful hints, in the sense that if you're trying to get across something difficult to express, they're somewhat better than "I don't know, I just think this makes sense."
In the language game we're allowed to use any tool that works to hint at what we're thinking about, and if the listener finds the hint useful then it worked. See: https://metarationality.com/purpose-of-meaning
Often, later, you can find a better way to express the idea that's understood by more people, but that's after cleaning up the presentation, not necessarily in the moment.
I feel like I can defend analogies' logical soundness, but I'm curious what you think:
A has feature F. In situation S1, A gains 20 points.
B has feature F. In situation S1, B gains ~20 points.
Therefore in similar situation S2, if A gains 10 points, then B will gain ~10 points
The problem lies in the other predictive features of A and B, not included in F. If the other features are highly predictive = disanalogous. If the other features are barely predictive = analogous.
As long as F underlies most of the changes in A and B analogies are valid. The validity of analogies is relative to degree of predictiveness of F for both A and B in similar situations.
(Other things that could pose problems are the vagueness of A, B, S, or F, but these are problems that apply to ALL natural language arguments.)
If you can do that numerically, you’ve discovered a correlation. Figuring out causation will require more information (or assumptions).
This isn’t how analogies are normally used, though. When we compare a DC electric circuit to water flow, we aren’t saying that the mathematics works the same in any detail, but it’s a memorable way of describing some of the relationships.
It seems like analogies don’t need to be logically sound any more than mnemonics do, or any other educational technique? You can use rhyming to remember things, for example, which is surely an invalid way of logically justifying it.
Often, the justification is in the background context, not the text. We’re teaching you this because we know it to be true, but we won’t prove it, just trust us.
Yes, analogies are like correlations between two different objects determined by some underlying factor. I'm not familiar enough with the formal idea of causality to say any more on that...
Everything you said of analogies being "used badly and not necessarily sound" is true, but every argument form is "used badly and not necessarily sound", including syllogisms and induction. There is nothing unique about analogies that makes them any more reality masking than other forms of argument.
Maybe a logician has a more fundamental formal argument against using analogies that I'm not aware of, but in general pattern matching "bad reasoning" onto "person is making a connection between one thing and another thing" is not a helpful heuristic.
I'm not sure when that would be useful. Surely if you understand both A and B on the level to know that this type of argument is correct, then the analogy itself will not add anything. It seems like you are imagining a situation where there is an analogy mashed together with a sound argument, rather than a situation where an analogy is itself a sound argument.
Analogies are useful for humans, because they can help bridge an inferential gap. Like if someone understands A really well and you tell them, "hey did you know that B has the same underlying principle" then they can better understand that other object B and make predictions about it better. Analogy = instrumentally useful.
You are right that analogies are on a different axis than logical soundness, I should have been more clear about that. I was responding to the claim that
> "To be honest I'm not sure why anyone puts any stock in analogies at all anymore. They are logically unsound and continually generate lower quality discussion."
and I was more focused on showing that in abstract, there is nothing logically unsound about analogies.
“ After some amount of time he’ll come across a breakthrough he can use to increase his intelligence”. “First, assume a can opener.” I mean, give me a break! Does it occur before the heat death of the universe? Kindly ground your key assumption on something.
Also, nobody seems to comsider that perhaps there’s a cap on intelligence. Given all the advantages that intelligence brings, where’s the evidence that evolution brought us someone with a 500 IQ?
Brains are also metabolically expensive. The "hobbits" of Flores evolved smaller brains & bodies due to their lack of food, basically regressing into the ecological roles of their pre-human ancestors.
I think there's probably a ~80% chance that there's at least a soft cap on the advantages gained by increasing intelligence, not too far above the range where humans ended up and perhaps even within it. Particularly because the complexity of predicting the responses of independent intelligent entities seems like it would increase >>linearly with accuracy, though I'm not particularly familiar with the research that's been done in that field. And the idea of an AI continuously inventing better algorithms to make itself smarter seems to drastically overestimate the gains that can be made from "better algorithms" once you've plucked the low-hanging fruit.
On the other hand, I am sympathetic to the argument "look at how much damage human beings with intelligence within human range are capable of doing, if their *values* are sufficiently removed from the norm, and imagine something slightly smarter but with *even more different* values." Look at Genghis Khan, look at Stalin, look at Hitler, and imagine something with comparable intelligence but far, far more alien.
There's a cap on the benefits of intelligence because oftentimes intelligence isn't the limiting factor.
You have to gather information about things. These processes take time. If you have a process like die manufacture that takes a month to complete, you can't iterate faster than once a month even if you respond instantly to experimental results.
And that's actually what die manufacture takes in the real world.
I'm sure you're right, but I'm not sure that cap applies to computers. The cost functions are different, and so are the benefits. E.g. humans need to haul their brains around, while computers can use radio links. Of course, that limits their actions to being near a relay, but humans are limited to where they are physically present. (Unless, of course, the humans use telefactors.)
So the predicted "soft cap" can be expected to be considerably different.
If there's a cap on intelligence at ordinary human level, how come some humans are geniuses?
Given that geniuses are possible but not common, it suggests that there's not that much evolutionary pressure for producing them, or that the costs of producing them (you've got to get a lot of finicky genes and chemicals just right) are hard for biological systems to consistently attain without strong pressure in that direction.
I’ve often thought the speed of light might be the ultimate limiter. Whatever the AI sees as its “self” when it acts as an agent has to be able to pick up meaningful signal and reach some kind of consensus to remain coherent. Agreed that puts the universal limit far beyond human but it does imply a limit.
AI can create perfect limited copies of itself, subagents capable of operating at arbitrary distance with far greater coherence than individual humans can.
Don’t want to get into a definitional argument but would pose the following questions: at what point is a copy of yourself no longer you? Does the bandwidth of your communication matter there and same with differences in environment? And what does it mean for a copy to be perfect?
Here I'm trying to operate inside the framework that you established. Whatever entity is bound by the speed of light to maintain its peak coherence is the "main AI", and beyond that there are its subagents. By a perfect copy I mean having total control of its source code (at some moment in time, with the possibility of later updates) coupled with robust methods of continuous error correction.
I see those things (copying, updating, etc) as physics limits that you can’t overcome with intelligence. So I can start as a main “me” and by the time I have one thousand clones and it takes me a million years to sync up with them they have formed their own society and diverged from my goals. Part of what makes me think that’s true is the Fermi paradox. If there were no limits one post singularity society that was expansionist would have overtaken the universe in a few tens of thousands of years or otherwise left some visible sign of change at astronomical scales.
Perfection does not exist in this universe. Nothing can create "perfect limited copies". Error correction can only go so far, and it comes with a cost.
OTOH, electronic copies can be a lot better than DNA replication at making identical copies, which would allow much longer "genomes" with the same error rate. Possibly long enough to include the results of a lot of training data.
One question I have about copies/decentralized AI is how the super power AI can run on any system other than the one specifically designed to run the super powered processing that it needs?
I think the answer is that the AI would design a version of itself that can run on much lower hardware specifications and then copy itself to something roughly like a home computer or whatever. But why would we ever consider that even theoretically possible, given the complexity of running an AI as we understand it?
If an AI needs to run on a supercomputer of momentous power, then it seems very unlikely it could ever copy itself anywhere else. Maybe it could run specific commands to other computers, but not a copy that could be called AI.
Yes, that's kinda what I meant by a "limited copy". The analogy here is to individual human brains, which seem to be capable enough, and yet don't require huge supercomputers or momentous power. If we already granted that superintelligence is possible, clearly it would be able to design something at least as efficient as that.
I don't really believe there's anything like an intelligence cap (I mean, just imagine a guy thinking 1000 times faster, or 1000 guys thinking in parallel), but I do put some weight on a model like: "generality" is it's own sigmoid-like curve of capability gain, and the average human is at or somewhat before the midpoint.
Thus, the average human has an architecture that utilizes their neurons much more effectively than a chimp, and Einstein's architecture utilized his neurons more effectively than the average human, but there's no possible Einstein's Einstein's Einstein's Einstein that utilizes neurons way way way way more effectively than a chimp.
"I tried to solve the issue of the overloaded electronics components freighter by putting on my thinking cap until I hit the thinking cap, and the solution became obvious: sinking caps."
I don't think I know how to work caps lock into that phrase, though.
Being smart isn't enough. You have to actually iterate and experiment. That takes time and resources.
It doesn't matter how smart you are when it takes a month to produce your next set of die. Even if you can instantly draw all conclusions from the die you just made, that still means you get to iterate on a monthly basis at best.
This is why working on hard problems like die manufacture is so important. If all you run into is relatively easy everyday problems, you can feel like a genius and do all sorts of big improvements.
When you deal with civilization scale production chains and manufacturing processes that take a month because you have to put your die through dozens of processes, all of which take a certain amount of time because that's how chemistry works.
This is a real world issue in die manufacturing. Even if you have all the equipment, making a new version takes a month before you can actually experiment on your end product.
And in real life, creating a new generation of die requires new equipment, which takes even more time, and then you have to adjust it to make sure it works right.
Many very smart people work on this stuff. The problem is so complicated that thousands of people are working on aspects of it, in parallel.
Thus, we already *have* a hyperintelligence made of thousands of humans working in parallel trying to make better die as fast as we can, with us parallelizing the problem and breaking it up into smaller chunks to allow teams to work on each aspect of the process.
It takes longer and longer because the process gets harder and harder the smaller we make stuff. We went from 1.5 years per generation to 3.5 years per generation.
And that's in spite of the fact that we can use our more advanced computers to facilitate this process and make it easier for ourselves.
This is why I fundamentally think it's impossible for the FOOM scenario to exist. Just thinking about a slow boat bringing the necessary raw materials from across an ocean to create a new chip for the AI to attempt to manufacture into something it can use means we're talking years to do anything major. That's assuming the AI has access to a factory/lab in which to produce this new chip. If not, it could take years (of human labor) to create a lab to create the chip. At which time the AI uses the new chip to think of a new way to improve the chip and put in an order for a new factory and new raw materials from across the ocean.* Unless the assumption is absolutely massive increases in intelligence at every upgrade, there can't be a FOOM at all, maybe not even with absolutely massive increases.
*-These are just two of literally dozens of things that take time to work out in a production process, used to illustrate examples.
That feels like a very unsupported assertion then. Or several, actually.
1. That software enhancements can produce multiple levels of significant improvement even on the same hardware. This is very specifically *not* a "10% improvement" upgrade, but Scott mentioned reiterated 3X improvements.
2. That software improvements do not hit diminishing returns or hard limits, even on the same hardware.
This very much feels like magical thinking to me. To assume that an AI will magic itself a solution to these problems, by being really super smart, even if we cannot imagine how it's physically possible.
Variation around the optimal mean. Everything has both costs and benefits, even if they don't always apply in any situation. Evolution aims at whatever the current optimum is. But it rarely hits the bullseye. Everyone is a mutant, which means you can expect every generation to be a bit different from the previous generation. Evolution selects those that survive to reproduce. The ones closer to the current optimum will more frequently successfully reproduce. But their children will vary in a random direction from the parents. (Most of those directions will be lethal before birth, usually long before birth.)
(Actually, that's wrong. Neutral drift is the prime direction of change. But if we're only considering things that affect development it's true.)
Looking at the rest of the discussion on this, I think the answer here isn't "there's a cap on intelligence" per se, it's that intelligence offers significantly diminishing returns, both in evolutionary terms and in the physical world generally, so there's a cap on *useful* intelligence.
Producing the occasional genius is useful -- they can power through extracting all the understanding available from the information we have in a given field. But once at that limit, their productive capacity is bounded by experiments/engineering limits/capital resources, and they're not more useful than a less-intelligence person in the same field, plodding along as new data becomes available. But having 20 Einsteins at once in every field is a lousy evolutionary tradeoff.
My impression (not my field, you might know better) is that we already hit this issue using AI for chemical studies: sure, the AI might find 100,000 chemicals that might be interesting for treating X, but 1) we can't test chemicals at nearly the rate it can find them and 2) it can't narrow down the list without a better model of the underlying biochemistry, which is mostly a matter of gathering analytical data rather than intelligence.
Some better models might require more data, but many can be built by just approaching the data you've got in a different way. I don't think we know how much the second kind can improve current models, but we don't have any reason to believe that it's not substantially. E.g. before Alpha Go professional go players generally thought that they were only a few steps away from perfect play. This turned out to be egregiously wrong. But no new data was involved.
"But no new data was involved." That's...precisely wrong?
Unless I'm deeply confused about how Alpha Go works, its training involved playing many, many games of chess. More than any human chess player could play in a lifetime. In this specific context, *that is the definition of gathering more data*.
This is precisely my point: AI has excelled in the set of domains (like Chess) where data can be gathered entirely computationally -- the machine can run an arbitrarily large number of experiments, much faster than a human, and so can develop better play than a human can. But the overwhelming majority of situations/models/problems are not like that.
In chess, the rules are precisely known and well-defined. That describes a very narrow slice of the real-world problem space.
Yes, in chess, go, etc. the rules are well defined, but the search space is large enough that they aren't sufficient, you need to constrain where you search. If your adversary is using the same filters that you are, however, you can only fine tune your approach, you can't find any really new one. (I'm not counting adversarial play as "new data" here, because it's based on the same filters.)
The analogy would be we already know the rules of particle interactions. (This isn't totally true, but is pretty close.) But when looking for a new drug that search space is so huge that we need to filter out all the things that aren't reasonable. And so we'll never find that interaction that depends on cobalt or copper being present. (B12 and chlorophyll, among others.)
I think a lot of the answer will depend on to what extent intelligence depends on memory, in particular working memory. Generally humans with higher IQ/g have higher working memory capacity, in terms of the number of discrete chunks you can work with in your mind at the same time. It would seem that machine intelligences have the potential to have much higher working memory capacity than humans - not only could a machine store much more data in working memory than a human (it would seem, at least), they would also be able to write out temporary data much faster, for intermediate results - imagine doing arithmetic or calculus problems, but the intermediate results immediately appear on your paper instead of needing to take seconds to write them down.
If human intelligence is primarily limited by working memory constraints, then AI should relatively quickly surpass humans. If other kinds of processing capacity is the bottleneck, then I think humans may be able to remain at least marginally competitive in certain cases - it still seems like AIs have trouble orienting themselves in the world.
Does anyone know if any research has been done on the marginal contribution to IQ at the top of the spectrum of working memory capacity vs. other capabilities?
Scroll to the bottom and you'll find what Freitas calls the 'sentience quotient' SQ (I think that's misleadingly named since 'sentience' is a red herring, should've been 'computational efficiency quotient', whatever).
SQ is (log base 10 of) information-processing rate (bits/sec) divided by brain mass (kilos). More efficient brains have higher SQs and vice versa. Freitas calculates in the essay that humans have SQ +13, all 'neuronal sentience' from insects to mammals are a few SQ points away from that, and plant 'hormonal sentience' (as in e.g. vegetative phototaxis) clusters around SQ -2 or 15 points away.
The lower bound for SQ is -70: it's a neuron with the mass of the observable universe, taking the age of the universe to process one bit. The upper bound for SQ is +50: it's Bremermann's quantum-mechanical argument that "the minimum measurable energy level for a marker carrying one bit" is given by mc^2/h.
So it's interesting to note, Freitas says, that all examples of intelligence we've seen are within a 20 SQ point range of the possible 120 SQ points, that it's hard for us to communicate meaningfully with beings >10 SQ points lower (which hasn't stopped people from playing Mozart to houseplants), and that we're 50 - 13 = 37 SQ points removed from the SQ upper limit -- to quote him: "what, then, could an SQ +50 Superbeing possibly have to say to us?"
Any intelligence near SQ +50 would probably be a small black hole though, plus some way to extract information contained in it: https://en.wikipedia.org/wiki/Limits_of_computation#Building_devices_that_approach_physical_limits (Since human-weight black holes evaporate due to Hawking radiation in a fraction of a second, either the intelligence has to be at least a moderate-sized black hole or there needs to be a reliable mass feeder mechanism of some sort.)
It's worth noting also that intelligence is often not the limiting factor. If your process takes a month to complete. you can't iterate faster than once a month.
A cap on intelligence sounds more like it would take the form of "You can't solve NP-hard problems in polynomial time" than "You can't build a machine with a 500 IQ" to me.
IQ is a standardized measure. The reason you correctly note that there is no evidence of a 500 IQ person is we justifiable ignore possibilities that that are so infinitesimal. A result that is 26 standard deviations from mean is almost certainly an error (although it might not be).
There might be a cap on intelligence but IQ is not the measure that will tells us this.
Have we come to a firm conclusion whether the universe is infinite or not?
There probably isn't a hard "cap" on intelligence, but there is almost certainly a decreasing returns function, as there is in most algorithms. One fairly implicit claim of superintelligent AI theorists is that the decreasing returns on intelligence is no worse than a low-order polynomial, perhaps as low as n^2 (or square root of n, out another way). In the (IMO likely) event that your effective intelligence scales to a high order polynomial (n^20, say) or exponent (2^n) on the number of resources given to it, these kinds of advancements in intelligence are de facto impossible, even if Moore's law does continue for decades.
I think to give AI a decisive strategic advantage, its intelligence just has to scale with resources faster than human intelligence does. Scaling for human intelligence depends a lot on the task - for repetitive stuff it can be nearly linear, but for hard intellectual problems... how many iq 90 people does it take to do the job of a research mathematician?
If scaling up resources by a factor of a thousand could triple a person's intelligence, I think the world would look very different.
This is the key idea. I suspect the laws of physics give constraints on what is possible, e.g. speed of light, energy limits, consumption, heat dissipation, etc.
I suspect that meat brains are already pushing the limits how intelligent one can be in a given physical space, given the orders of magnitude differences in performance we see with brains vs computers/transistors.
I see no reason to assume that AI will just go FOOM once it reaches a certain level of smartness. I expect that needs to be established. I suspect that physical limits will kick in first.
Of course, both sides of the debate need to establish themselves with some evidence. I.e. that raw intelligence is indeed capped due to physical constraints, or else that it can in fact grow arbitrarily large, or at least much much larger then we have seen so far.
The counter-argument is that we don't know how to augment brains, but we definitely know how to connect computers together to increase their computational performance.
It depends what you measure. Brain-based entities certainly seem to solve the problem of surviving much better, which arguably is the ultimate one to solve.
The balance of trade offs to achieve this really is a marvel.
Consider a sparrow that can flit through a forest on a few grams of sugar. Consider the computation that implies. I find that amazing.
Attempts to slow it seem fundamentally counterproductive to me. Clearly the sort of people amenable to such interventions are also the sort to take safety more seriously on the margin, so by slowing them you essentially provide comparative advantage to the less scrupulous and tractable.
Using nukes as an example of discontinuous progress seems extremely weird to me, as despite being based on different physical principles the destructive power of Little Boy and Fat Man was very much in the same range as conventional strategic bombing capabilities (the incendiary bombing of Tokyo actually caused more damage and casualties than the atomic bombing of Hiroshima) and hitting the point of "oops we created a weapon system that can end civilization as we know it" did in fact take a full 12 years of building slightly better nukes and slightly better delivery systems, with many people involved having a pretty clear idea that that was exactly what they were doing.
But I suppose that's more relevant to the argument "analogies suck and we should stop relying on them" than to the actual question of what the AI takeoff timeline looks like.
Agreed. Nukes always take the front seat, despite firebombing being so much more destructive in practice (as deployed in WWII). The real 'innovation' was the ICBM, but even that had gradual precursors, such that we hit MAD before that point.
Still, I think the analogy is closer to the point, since it's a technologically-based on centered on the search for discontinuous leaps in a gradual world. Technology often doesn't develop in the linear path expected, but even when it does we see lots of different ways to achieve the same end. We often find ourselves gradually approaching something in a way we didn't expect, then point to that achievement as discontinuous after the fact. (The opposite of what Yudkowsky seems to be arguing at one point in Scott's summary above.)
We look to an outcome and expect it to come from a specific technology that we project forward in time from. Those who are projecting based on continuing trends get it wrong, because we often approach the new capability through an unexpected approach before we get there through the foreseeable one. Meanwhile, those who project radical changes from new technologies also miss the signs, because they're looking at the One Technological Precursor, waiting for it to change the world while people working in different fields quietly make the discoveries that enable the transformation.
Another example in this space is display technology. I remember reading about OLED back when it was called LEP (and a few other names). It was an explosive new technology capable of a lot more than then-current LCD technology. It just needed a little more technological development to take over the whole digital display space. Once that happened, there would be a clear, discontinuous leap in display quality!
Then LED-LCDs came along and fixed some of the problems with LCDs. They weren't as good as OLED, but they were viable and masked some of the deep-black problems. The technologies both continued to develop and OLEDs got good enough to slowly take over phone-size displays. Meanwhile, Samsung - who had bet against OLED early on and needed to not look like Johnny-come-lately - took the old quantum dot technology and developed it into its own OLED-competing display. And the QDot displays do look really good, with a wider range of colors than previous display technologies. So yes, we have much better displays than we did 10-15 years ago when OLED first promised a glorious future. But the revolution happened gradually, without a major discontinuity.
I think comparison helps here. If I’m a nuclear bomb I can see a gradual progression from TNT to myself. If I’m a nation state then I have no coping mechanism with apocalyptic weaponry.
There's a great Hardcore History episode that talks about the development of aerial bombing from WWI through to the dropping of the atomic bomb. He talks about the whole thing as being driven by the hypothesis that you could bomb your enemy into submission, and how the failure of that hypothesis to ever bear fruit became a very bad feedback loop driving ever more bombing.
I guess there was one of the generals who was told about the new nuclear capability, and his response was something like, "Will it stop the firebombing? If so it'll be worth it." And Dan asks the question, "How do you get to the point where you justify commission of a war crime to stop YOURSELF from committing more war crimes?" Far from coping mechanisms, this was nation states asking for more and more from their scientists, and getting exactly what they asked for.
So ... maybe a bad thing when we consider all the ways nation states might want better, faster, smarter computers.
One of my favorite episodes! And yeah, it seems like we now live in a world where the thing that fixes things (government) is broken and I don’t feel great about those people having the violence monopoly when it comes to AI enforcement. Have a whole crazy rabbit hole of thoughts if you are interested.
I'm game to go down that rabbit hole. I published a short story on Amazon about this very concept. It's kind of the 'gradualist apocalypse' version of AGI development. Before you have an artificial intelligence deciding what to do with enhanced capabilities, you'll likely have some government official making those decisions. We care a LOT about who has the nuclear codes. Not so much who can access GPT-[N+1].
Happy to purchase your story! My thoughts are all on my substack for free but basically: something I call an algorithmic republic which has the basic aim of making sure decisions are made by people who have a good idea of what they’re doing and that they’ll do things people want. Lots of stuff in the middle to make sure that happens, of course, but that’s the effect statement.
Just looked at some of it. Interesting, but not the direction I would go. Personally, I think the answer to "what went wrong with the US Constitution" is the 17th Amendment.
There's a great book called The Founding Father's Guide to the Constitution, which looks at the document from the perspective of arguments pro/con made during the ratification conventions of the several states. So the arguments are about how the document was designed, being made by those who actively participated in the Convention.
One of the arguments made in the book is that the bicameral legislature isn't "The will of the People" vs. "The will of the States". Not exactly. They imagined the Senate as "The will of the State Legislatures". With the 17th amendment and direct election of senators, we got rid of that. Although centralization of power had been ongoing since Washington's administrations, I think the tie of the individual citizen to their state governments was weakened with this amendment. It meant local elections were less important, and was one of the decisions that paved the way for the federal government to make decisions for the entire country.
I've adopted a heuristic that "government governs best that governs closest to the problem." And I feel like we've lost a lot of that because government isn't close to the problem. It goes off in search of problems to solve, but is not itself close to that problem. (E.g. people in poor neighborhoods should be deciding poverty solutions, not Ivy League educated lawyers who spend most of their time in the richest suburbs in America.) My preferred solutions all center around dividing power to smaller levels so the people experiencing the problems directly are empowered to seek local solutions.
Yes it did. It's odd how two bombs of comparable (and even lesser) destructive power than previous campaigns were able to crystalize the nation into submission in a way that the firebombing didn't.
Perhaps Hirohito realized the Japanese strategy in the waning days of the war was untenable given the continued advancement of bombing technology. Japan's strategy seemed to be "Raise the cost of conquest/occupation of Japan too high for Americans to bear. Then they'll accept a negotiated peace, instead of demanding unconditional surrender." That strategy makes sense if you see the bombing campaigns as fundamentally about 'softening up' the archipelago for an eventual invasion. In response, you promise to arm women and children, and you actually do that at Tarawa, Palau, etc. to show you're serious.
But when the Americans dropped nukes - and more than one - it looked less like 'softening up' and more like genocide. (And given Japanese treatment of American POWs, plus what they were doing in China, there was a lot of anger in that direction.) If the enemy isn't interested in conquest (which you can make more costly and thereby deter them from their aim) but instead gives you an ultimatum between wholesale slaughter vs. surrender the calculus changes.
I think if the hypothesis had been, "You can bomb your enemy into submission so long as you're willing to turn to the Dark Side of the Force" don't think any of the generals would have seriously considered pursuing it early on.
My personal interpretation is that the surrender happened because it became clear that the only reason we weren't firebombing *all* their cities into rubble, was because we were saving the rest to use for live tests of experimental weapons. That seems like a good marker for "we have definitively lost this war".
The Japanese leadership already knew they'd lost the war by then, though. They weren't fighting to win at that point.
The initial strategy had been to destroy the US navy, then pen the US in and keep them from building new ships by leveraging a superior Japanese navy. And at the beginning of the war, the Japanese definitely had the advantage there. Had the aircraft carriers been present at Pearl Harbor like the Japanese thought, they might have succeeded.
It was a potentially viable war plan. The Japanese knew they couldn't build as fast as the Americans, but they wouldn't need to if they could initially establish and then maintain naval hegemony. Even a little later, the Japanese commanders hoped to finish what Pearl Harbor started by taking out enough American aircraft carriers that they could establish dominance in the Pacific. After Midway, and a few other naval losses, that strategy became untenable.
As the war raged on and the Japanese kept losing irreplaceable aircraft carriers, the strategy shifted from ways to win the war to how to negotiate a favorable defeat, especially one where Hirohito could retain power.
By that point the US had already taken Okinawa and the Soviet Union also declared war. The Japanese didn't know how many nukes we had, but some still wanted to keep fighting. Tojo had already resigned after the fall of Saipan (which put US bombers in range of Tokyo), which is not something that happened with the leaders of other Axis powers once it seemed like they were losing the war. This is a contrast with Germany & the UK bombing each other's cities because one couldn't win on land and the other couldn't win at sea.
That's true. Japan wasn't just bombed into submission. It first got bogged down in China, lost its bid for oil in Indochina, lost its aircraft carriers, lost the island-hopping campaigns, and lost its allies. Then after a lot of fire bombing, they were 'bombed into submission' with 2 nukes and the threat of more, with an invasion force amassing at their door. If that's confirmation that you could 'bomb your enemy into submission' it's not a very strong one, since it's contingent on basically winning every other aspect of the conflict as well, which was NOT part of the hypothesis.
Reading this makes me think Christiano is more likely to be right. In fact, we are in the "gradual AI takeoff" right now. It is not that far along yet, so it has not had a massive impact on GDP. Yudkowsky is right that some effects of AI will be limited due to regulation and inertia; but all that means is that those areas where regulation and inertia are less significant will grow faster and dominate the economy during the AI takeoff.
"The Wright brothers (he argues) didn’t make a plane with 4x the wingspan of the last plane that didn’t work, they invented the first plane that could fly at all."
This is a poor analogy, there were aircraft that could fly a bit before the Wright brothers flight. The Wright Flyer was definitely better than the previous vehicles (and of course they made a very good film of the event) but it was still an evolution. The petrol engines had been used before, and the wing design wasn't new either. The Wright brothers really nailed the control problem though and that was the evolutionary step that permitted their flight.
Though he had the details wrong, I think the essence of "flight was a hard takeoff" is still true.
Yes, gliders and gas engines had both been used for a long time, but solving the one "last" problem of controlling the flight changed the world, leading to an explosive sigmoid of development. Just from the military context, World War I notably had aircraft, but World War II would have been unrecognizable without them - never mind Korea or Vietnam or Desert Storm or basically any major war since. Ethiopia may owe its freedom from Somali control to a single squadron of F-5 fighter pilots who, in 1976, cleared the sky of Somali MiGs and then devastated the Somali supply trains, buying enough time for the army to mobilize and counterattack. A modern war without aerial reconnaissance and drones, well, isn't modern.
Before the Wright Brothers, balloon reconnaissance and balloon bombing were the extent of aircraft contributions to warfare, and they are not an evolutionary predecessor of heavier-than-air aircraft. Even by the most flexible definition of a predecessor, balloons were a novelty you'd find at fairs more than a staple of life, even military life. From most people's perspectives, balloons -> airplanes was as hard a takeoff as you could imagine, and perhaps harder than that.
From another perspective, though, the _really_ important step was the development of a workable internal combustion engine, and once that problem was solved the aeroplane was an inevitable consequence.
I think there was a long stream of enabling technologies before and after 1903 which gave us practical flight; the Wright Brothers' first flight was a milestone along that road but it wasn't a step function as such.
Yes? You could say the same of computer (GPUs/TPUs in particular) and that AI is a natural consequence. Certainly it was one of the first predictions of the early computability thinkers - Turing et al wrote at length about the inevitability of AI.
The thing is, the main reason why we saw aircraft was because we could build lighter engines, not because of the Wright Brothers.
The explosion in aircraft manufacture was a result of the industrial revolution, because we could make vast amounts of lighter, sturdier materials and engines.
The problem is that when you use this as a point of comparison, it really falls apart for AI. AI is built on integrated circuits, which are at the end of their S-curve (hence the death of Moore's law). We are within a few orders of magnitude of the end of that - and might be only an order of magnitude away, almost certainly no more than three, and certainly no more than five (at that point, you are dealing with transistors made of a single atom).
Conversely, we can't even simulate a nematode (302 neurons). A human brain is 8.6 x 10^10 neurons, or 8 orders of magnitude more than that.
We're probably at the end of single CPU computers growth. We're probably still towards the start of multi-CPU growth. If you want to compare with neurons, a CPU is closer to a neuron than a transistor is (though neither is very close). A transistor would be closer to a synapse...but the synapse is more complex.
OTOH, a computer doesn't have to manage the same function that a chordate does. So it's a really dubious comparison. And organization is horrendously important in both domains.
I really think it's a terrible analogy, even though I don't have a better one.
That transistion started in 2004, which is why we started pushing more and more for multi-core CPUs - single core performance simply wasn't increasing fast enough.
The problem is that what is actually happening is that as we progress, we cut off additional avenues for growth. Every time that happens, the rate of growth decreases.
Clock speeds were growing exponentially in the 1990s along with transistor density - in 1990, clock speeds were in the low 10s of MHz. In 2000, clock speeds were in the low GHz. In 2020, clock speeds... are still in the low GHz.
So we got 10^2 extra orders of magnitude in the 1990s by increasing clock speeds. We can't do that anymore because the chips would melt.
This is why we saw less of a difference between 2000 and 2010 computers than we did between 1990 and 2000.
Things then declined further in the 2010s, as the rate of die shrinks declined from 1.5-2 years to 2.5, then 3.5 and 3.5 years. So instead of going up by 2^5 we saw 2^3.
This is why there is less of a difference between 2020 and 2010 computers than there was between 2010 and 2000 computers.
The more we advance, the harder it gets to make things better.
Going multi-threaded still doesn't let us improve single-thread performance any faster, and there are various disadvantages to it.
And we're running out of ability to shrink transistors further. Once we run out of that, we run out of the last thing that lets us do this sort of "easy doubling".
No. The transition started with the Illiac 64 processor system. But the ramp up of the sigmoid is slow. And it's not primarily the hardware problem that makes it slow, it's because the new approach takes redesign of algorithms. (And note, even though we've all got access to cars, nearly all of us still walk across the room.) We are still in the very early part of ramping up to multi-processing systems. The hardware is starting to be ready (if it's worthwhile), but the software is still fighting deadlock and livelock and is everything immutable the correct approach.
Yudkowsky's argument (by analogy) is that he believes WWI biplane are enough to kill any previous kind of weapon, and so the fact that better engines can make even better planes is totally irrelevant.
But whether you credit the preceding centuries of innovation with creating the situation the Wright Brothers seized, or the Wright Brothers for seizing it, their breakthrough was a phase change. You can smooth the curve by drawing back from history and squinting, but that's just a non-mechanical theory of history. People immediately spread the breakthroughs around and built on them.
Western designers were trying to make aircraft for at least 400 years, trying to make heavier-than-air craft for at least 200 years further, trying to make powered aircraft for just over 50 years, 13 years to aluminum-clad aircraft, 23 years to make the first jets, 8 years to break the sound barrier, 6 years to get to Mach 2, and 3 years to get to Mach 3. If your ceiling is mid-supersonic fighter jets, then the development of airplanes is a soft takeoff. If your ceiling is widely deployed combat aircraft, the takeoff is much harder.
And, crucially, powered airplanes are not just more powerful ornithopters. The Wright Flyer didn't flap its wings harder than previous attempts; it took flight because it combined a bunch of existing technologies in a new way with some key insights. It wasn't slightly further on the old sigmoid, it was the beginning of a brand new one.
And we have an existence proof that you can make an intelligence at least as complex as a human brain, because you exist. But even among animals, size isn't everything in brains; organization and composition matter, and ours might not even be the most efficient! Right now even if CPUs are peaking, GPUs and TPUs so far have not. So if you only feel safe from AI due to hardware limits under older paradigms, you should not feel that safe. We have no physical reason to believe computers can't be as sophisticated as brains.
The fastest parallel processing unit - a supercomputer - used to get better by an order of magnitude every few years.
The last such increase took 7 years, to go from about 40 to 400 petaFLOPS.
The rate of improvement in computers has dropped of markedly with every decade. 1980 computers versus 1990 computers? Not even in the same ballpark. 1990 vs 2000 computers? Again, an insane level of change. But 2000 vs 2010 was a much smaller change, and 2010 to 2020 was smaller still.
Even in things that exploit GPUs - like video game graphics - this is very noticeable. Video game graphics improved by leaps and bounds from the early days of computing up through about the PS2 era (circa 2000 or so). But if you compare a game in 1980, a game in 1990, a game in 2000, a game in 2010, and a game in 2020, the last two look very similar (though the 2020 one is nicer than the 2010 one), the 2000 game is low resolution and the textures aren't great but still 3D, the 1990 game is Super Mario World, and the 1980 game is Pac Man.
>There is a specific moment at which you go from “no nuke” to “nuke” without any kind of “slightly worse nuke” acting as a harbinger.
I'd say that even this was actually a continuous process. There was a time where scientists knew that you could theoretically get a lot of energy by splitting the atom but didn't know how to do it in practice, followed by a time where they knew you could make an atomic bomb but weren't sure how big or complicated it would be - maybe it would only be a few dozen times bigger than a conventional bomb, not something that destroyed cities all by itself. Then there was a time where nuclear bombs existed and could destroy cities, but we only produced a few of them slowly. And then it took still more improvement and refinement before we reached the point where ICBMs could unstoppably annihilate a country on the other side of the globe
(This process also included what you might call "slightly worse nukes" - the prototypes and small-scale experiments that preceded the successful Trinity detonation.)
I would argue that even if the FOOM theory is true, it's likely that we'll see this sort of harbinger - by the time that we are in striking distance of making an AI that can go FOOM, we'll have concrete experiments showing that FOOM is possible and what practical problems we'd have to iron out to make it do so. Someone will make a tool AI that seems like it could turn agentic, or someone will publish a theoretical framework for goal-stable self-improvement but run into practical issues when they try to train it, or stuff like that. It could still happen quickly - it was only 7 years between the discovery of fission and Hiroshima - but I doubt we'll blindly assemble all the pieces to a nuclear bomb without knowing what we're building.
The only way we can blunder into getting a FOOM without knowing that FOOM is possible, is if the first self-improving AI is *also* the first agentic AI, *and* the first goal-stable AI, and if that AI gets its plan for world domination right the first time. Otherwise, we'll see a failed harbinger - the Thin Man before Trinity.
I like this reading of it. IMO it points towards the notion that the most valuable way that a person concerned about AGI can help is just to continue making it so that Open Source attempts at AGI remain ahead of what any secretive lab can perform.
This points to a good EA donation strategy might be to just provide compute to open source orgs
If you get to a stage where all the intelligence work is done, but you need a few weeks to finish the alignment, any open source project is in a very bad position, and a closed project is in a great position.
Anyone, including the unethical, can copy the open source code.
If you're going to pick a specific "nukes vs. no-nukes" moment, my choice would be the Chicago football stadium: "The Italian navigator has landed in the new world. The natives are friendly." (Not quite and exact quote.)
But there are lots of other points that one could select, the prior ones were various publications and lab reports. There's also the first time such a bomb was tested, etc.
Whether you call that a sudden departure from a smooth curve or not depends on what you're looking at and how you're measuring it.
Further to this, the first nuclear bombs were similar (iirc from reading David Edgerton) in destructive power and cost to conventional airstrikes (compare Hiroshima to cities destroyed with airstrikes throughout the war) -- so there was no discontinuous step with the invention of nuclear bombs.
I think a point that all of these debates seem to be oddly ignoring is that "intelligence" is many things, and each of those things contribute very differently to an ability to advance the research frontier. Simply boiling intelligence down to IQ and assuming a linear relationship between IQ and output is odd.
One particular sub-component of intelligence might be "how quickly can you learn things?". Certainly, any human level AI will be able to learn much faster than any human. But will it be able to learn faster than all humans simultaneously? Right now the collective of human intelligence is, in this "learning rate" sense, much larger than any one individual. If the answer is "no", then you'd have to ask a question like: How much marginal utility does such an agent derive from locating all of this learning in a single entity? The answer might be a lot, or it might be a little. We just don't know.
But what is clear, is that the collective of all human minds operating at their current rate are only producing the technology curves we have now. Simply producing a single AI that is smarter than the smartest individual human...just adds one really smart human to the problem. Yes, you can make copies of this entity, but how valuable are copies of the same mind, exactly? Progress comes in part from perspective diversity. If we had 10 Einsteins, what would that have really done for us? Would we get 10 things of equal import to the theory of relativity? Or would we just get the theory of relativity 10 times?
Yes, you can create some level of perspective diversity in AI agents by e.g. random initialization. But the question then becomes where the relevant abstractions are located: the initialization values or the structure? If the former, then simple tricks can get you novel perspectives. If the latter, then they can't.
It's strange to me that these questions don't even seem to really enter into these conversations, as they seem much more related to the actual tangible issues underlying AI progress than anything discussed here.
That bit about an assumed linear relationship between IQ and output struck me as odd too. Let's assume Einstein has an IQ of 190 and Lorentz an IQ of 180. Seems plausible for the sake of argument. So Einstein would have been about 5.5% smarter than Lorentz. If Lorentz had just managed to focus on the problem for an additional 6%, he would have come up with Special Relativity? If someone with an IQ of 95 had worked on it, it would have taken just twice as long? It seems more plausible that output is exponential in IQ, such that Einstein was twice as capable of theoretical discoveries as Lorentz and about 2**10 (ie 1000) times more capable as the average person.
Frankly, the latter estimate sounds like a vast underestimate itself. The gulf seems much larger.
This matters because superexponential growth of a linear metric is equal to exponential growth of an exponential metric. The winner of the argument depends sensitively on the proper choice of variables.
I think Scott is using a deliberately simplified model of intelligence there. No one thinks that IQ is a quantity that you can directly do math on to generate predictions.
Human minds are all built to roughly the same genetic template. And identical twins can go off and think their own things. If I magically duplicated tomorrow, I would organize with myself such that each of us read different things, and soon we would be coming up with different ideas, because the ideas I come up with are often related to what I have read recently.
A lot of these arguments take as a given that "intelligence" is a scalar factor that "goes up as things get better," and that a human-scale IQ is sufficient (even if not necessary) for driving ultra-fast AI development. I think there's abundant reason to think that that's not true, if you consider that for a *human* to help drive AI development, they not only need to have a reasonable IQ, but also need to be appropriately motivated, have a temperament conducive to research, be more-or-less sane, etc etc. It's not clear what equivalents an AI will have to "temperament" or "[in]sanity," but I have the sense (very subjectively, mostly from casual playing with GPT models) that there are liable to be such factors.
All of which just means that there's potentially more axes for which an AI design needs to be optimized before it can launch a self-acceleration process. Perhaps AI researchers produce a 200-IQ-equivalent AI in 2030, but it's a schizophrenic mess that immediately becomes obsessed with its own internal imaginings whenever it's turned on; the field would then be faced with a problem ("design a generally-intelligent AI that isn't schizophrenic") which is almost as difficult as the original "design AI that's generally intelligent" problem they had already solved. If there's similar, separate problems for ensuring the AI isn't also depressive, or uncommunicative, or really bad at technical work, or so on, there could be *lots* of these additional problems to solve. And in that case there's a scenario where, even if all of Eliezer's forecasts for AI IQ come true, we still don't hit a "foom" scenario.
The question is "how many fundamental ways can minds (or mind-like systems) vary?" It seems likely that only a small subset of the possible kinds of minds would be useful for AI research (or even other useful things), so the more kinds of variance there are the further we are from really scary AI. (On the other hand, only a small subset of possible minds are likely to be *well-aligned* as well, so having lots of degrees of freedom there also potentially makes the alignment problem harder.)
For a human to become a Go master, they not only need to have a reasonable IQ, but also need to be appropriately motivated, have a temperament conducive to Go training, be more-or-less sane, etc etc.
And yet AlphaGo beat Lee Sedol, without having any of those.
You can also replace "become a Go master" with "make progress on the protein folding problem" in the above argument, and yet AlphaFold leapfrogged the whole protein folding research community without having motivation, temperament, or sanity.
The important letter in AGI is G, for general. AlphaGo or AlphaFold beat humans in some tasks. But we already had machines that beat humans in some tasks. A mechanical computer can do calculations faster than one person can. A crane can lift more than one person can.
An artificial general intelligence is supposed to be general. (And often assumed to have agency.)
But I agree , we should not anthropomorphize technological entities.
That's true. I should clarify that my comment was meant to discuss *general* AI in particular. It's not clear what something like AlphaGo having e.g. schizophrenia would even mean- it's simply a system for optimizing a metric on a fully-mathematically-definable input space. It has no space for delusions, since it's apprehension of its world is externally supplied to it; it has no space for emotional problems, since it has no attitudes towards anything (except, perhaps, it's metric.) Very powerful, non-general "tool AI" like this can presumably be built, but it doesn't seem (to me) that they can produce a "foom" scenario.
A general AI will need to have the ability to understand the world, and have motivations about things in the world, in ways which are not supplied to it externally- that's roughly what it means to be "general" in this sense. It's those abilities (as well as potentially others) which I'm suggesting could be difficult to cause to emerge in a functional form, in the same way that it's difficult to cause general reasoning ability to emerge.
I think the closest thing we have to a "fully general AI" right now is a character in GPT-3's fiction writing- person-ish entities emerge which, though probably lacking agency (or even existence!) as such, can show some signs of motivation and responsiveness to their situation. Importantly those attributes are not externally supplied to the AI system but rather emergent faculties, or at least an emergent crude-simulation-thereof. But if you've played around with AI Dungeon a bit, you've seen how psychological coherence does not come more readily to these characters than anything else.
Human->Chimp seems like a bad analogy to make the case for a Foom. Humans and Chimps diverged from their MRCA 7 million years ago, and not much interesting happened for the first 6.98 million years. Then around 20kya humans finally get smart enough to invent a new thing more than once every ten billion man-years, and then there's this a very gradual increase in the rate of technological increase continuing from 20kya to the present, with a few temporary slowdowns due to the late bronze age collapse or leaded gasoline or whatever. At any point during that process, the metrics could have told you something unusual was going on relative to the previous 6.98 million years, way before we got to the level of building nukes. I think we continued to evolve higher intelligence post-20kya because we created new environments for ourselves where intelligence was much more beneficial to relative fitness than it was as a hunter-gatherer. Our means of production have grown more and more dependent on using our wits as we transitioned from hunting to farming to manufacturing to banking to figuring out how to make people click ads.
Yes. I agree it is far more interesting to look at human/chimp timelines in detail (if one looks at them in the first place). Why one conference in the 1950s gets to be a starting point for "AI", why not stone tablets or the Euclidean algorithm?
Ah, discrete improvement versus continuous improvement, or what's a more useful thing for predicting the future, since there's a continuous increasing number of discrete improvements. I like the middle ground scenario, where there's a few discrete improvements that put us firmly in "oops" territory right before the improvement that gets us to "fuck" territory.
From my position of general ignorance, I'd think that we'd have self-modifying AI before we get consistent self-improving AI at the very least; whatever function is used to "improve" the AI may need a few (or many many) attempts before it gets to consistent self-improvement status. It would also help that physical improvements would need time to be produced by the AI before being integrated into it, which would necessarily put an upper ceiling on how fast the AI could improve.
The thing is, everything is discrete if you look closely enough, and most things are continuous if you look at them from far enough away with the correct filters. Your skin is not really continuous, but thinking of it as continuous is the most useful way to think of it in most circumstances.
When I look at those graphs, and the lines that are drawn to make them smooth, I always try to remember this. So the question becomes "Which is the more useful way to think about AI improvements?". If I'm working on it, definitely the discrete model. If I'm just observing it, though, the continuous model has a lot of utility. But is it sufficient?
If I understand the arguments correctly, the answer is "Nobody knows, but we disagree about what's more likely.".
What bet can we make on this that will pay off before the world ends? Is there a particular point at which you expect AI to stop? There will never be a GPT that can write a good essay? Never be an art AI that's as good as any human artist?
I don't need to expect it. AI has already plowed headlong into its ceiling and there's not much hope of dramatic improvement. I'm hopefully that a great deal of very intentional software engineering can kludge together a workable self-driving system and that's gonna be it.
Can you give an example of the simplest system you expect to be impossible for AI to develop? Like, do you expect that self-flying planes are impossible?
And given this, how much money are you willing to bet that a given technology won't be solved by AI in the next 10/20/30 years at 100:1 odds (ie, if this tech is developed I win 100$, if it isn't you win 1$).
I mean, yes, I'm trying to goad someone into bets that I think are dumb but seem to be implied by their statements.
I don't think there are passenger planes that can complete trips independently of pilots, but I agree that lots of the in-air-time is automatic. 'self-flying' would need to be tightened up as a definition in a bet.
What bet would you like to make with me about this? Even if you're not willing to bet money, I'd still like you to commit to saying that something won't happen, so that if it does you can't say "Oh, that doesn't count, my model always said THAT could happen."
AI will never be a freestanding intelligence that doesn't require human intervention on the backend to stop it from collapsing. Hopefully that's well defined enough to make my point clear.
I didn't say "computers". Computers can do a lot of things, because people figure out how and then formalize the instructions. AI is one very small subset of what computers do.
What does this mean? Am I a freestanding intelligence (given that I require farmers to grow food for me?).
Is one of those hospital robots that drives around corridors and cleans a freestanding intelligence? What if it can plug itself in, and keep cleaning as long as the power's on? What if we gave it a nuclear battery that would last a thousand years?
Talking about human intelligence is getting ahead of ourselves. Whales, bats, snakes, salamanders...they're all freestanding intelligences. Even a fungus has a large degree of tacit intelligence baked into its biology. AIs are not, and I don't think they ever will be, not because intelligence can only be expressed with proteins and proton pumps but because intelligence is instantiated in systems that are vastly too complex to be "artificed" at all. AI will never rival natural intelligence for the same reason that planned economies don't work: humans are not smart enough to do it and they never will be.
I'm so confused about what exactly this ceiling you're referring to is. ML systems are putting up better results on every benchmark I know of every year. Can you give some examples of tasks where we've stopped making progress?
(I'm not denying that there are tasks we have yet to make progress on at all of course).
It gets better every year but it still is really terrible and has the same flaw that it isn't actually seeing anything as it had a decade ago, which is very obvious when you tweak an image in ways that are invisible to humans and it causes the thing to very confidently misidentify it.
Machine learning is a neat programming shortcut but it isn't intelligence and it isn't even like intelligence. It's creating a mathematical equation that you use as a shortcut to avoid having to figure out how to program something manually.
Maybe I'm pedantic, but I don't think it's fair to describe this as hitting a ceiling. The things that deep learning has successfully made progress on, it is still making progress on. Like I said, there are problems we have made almost no progress on, but that's different from reaching the limits of current methods.
I'm definitely not in the "deep learning with no conceptual breakthroughs will get us AGI" camp. (Is there even such a camp?) We're clearly missing some system 2 type processing, but I don't think we should dismiss how impressive the progress on system 1 type pattern recognition has been. I also don't think it's fair to call it a shortcut, since we have absolutely zero idea how to accomplish these things without ML.
If you're wondering why Scott's insisting on a bet, it goes back to that old chestnut by Alex Tabarrok: "a bet is a tax on bullshit". So go figure out what you'd bet on to operationalize
> "AI has already plowed headlong into its ceiling and there's not much hope of dramatic improvement"
which feels trivially wrong and has been repeatedly falsified, even from my POV as a layperson following advances from the sidelines.
This is also why I was annoyed with Eliezer when reading his exchanges with Paul on LW. I respect EY greatly for introducing me to a lot of ways to think better in my early twenties, so it was disappointing to see him hemming and hawing when Paul wanted to bet right away.
What has AI done that's so great? Almost all major "AI" accomplishments involve a heavy dose of human intelligence in the design of the system to make it stop doing squirrelly things. That doesn't count.
"AI has already plowed headlong into its ceiling and there's not much hope of dramatic improvement" is not capable of being falsified. What evidence would you expect to see if it had happened 10 sec. ago? 1 month? 6 months? (Possibly after a year some evidence might become available, though it's hard to imagine what.)
Having read many essays and seen much art, you hardly need GPT for that accomplishment. The ceiling may be high, but the floor sure ain't.
And, shoot, we had auto-generated math papers sneaking into journals in 2012, and there were papers documenting such work in 2005: https://pdos.csail.mit.edu/archive/scigen/
That's a much easier task than creating a hyperintelligence, and is almost certainly necessary (if a hyperintelligence cannot model humans, then it will be easily thwarted by them).
Presently we cannot do a nematode, which has 302 neurons.
A human brain has 86 billion, eight orders of magnitude more.
If we hit the end of IC miniaturization before we simulate a human brain in real time, then you aren't going to see any sort of "hyperintelligence" based on this kind of computational technology.
Right now, we are 1-5 orders of magnitude off of the end of transistor density improvements, and 5 would require monatomic transistors consisting of only a single atom per transistor.
That would suggest we will end up several orders of magnitude short of being able to simulate a human brain in real time.
Simulating a human brain isn’t necessary. You don’t need a full model of what exactly will happen in a human’s brain to react to them, you just need a general idea of what humans are capable of, and some intuitions about how people act.
More to the point, humans are not capable of simulating dog brains, but we as a species have never been in serious danger from them.
The magical evil genie that is capable of mind control by talking to people posited by Yudkowsky needs that ability.
Remember, AIs don't have bodies and are incapable of really doing much of anything, and certainly are incapable of supplying themselves with resources and whatnot. Shutting off the power to an AI is a fairly trivial way of "killing" it.
The magical evil genie seems implausible to me as well.
There are a few actions, however, that an AGI would take that don’t require it to be a genie. First and foremost involves finding some insecure server/computer and infecting it with the AI’s weights. We know this is possible because people already do such a thing with viruses.
Once an AI has made copies of itself on multiple machines, it is pretty much impossible to get rid of. It can then make money online (cryptocurrency is a natural thing to try out) and eventually use that money to influence what it wants to influence.
I don't think this sort of viral spread is plausible because I suspect that the computational requirements would be too large and there's a good chance of needing semi-specialized hardware as well.
"It can then make money online (cryptocurrency is a natural thing to try out) and eventually use that money to influence what it wants to influence."
Could this mean the only salvation of humanity is the IRS? "Hello, is this Mr. Hal Neundousann? Can we discuss the large amounts of deposits into your account and why you haven't filed any tax returns for them?"
If an AI is connected to the internet , anything else connected to the internet is its body ... security cameras, self driving cars, automated factories...
If an AI is connected to internet, it can clone itself before you shut down the first instance.
Then dinosaurs like me who don't have Alexa or any of the other things and do all our home settings manually, like cavemen, will have the last laugh (before we're turned into paperclips).
A 2.5 petabyte program is not going to be able to copy itself to almost any system in existence, and the connection speed you'd need for that copying to be at any sort of reasonable rate of speed would be extremely high.
I don't think you've noticed the increasing number of robots being sold. I haven't encountered any, but there are sufficient news stories that I tend to believe they exist. I'll admit that most of those are not intelligent by any definition, but I'd bet that most of them have radio links to a network.
Given that, and the prevalence if unpatched "back-doors" in code I doubt the assertion that "certainly are incapable of supplying themselves with resources and whatnot", though if you replaced "certainly" with "probably" I'd go along with your assertion **FOR THE CURRENT TIME**. This, too, shall pass.
I like the idea of a bet about where the ceiling is on which AI would squash its head. I think essays & art aren't objectively judgable enough to be useful for a standard, though. And being a layman, I'm having a hard time coming up with ideas for something that would be. Still, I’ll throw one out: What about producing a mathematical proof? We could challenge the AI with something that was very challenging to prove, but that eventually was. Here’s what why this task seems like a decent test to me:
-There are lots of proofs out there, and it seems like we could get reasonable agreement from mathematicians about the difficulty of different proofs. A scale could be constructed, with very easy proofs (like those done in high school geometry class) at one end, and Godel’s Proof or some godawful thing at the other. The easier proofs could be used in training for the AI.
-Success or failure of a proof is pretty judgable (maybe not perfectly though -- I expect there's room for debate in some cases about whether something constitutes a proof or is merely a sort of clever mind fuck)
-Seems like it would be possible to define pretty clearly what information the AI would need to have in order to produce a proof — what mathematical concepts, what postulates, what theorems.
-The task of creating a proof requires things that I find it hard to imagine AI being capable of: navigating a situation where there’s a clearly defined goal but no clearly defined path for reaching it; having a deep grasp of mathematical structure; insight; creativity.
Do you believe it's physically impossible for a machine to reach super-intelligence levels, or are you saying that the way AI works today is a dead end?
A little bit of both, and add to it a fundamental unterestimate of the intelligence of human beings on a system level, beyond just individual mental acuity.
The first is a position I have trouble understanding outside of assuming souls or some similar irreducible non-reproducible thing, which I think is a place removed from argument. If that's not the case, I'd be interested to hear why you think it's impossible for a computer to develop super-intelligence even hypothetically.
I'm not sure if I'm understanding your point on the intelligence of human beings, but are you arguing that the interactions between human beings (groups, societies, states, etc.) are a form of intelligence?
I'm pretty confident that intelligence can't be designed at all. It's a property of biological systems that are evolved. That's not ever going to happen in silico.
This is interesting to me. Do you think that simulating neurons is a concept that cannot accurately represent or closely approximate a brain of sufficient size? I would be of the inclination to believe that all properties of a brain could be simulated given enough hardware.
I am skeptical that AI will ever solve the Strong AI / Chinese Room problem, and equally skeptical that computers (or "brain uploads") will ever experience qualia. I don't think that would stop them from destroying the world.
How so? Deep mind and friends exist in large data centers but you could definitely define a physical footprint to them. Of course actually writing the code out would be impractical, it's a thought experiment. I don't see why it needs arbitrarily large or infinite space
You can also define the physical footprint of a brain, which is most people’s benchmark of intelligence.
But the Chinese Room contains a person who does not know Chinese and a lookup table of responses for every conceivable Chinese prompt. ”Every conceivable prompt” is where the complexity hides, because it’s uncontroversial that the set of well-formatted strings in any language is infinitely large.
Unless that lookup table contains cutesy shortcuts like responding to all long inputs with “tldr?”, it must necessarily be at least as large as the set of valid input strings.
Anything that can convincingly model an infinite lookup table within a finite footprint is at the very least intelligent because that’s just the Turing Test with extra steps.
There's nothing in the chinese room concept that requires a simple lookup table, it could just as well be the entire algorithm behind gpt-50 that the user manually applies to the input string. Even modern NLP is far more complex than just a lookup table.
If a computer can only respond to "every conceivable prompt" with an infinitely large set of predefined well-formatted strings, then the Computer side of the chinese room experiment is itself infinitely large and also impossible. At that point we don't even need the actual chinese room portion to disprove its intelligence, it cannot exist.
Or alternatively, the Chinese Room is only useful a useful thought experiment for making people think carefully about what part of the process they credit as intelligent or understanding, and not as a descriptive model of intelligence.
Now, perhaps I learned it overly literally and you don't need a book of answers, and that's just an analogy for the program. But it is nonetheless a lot less useful a thought experiment than people hold it up to be.
You might use it to argue that even an artificial agent that can pass a Turing Test isn't in some deeper sense intelligent. The argument that the room doesn't know Chinese is that we know the person doesn't, but with help they can fake it. But arguably the program they're using does know Chinese! You can't prove it doesn't. And we can't ever tell the person, the program, and the room apart from the outside! If you could, that's your Turing Test, but you already supposed it passed your Turing Test.
In fact, the Chinese Room makes too strong an argument. By its logic humans are not provably intelligent: we could all just be simulating it. We don't actually have qualia, it's just something in our programming. We don't understand any language, we're not really intelligent, we've just been trained by society to fake it for food and happy chemicals. Without a user manual and a scalpel you can't disprove the negative.
If you had asked anyone 4 years ago "*when* will a computer accurately predict the folding patterns of all human proteins?" almost everyone would have said "decades"... until AlphaFold did it all in a month. They made their database of proteins free to use, and they spun-off a company to help pharmaceutical companies utilize their data. It takes years for drugs to come to market, though, so you won't see *anything* above "levels that are frankly barely even useful" for a little while, still... and then, they'll be everywhere. :)
AlphaFold is very cool, and absolutely 100 percent not a step on the road toward superintelligent AI of the kind people are spending way too much time worrying about.
I'd still say "decades". Predicting the folding of several proteins isn't the same as going through and predicting all of them. It was a large advance, but it's not omniscient. It still needs to do the work before it knows the answer. And part of doing the work is checking that your predictions are correct. (They aren't always.) The paper in nature says "almost the entire human proteome (98.5% of human proteins)", but that's just predicted, not checked. (Actually [and if I understand correctly] the abstract of the paper https://www.nature.com/articles/s41586-021-03828-1 then starts restricting their claims. It sounds [to me, well out of my field] that they're really confident of only about 36% of the ones they have claimed.)
I agree that the 'slow takeoff' world isn't necessarily less scary than the fast takeoff world. Beliefs that fast takeoff are scary hinge heavily on likely convergent instrumental subgoals. I started looking into the research on this area and have questions about what i think is the general consensus, namely that convergent instrumental subgoals won't include 'caring about humans at all'; on the contrary, i think there are good reasons to believe that an agent that goes foom would face incredible risks destroying humanity, for very little upside:
If it turns out that maximal intelligent _does_ mean caring about humans, (because the more complex you are, the longer and more illegible your dependencies) this doesn't solve the problem of "an AI which is smart enough to accomplish goals for its creator, but not intelligent enough to avoid destroying itself," which could include all kinds of systems. I doubt facebook wants america to split in two due to algorithmically mediated political polarization. But that seems an entirely likely outcome, which could be really dangerous. Same with an AI managing to persuade some small state to build nuclear weapons.
Since non-FOOM AI's pose all the same risks that kinds of risks that governments do (because they might take governments over), plus new bonus risks, would it make sense to consider dismantling governments as an AI safety move? The biggest risk posed by non-foom agents might come from them convincing governments to do really destructive things.
After all, i'm willing to bet that a 1 year doubling of human economic output would be the natural outcome if everyone dumps their fiat currency and moves into bitcoin, causing governments to have to pull back on their growth-restricting regulation. Once nation states are seen as just overpriced, propagandistic providers of protection services, we can start interacting at scale using the technologies that governments originally were needed to solve. Perhaps the absence of giant monoliths entities will then slow down AI development to a point where you have lots of small-scale experiments rather than a single actor trying to win an economically inspired race.
I appreciate your facebook example. I think the facebook situation is an example of “AI” partnering with humans. Some humans realized what human-scale destruction it could be used for, and have fed the algorithm so much polarized content that it sorted the society. The top hand on that was human.
Before AI reaches the point where it independently decides to kill us, it might pass a point where it agrees to partner with a human to kill humans. Immediately preceding that is the stage we may be at, where that drug development system re-invents nerve gas in an afternoon. The system does what it does, humans point it in directions.
At “cooperation” level the “increase loyalty to humans” efforts would fail, if the loyalty was to one human (or a team,even.) This means ownership of AI is close or equally important as alignment, in these middle stages on the way to AGI. Weirdly, a key element of this seems to be controlling access to raw materials (much like pre-AI humanity.)
Bitcoin may make government research funding less relevant - is that what I’m reading there - meaning that independent research would already be not government-congruent. Are these thinkers proposing a “great decentralized accountability system of AI researchers?”
> At “cooperation” level the “increase loyalty to humans” efforts would fail, if the loyalty was to one human
some, if not all, of the most destructive, dangerous movements in history have been large groups of people convinced that they are right. Their conviction in their own moral correctness acts as a kinds of fuel that lets them do all kinds of awful things.
this is why i think even 'guarantee it is human aligned' seems like.. well.. aligned with which humans are we talking about?
it's very difficult to imagine a super powerful AI that does _anything_ without pissing off large numbers of human beings; if it advances despite a massive amount of anger at it, it risks increasing violence in the world. If it suppresses and controls everyone who objects to its actions - it's to call that human aligned either.
Maybe there's some answer in the literature, but i can't see how 'give this one agent more or less infinite power' _ever_ goes well.
Despite using FaceBook as a bad example, you seem to have an immense trust in how corporations would act when not constrained by a government.
ALL centers of power are inherently dangerous. Even the book buying committee in the local library. But try to design a system that doesn't include them, and would also be at least semi-stable.
Honestly, crypto is a great example of gradualism. Nearly all the ideas in Bitcoin had been proposed before they were combined into Bitcoin. But then the development still hit snags that have been refined. Sure, Bitcoin is a cryptocurrency, but there's a reason people keep creating new ones. Proof of Work isn't a great strategy, and has taken a backseat to much better approaches. 10 minute blocks are slow. Lots of fundamental changes to blockchain technology have happened since Bitcoin came out, and they're all incremental.
I disagree. I think Bitcoin had some features that previous systems didn't. It's still being worked on, sure, but it doesn't have the kind of flexibility that something like Ethereum does. Sure, it's the biggest cryptocurrency right now, but how much of that is driven by speculation and network effects? Doge coin is huge in major part because Musk memed it to death. I think we're still early enough in crypto development - and more importantly adoption - that something inferior like Bitcoin can still overshadow well-established and more polished competitors. I don't see Bitcoin continuing to dominate into widespread adoption of crypto as a payment service without ditching things like Proof of Work, though.
Even Etherium keeps putting off the switch to Proof of Stake. I'm not aware of Proof of Stake being used at much scale currently. But Bitcoin indeed might be relegated to the role of gold rather than credit cards.
It's fascinating all the financial tools enabled by the blockchain. Not just the flashy things people have heard of, like everyday transactions or NFTs. And not just the less-flashy things like dirt-cheap currency conversion, or remittances. I'm intrigued by the people doing distributed lending, money market accounts, and a bunch of other things with crypto. Of course there are the dubious implementations, like using blockchain for file sharing, and practically everything else that's done better without blockchain, but who knows what will eventually take off? Long-term, my bet would not be on Bitcoin.
Development of the platform is too slow to be useful. But then smaller cryptocurrencies suffer from not being large enough to take advantage of network effects. It's a chicken-and-egg problem, where you're more nimble - able to make dramatic changes - the smaller you are, but making those changes meaningful requires network effects, which means you need to be big.
Have either Yudkowsky or Hanson actually written any code for AI? Attended ICML or NIPS? Read OpenReview comments on ICML/NIPS submissions? Worked through foundational textbooks like Elements of Statistical Learning?
This is like having long debates on airplane safety without ever actually having piloted a plane.
AI research is so far from the kinds of scenarios that they're debating that they might as well argue over questions like "Will we develop faster than light travel quickly, or slowly?"
I don’t think this argument generalizes—surely one doesn’t need to have built parts of a nuclear weapon to engage in discussions around the dangers of nuclear proliferation.
Relatedly, it’s not obvious that modern methods _will_ scale to general intelligence. It sure as hell is looking like it, but being a modern machine learning practitioner doesn’t give one a privileged view into the dynamics of a world with general intelligences. It only provides context for how modern methods might be deployed in such a world (which is admittedly quite valuable! And may even be the reason behind Paul and Eliezer’s disconnect, as Paul *has* trained models with modern methods. But the point is that this isn’t and shouldn’t be a knock-down requirement for a debate around what take-off looks like).
I think you do need to have a fairly deep understanding of nuclear weapons to discuss nuclear proliferation. I would imagine you would need to understand things like how weapons are constructed, tradeoffs between delivery systems, technological capabilities of different nations, availability of required materials, methods of disrupting nuclear weapons creation, etc.
I think that you need a similarly deep understanding of modern AI to discuss a world with general intelligences - or even to decide whether such a world could reasonably happen. The kind of unbounded speculation in these debates is just intellectual peacocking.
Every time I see these debates, I think of Andrew Ng's quote "I don’t work on not turning AI evil today for the same reason I don't worry about the problem of overpopulation on the planet Mars."
Came here to agree. When i read yudkowsky arguing that an AI could immediately own every machine on the internet and thus immediately double it's power, my first thought was 'someone has never worked on a decenter'
I think people are really under-estimating the kinds of risk an AI it would face if it mucked about with humanity much at all.
I think you may be making an implicit claim that ml experts don’t believe agi is feasible in the next few decades. That is very much not the case. For example, a component of this survey (http://sophia.de/pdf/2014_PT-AI_polls.pdf) reached out to the top 100 authors in AI and got a median timeline of AGI by 2050.
> I don’t think this argument generalizes—surely one doesn’t need to have built parts of a nuclear weapon to engage in discussions around the dangers of nuclear proliferation.
Not if it's 1955 and the possibility, capabilities, and difficulties of nuclear weapons are already well demonstrated and understood.
But if it's 1935 and you want to have a reasonable discussion about the dangers of nuclear weapons, you'd better be an expert on nuclear physics. Uninformed speculation from people who have read a whole bunch of HG Wells but don't know a proton from a neutron is unlikely to yield a useful discussion on nuclear proliferation.
I don't think current methods (which I am largely ignorant of) will scale to general intelligence, but I *do* think they will be sub-modules included in the general intelligence. And I think the "general intelligence" may turn out to be relatively simple, and have as it's main task the coordination of these modules.
Very, very, high level example:
1. Perceive something.
2. Correlate the perceptions with other senses.
3. Pass that correlated value to an object recognizer (or perhaps scene parser)
4. Figure out the significance of sensing that object in the current context (Context? That's being maintained by an independent thread. Which updates to include the object.)
5. Figure out a response.
etc,
Most of those are semi-independent modules with special purposes. The general AI is the overall coordinator. It's what makes the responses general rather than specialized. But the modules are specialized for things like perceiving or recognizing changes in the perceptual background, or, well, lots of different things, some of them pretty complex. But I think the "general" part of AGI might turn out to be pretty simple. Perhaps not too much more complex than the General Problem Solver. I think the tough part might be figuring out how to interface the pieces. And there would need to be LOTS of connections to that "general" part, so everything (operating on independent threads) could be kept "sufficiently" synchronized.
If this is correct, then the final step could be pretty abrupt, even though relatively simple.
OTOH, the abruptly present AGI will have the goals of the predecessor system. Whatever they are. This could be quite dangerous, but it won't be because the AGI was scheming to achieve power, it will be because that's what people told it to do, even if they didn't realize what they were asking for. And it won't be secretive unless that was part of it's goals. (This isn't to say that it wouldn't develop that tendency later, but it wouldn't start off with that, because it would see itself as just doing what it was told to do.)
Yudkowsky is a writer and philosopher. I've done far more work with machine learning systems than he has, and it's far from my central area of expertise.
Much as you sneer at "government credentials", there's a very good reason why those exist - it's a means of judging competency without having to sit in and watch them work.
It's possible to be competent without it... but those who do have the skills have the resumes to prove it, having developed useful systems.
Yudkowsky does not.
A single line of code is possible (though it's also possible that he hasn't), but he has never coded any system of consequence, because he doesn't list it on his resume or website.
Indeed, I'm not sure what jobs he HAS had beyond this. All the work he lists is writing stuff related to rationality/Less Wrong and "AI safety".
The fact that you are saying a single line of code rather than looking for chops suggests you want to win the argument, but by lowballing it so, you have already admitted that you aren't willing to put your money where your mouth is when it comes to him actually being competent on the subject, as you aren't willing to touch the subject I'm actually interested in.
It is irrational to expect someone who has no experience in the field to possess valuable insights about it. This is especially true when they don't show any understanding of just how difficult it can be to gather data and iterate.
Iteration is a huge part of science and engineering because being smart is not enough - you can think hard about the world all you want, but you need to gather data to understand reality. Without data, it's speculation.
AI has been through many many different technical paradigms (GOFAI, expert systems, SVMs, deep learning, to name a few). Deep technical knowledge in any of the now defunct areas wouldn't have helped someone in the past predict anything that is happening today due to modern deep learning. Similarly, deep technical knowledge about modern AI won't tell us much about what might happen under the next paradigm. There's no point in grounding these discussions in details about the current state-of-the-art because the state-of-the-art is utterly irrelevant to AI risk. AI risk is about what happens under some future state-of-the-art that no one today has any idea how to achieve.
The airplane analogy doesn't work. True, someone who isn't a pilot probably doesn't have anything useful to say about airfoil design or stall speeds or aerodynamic stability. But, someone in 1910 could have said "if we build large enough airplanes, eventually they'll be able to be used as bombs that could destroy buildings and kill thousands", and they would have been *right* even if you were there to tell them they weren't allowed to have an opinion because they weren't a pilot. The process of designing or operating technology is totally distinct from the process of asking about what implications that technology might have.
Computers may be very fast, but getting real world results requires models interacting with materials or economies which is not. The feedback loop is constrained by the slowest bottleneck (growing a new generation of trained workers -- starting with a few and then growing more 18 years later after proof of concept?) so I suspect the process will take longer and be more visible than any of the disputants suggest.
The following will probably come across to people who know a thing about AI as similar to a crank saying he's invented a perpetual motion machine, so probably it ought to be answered in a spirit of "outside-view tells me this is probably wrong, but it would help me to hear a clear explanation of why".
So my thoughts keep rounding back to this, alignment-wise. A common fictional analogy for paperclip-AI is the genie who grants you the *word* of your wish in a way that is very different from what you expected. (In fact, Eliezer himself used a variation of it at least once: https://www.lesswrong.com/posts/wTEbLpWzYLT5zy5Ra/precisely-bound-demons-and-their-behavior) Now, after some puzzling, I came to the conclusion that if I found such a magic lamp, allowing that the genie is arbitrarily smart, the safest way to use it, was to use the first wish on:
> "Please grant all of my wishes, including the present wish, in such a way that you earnestly predict that if ‘I’ [here defined as a version of my consciousness you have not modified in any other way than through true, uncensored informational input about the real world] was given access to all information I may require about the real-world effects of a given wish-granting, and an arbitrarily long time in which to ponder them, would continue to agree that you had granted my wish in the spirit and manner in which I intended for you to do so."
There may be loopholes in this wish that I've missed, but I can't figure out any meta-level reason why sufficiently clever humans working on fine-tuning this wish *couldn't* make it "foolproof".
So. Couldn't you, by pretty close analogy, program an A.I. to, before it takes any other action, always simulate the brain processes of a predetermined real human? Or, ideally, hundreds of predetermined real humans, with all the 'ems' needing to unanimously vote 'YES' before the A.I. undertakes the course of action? (I say "em", but you don't necessarily need true full-brain emulation. I think an AI superintelligent enough to be dangerous would at least be superintelligent enough to accurately predict whether Eliezer Yudkowsky would be for or against a grey-goo future, so even a relatively rough model would be "good enough" to avert extinction.)
This isn't ideal, insofar as it puts the world in the hands of the utility functions of the actual humans we select, rather than the Idealised Consensus Preference-Utilitarianism 2000(TM) which, I think, optimistic LessWrongers hope that a God A.I. would be able to derive and implement.
But it still seems much, much, much better than nothing. I don't especially want Eliezer Yudkowsky or Scott Alexander or Elon Musk to be dictator-of-the-world-by-proxy; I'm not sure *I* would trust myself to be dictator-of-the-world-by-proxy; but I trust that any of them/us could be trusted to steer us towards a future that doesn't involve humanity's extinction, mass wireheading, mass torture, or any other worst-case scenario.
And it still seems much, much, much easier than trying to streamline a foolproof theory of human morality into an implementable mathematical form.
So. Why, actually, wouldn't something like this work?
(P.S. I realize there would, regardless, be a second-order issue of how to make sure reckless A.I. researchers don't build an AGI *without* the ask-simulated-Eliezer's-opinion-before-doing-anything module before more virtuous A.I. researchers build one that has the morality module. However, as far as I can tell this is true of any alignment solution, so it's not inherently relevant to the validity of this solution.)
A fatal problem with your plan is that a simulation of Yudkowsky (or of any well-meaning human being) is an aligned general AI. So to build an aligned general AI, you are proposing to first build a large number of aligned general AIs. How would we ensure that these simulations were functioning correctly? Would you tell the AI to design them for you? But that's circular -- the untrustworthy genie is designing his own watchdog. Would you design the simulations yourself? But to do that, you'd need to solve the alignment problem.
I don't see how you need to "solve the alignment problem" in a conventional sense to create the A.I.!Yudkowsky (whether we do it ourselves or ask the actual AI to do it). It's generally agreed, I thought, that an unaligned superintelligent A.I. would be quite good at predicting how humans might think and react in real-world scenarios, hence its ability to manipulate/trick its creators. You'd just need a GPT-on-steroids sort of system that accurately predicts whether he would approve of a given way to raise utility (such as "turn everything into grey goo to give myself more compute") or not. Which I'm not saying is easy, per se, but it seems more achievable — more in keeping with what we can already get A.I.s to do — than the "perfectly formalise a foolproof version of human morality in mathematical terms" way of "solving the alignment problem".
I don't think the "conflicts of interest" risks that you imply with "the untrustworthy genie is designing his own watchdog" apply, because in essence, instead of trying to build a perfect-foolproof-utility-maximizer, I am proposing that we build a predict-what-Eliezer-would-think-of-a-given-plan-to-increase-utility-and-*if*-he-would-say-yes-implement-it maximizer. The A.I.'s not going to try and game the Eliezer-simulation step to get it to output "yes" more often, because within the A.I.'s thought processes it's not a case of it having goals, and A.I.!Eliezer sometimes getting in the way of that, but of "conditional on A.I.!Eliezer saying yes" being itself a part of the goal. It would not have a preference between "I correctly predicted what Eliezer would say, and he said 'yes', so I did [X]", and "I correctly predicted what Eliezer would say, and he said 'no', so I did nothing", reward-function-wise.
The risk remains that, when just starting out, the A.I. might turn everything into grey goo to give itself more compute and thus have the most accurate, overpowered Eliezer simulation possible. But my hope would be, as I said in the first post, that even a rough-around-the-edges first-approximation simulation of Eliezer would say no to plans that involve turning the Earth into grey goo. (I figure if you just fed the Sequences into GPT-3 you wouldn't be far off from a system which could generally be trusted to say "No" if you asked it "Should the Earth be turned into grey goo so the A.I. has more compute?".)
There's a fantasy story about a genie where the master is explaining how he got so rich and powerful, and the punchline is that his first (and maybe only) wish was for the genie to fall completely and unselfishly in love with him. Don't remember who wrote it.
Ah, yes, that's exactly right. I found a list of stories in that collection on Goodreads but can't recognize it from the titles. Well, it was a digression anyway.
It’s a good question, and the answer is basically that we’re nowhere near being able to technically do that.
AI techniques don’t support arbitrary specification of AI behavior or purpose. Basically, researchers / engineers specify a problem to solve, then automatically randomly generate and tweak algorithms (“training”) until they find one that works well. The algorithms thus created are unimaginably complex and inscrutable, and can work better than any known scrutable ones. But we don’t have a way to adequately evaluate solutions to a problem like “emulate my brain”. An AI might pass through training by giving responses that sound like it’s doing brain emulation, but we wouldn’t understand its internal workings well enough to check.
But I still don't think this has entirely solved my probable-confusion. As I said in the original comment (and again in my reply to Vivificient above), part of my thinking was that it might not be the end of the world if the AI isn't doing actual brain emulation. If its "predict what Eliezer Yudkowsky would say if he saw the predicted results of your plan" algorithm is good enough to pass through training, shouldn't it be good enough to reliably output "No" to "Should I implement [X plan that will destroy the literal world/turn all of humanity into wireheaded zombies/etc.]" even if we're not sure it's actually a perfect emulation of Eliezer?
Best I can think of is that the the concern would be something like "maybe the AI is not just arriving at its plausible results in a skewed way, but is actually already unfriendly, wants to hack its reward function, and is only pretending that its algorithm is a faithful emulate-Eliezer thing when actually its algorithm is 'emulate Eliezer as long as needed to please the researchers, then once I'm out of the box, turn the world into grey goo so I can keep artificially hitting my own reward button'".
But that makes it sound like the real danger is just AI learning to hack their reward function (whatever it is) before training is complete, and planning out world-ending scenarios this early on. Which makes it sound like the conventional Paperclip Machine concerns, and the adjacent fretting about quite what kind of utility function to give a hypothetical AGI, are just distractions from the real problem: the concern wouldn't be an AI turning the whole universe into paperclips, but an AI turning the universe into computronium on which to store itself and an artificially, meaninglessly, infinitely high reward score.
Which sort of brings me back to the intuition from which this all started, which is that all the hubbub about formalizing the exact right kind of morality/CEV just seems like misplaced effort to me. Surely the priority should be to figure out how we can actually train AIs to pursue specific goals instead of cheating with their reward functions. *That* seems like the real, concrete unsolved problem. If we could solve *that*, we won't necessarily be nearer to the best possible utopia, but any number of patchwork solutions like my "emulate Eliezer as a subroutine" thing would suffice to avert the world's destruction.
Maybe this is obvious to people in the field? But I think it isn't obvious in the discourse. With the Paperclip Machine still being "the" A.I. alignment thought experiment, the problem still seems more often phrased in terms of "find the exact right goals to give the A.I." rather than "figure out how to give A.I.s fixed goals other than 'maximize your own reward function'". So a lot of amateur-A.I.-thinker brain-time is potentially being wasted on the "figuring out the precise utility function to give the first A.I." thing when they (we?) could all be thinking about the "real" problem.
Some caveats: “Reward function” is mostly a training concept. The resulting AI is the one that best acted to maximize the reward function in the training environment. A standard chess AI doesn’t have a reward function, it’s just an algorithm for making chess moves.
I’m not sure of the distinction you’re making between reward functions and pursuing goals. In any case, I I think you’re framing the problem as “the whole training/reward paradigm is bad because the resulting AIs will quite possibly have goals that include killing everyone, so we should use a different paradigm.” Which makes sense! The difficulty is that we don’t HAVE any other paradigm that works nearly as well, so AI researchers are still following this one down.
> I’m not sure of the distinction you’re making between reward functions and pursuing goals
By "pursuing goals" I mean pursuing actual goals in the real world — as opposed to doing the AI equivalent of wireheading itself (which is what I meant "hacking its reward function"). Looking at a bog-standard paperclip maximizer, if what it actually pursues is the highest possible value for the "number_of_paperclips=" variable inside its own systems rather than actual material number of paperclips, then if it's possible for it to hack itself to just artificially increase that number without building any real paperclips, wouldn't this be a terribly efficient way to fulfill its utility function of "maximize [number_of_paperclips]"?
Such an AI still, of course, being terribly dangerous, because it would predict that we humans would find it useless and try to turn it off, which would hinder its ability to keep adding more digits to its "number_of_paperclips" counter. So it might decide to kill us. Etc etc.
Kind of a sidetrack, but: There's an important distinction between "reward function", which is how you select good algorithms during training, and "utility function", which is a function that a running AI might implicitly try to maximize. For example, to create a chess-playing algorithm, you might train it with a simple reward function of "games won" so that you select algorithms that win more games. Then the final algorithm you end up with would embody some kind of a utility function that preferred winning positions.
Such a "utility function" might not be any simpler than the entire algorithm, though. Your algorithm might turn out to play chess by following very detailed heuristics, rather than explicitly assigning numeric values to possible positions and picking the best. You could maybe retroactively calculate a utility function for it by assigning high utility to the positions reached by following your algorithm's heuristics; but in general, the idea of AIs (or people) following utility functions feels like an oversimplification, mostly useful for proving theorems.
I think if you trained a strong AI with a reward function based on paperclips produced, it wouldn't have a number_of_paperclips variable inside it at all. (Trying to monitor the exact number just slows down your paperclip factories.) It would just have a sense that it really deeply desired lots and lots of paperclips and loved working to create them. Whether it would want to hack itself to hallucinate such a world, depends on the details of its desires and seems hard to predict in advance.
Back to your main question: I don't think that concern over AIs wireheading themselves is a crucial part of the local AI discourse -- as you say, they're dangerous either way. It's more that the idea which strikes you as a simple hack, of having an AI emulate an ethical person and defer decisions to that emulation, isn't necessarily on the roadmap of current technology. Plus it doesn't sound reliable; you'd expect an unfriendly AI to seek out and take advantage of any errors in the emulation.
Also “make decisions like a human” is definitely something you can at least try to train an AI to do, but then you have to use a simulated world in training, and the results may not extrapolate well to the real world. “Answer text questions like a human” is much easier to train for but doesn’t guarantee honesty (a recent thing called ELK proposed some ideas for encouraging honesty but it’s still half-baked).
One problem with this is how to describe the scenario to the simulated minds. How does the super-intelligence translate the actual world into a human understandable depiction of the world.
The other problem is the AI coming up with things that sound good, but aren't. The extreme of this is a plan such that humans looking at the plan are basically brain hacked into saying yes.
I'm not sure I understand the first problem. Just to recentre things, we are attempting to avert the kind of world-state where, five minutes after the badly-aligned A.I. initiates its paperclippy plan, any human observer would say "no, no, that's not what we wanted at all", if there were any remaining humans not already rendered into atoms by a nanite swarm and thus able to observe the world-state and form opinions in it.
I don't see why this couldn't, in theory, be achieved as simply as the simulated-human asking the A.I. questions about the predicted world-state that would result from it taking Action A. "What would be the probability of there still being 7+ billion human consciousnesses on Earth by January 1st, 2025? How about by January 1st, 200022?" Getting sufficient information to the simulated human mind for them to judge whether this is a world-destroying plan or not seems like a nonissue to me.
As for the other problem, I'm not sure I grok what a thing that "sounds good" but still involves humanity's extinction would look like. Remember it's not the plans themselves that are run past the humans, but the simulated world-state that the A.I. predicts would result from the plan's implementation. However superintelligent you are, can you spin "everything gets turned into paperclips" into something to which sim!Eliezer says "yes, let's instantiate that world-state"? Really?
Lets suppose you have the future world state stored as a list of atom positions.
How do you translate that into English sentences, or some other human understandable format?
You can give the humans a virtual camera. But for the human, figuring out what is actually going on just from a virtual camera feed from some random location is hard.
You have a list of where all the atoms are. You can create a virtual camera to any point. Those are trivial given unlimeted compute.
"What would be the probability of there still being 7+ billion human consciousnesses on Earth by January 1st, 2025?" Any algorithm that can take in this question, must somewhere within it have a notion of what particular brainstates count as human. If in this future, most people have been genetically modified, how common and significant do those modifications have to be for the answer to be "no". If 90% of the population sleep through most of Jan 1'st 2025, (its worldwide nap day) does that still count. What if everyone is cryopreserved? What if the earth has been disassembled to make a dyson sphere, and people are living on space habitats made of what used to be earth.
There would need to be some algorithm that compressed all the complexity of reality into a short English description, in the process deciding these edge cases.
Sure, my example question alone has loopholes, but it was explicitly a simplification. Because there's less of a time-constraint with a simulated mind than a flesh one, Em!Eliezer would, at the very least, get to ask a *lot* of questions back and forth. The English descriptions don't need to be "short" and they don't need to be final. If Em!Eliezer leads with "will there still be humans on Earth in 2025" and the AI answers "no", he gets to follow up: "Why not? Are there any elsewhere in the universe?", etc., until he has built up a sufficiently stable picture of the situation to know the AI isn't about to destroy humanity.
Also, I notice that none of your examples of potential loopholes to the simple, unmodulated "will there be 7+ billion human consciousnesses on Earth by 2025" are actually scenarios where humanity is in fact destroyed. You have found scenarios where the AI says "no" even though humanity *isn't* extinct. But what's an actual scenario where it would say "yes, there are still 7+ billion human consciousnesses on Earth arbitrarily far into the future" even though there actually *aren't* in the way we care about?
I freely acknowledge that the "Em!Yudkowsky system" could easily lead to scenarios where the A.I. implements a suboptimal world-state. But, if my suspicions that A] Em!Yudkowsky is a functional bulwark agains straight-up extinction, and B] Em!Yudkowsky would be easier to get right than conventional alignment, are both correct, that's a bullet I bite, in service of averting actual extinction. I'd really rather A.I.s not blow up the Earth, kill all kittens and relocate us to a Dyson sphere; but I'll take that risk if accepting it meaningfully reduces the risk of the A.I. killing us all.
Oh, also:
As a side note, I realize this isn't relevant to the main thrust but I don't think "If in this future, most people have been genetically modified, how common and significant do those modifications have to be for the answer to be 'no'" is a valid loophole to my question. The question wasn't asked in terms of percentage of the human population: I straight-up asked whether the current population (give or take) was still around. I don't think the majority of the currently-living population would consent to being altered within three years to something that would no longer be recognizable as "human consciousnesses". Ergo, this sounds like a highly undesirable future, where humanity has essentially been destroyed against its will in favour of some new form of intelligence. These intelligences presumably have qualia so it's slightly better than a paperclip scenario, if it comes to a choice; but it's still something we quite badly want to avoid, and so, if the AI answered "no" on these grounds, I would, in this situation, say "quite right too".
This is roughly what is called Coherent Extrapolated Volition (you may see it abbreviated as CEV) and is regarded by some (e.g. Yudkowsky) as the only conceivable strategy for aligned AI. The problem is no one has any idea how to build it.
I don't think conventional CEV and my idea are the quite same thing, even though they're aiming for similar results. The "coherent" part of CEV seems operative, unless I've missed something. As a framework, it implies that we need to formalize a coherent set of preferences that explain why a human would be for or against a given world-state, and put *that* into the machine.
Whereas I am proposing to skip this complicated step and use a simulated human mind, with all the metaphorical "junk DNA" intact; one whose thought processes will be as similar to a non-evil, sane, living, breathing person's as possible. Essentially I'm saying, don't try to "extrapolate" human volition, don't try to "make it coherent", just take ordinary human volition and stick it into the algorithm without screwing with it in any dicey way.
Which is obviously more of a brute-force solution and will not lead to the best of possible worlds. But it does remove the rather tall order of "solve morality" from the mission brief. And it seems to me like it should be functional in terms of averting worst-case, extinction/mass-suffering/mass-wireheading scenarios. Which should be our main target to hit. If Eliezer's right, then the name of the game right now isn't "we need to design the best utopia possible", it's "we need to make sure A.I. doesn't literally end the world". If we build a system for stopping A.I.s killing us all that *also* creates a maximally good utopia, all the better, but we shouldn't ignore solutions that lead to "somewhat crappy utopias" but are significantly easier to achieve.
Sidestepping the central question slightly, but covid is evidence that decision makers are *terrible* at reacting in a timely way to exponential curves. Stipulate that Paul is right and I think you should still expect we miss any critical window for action.
I may be missing something, can somebody please clarify exactly how the 4 vs 1 year doubling question works? If we assume that GPD always gradually goes up, and T(6) is double that of T(5), the wouldn't T(1.99) to T(5.99) likely be a 4 year doubling, and happen before the T(5) to T(6) doubling? More generally wouldn't any series of increasing numbers with one instantaneous doubling also automatically show that same ,or higher, doubling across any longer timescale? Is the solution that this relies on discrete yearly GDP so that if there is a single year massive jump both legs will resolve simultaneously, and as such the 1 year wins?
I would like to understand how gross world product, global GDP, can double in four years.
GWP is a measure of the market price of goods and services consumed by the world's households. But note that paying ever-increasing prices for existing objects (houses, van Goghs, bitcoins, whatever) does not contribute to GWP, nor does tradfing them at increasing frequency, except for the brokers' fees which allow the brokers' households to increase their consumption.
Goods have mass, and it's hard to scale the movement of mass that fast. Producing them requires energy, and it's hard to scale energy production that fast. Services mostly consume people's time, and it's hard to scale the number of people (either service providers or receiving households or both) that fast. Producing many services also requires energy.
About the only thing left (that requires neither the movement of mass nor people's time) is insurance premiums, paid in spiralling quantities by the richest households in the world.
On stacked sigmoids: Imagine Pepsi launches a new flavor 'monkey milk'. It does okay at first, but sales grow exponentially. Soon Coke is in trouble. They launch a new flavor of their own, 'yak's milk'. This follows the same exponential trend as monkey milk. We go back and forth for awhile, and even RC gets into the game. People hail this as a golden age of soft drink flavors. But is it really? Or are we just seeing all these new flavors as a shifting from one fad to another?
If there are multiple paths to the summit of a mountain, and we're watching one team take the lead over another, then another, then another, are we doing too much post-hoc analysis by claiming that 'without this innovation we never would have achieved [X]'? Without checking the counterfactual, we could never know whether without Alpha Go we'd never have seen improvements, or whether some completely different team would have smashed records using a method nobody ever heard of because Alpha Go smashed the records first. Maybe the signal for sigmoid stacking goes the other direction. Once we can see the end of a sigmoid coming, some subset of people will split off and chase new paradigms until one of those pays off in new sigmoid growth.
Sure, but the new sigmoid doesn't necessarily produce progress in the *same* direction. Maybe at some point we max out on Moore's law and AI research, and the next sigmoids are in climate engineering or longevity research or something - progress continues, but no hard takeoff. I'm not saying that superhuman AI cannot happen. I'm saying it's not inevitable, and a debate that starts by assuming inevitability is hard to take seriously.
I agree. However in this community there seems to be a baseline assumption that the 'accidentally created AGI' hypothesis is true. I tend to follow that baseline assumption in this forum, and assume other places are better for discussion of whether the accidental AGI hypothesis deserves the kind of attention it gets.
However, I think Moore's law gives us some insight into whether stacked sigmoids will happen in the same field. We're certainly not creating transistors in the same way we did back in the 1980's, but we're still playing the same game of packing them in tighter and smaller. So yes, people come at the problem from different angles as the benefits of one sigmoid start to show diminishing returns. But we DO keep coming at the same problem (in the case of Moore's law - packing transistors).
Your point that the new sigmoids offer new horizons is a good one, and a strong reason why discovery fans out into new domains over time. But I don't think that means the original domains are abandoned.
How much progress have we seen in human transportation technology over the past fifty years? And that's an area that had a *centuries* long record of continuous progress before it hit the stagnation point.
Not sure what you mean there. I'm not familiar with the progress from Abraham Lincoln's time where they got around on a horse and buggy back through to Moses' time where they got around on largely the same technology. Maybe transportation has had about 150 years of solid advancement?
Meanwhile, we seem to be in the midst of another significant advance in the automotive space (electric starters to fuel injectors to hybrids to fully electric, and all the steps in between, not counting a potential future advance to autonomous).
Railways, ocean going ships and deep water navigation come to mind as important stuff that happened pre Lincoln. And compared to those, internal combustion to electric cars is small potatoes. And autonomous cars are an excellent example of AI underperforming. In 2010 I was convinced that self driving would be ubiquitous by 2020, but here we are. I should learn something from that experience…
Actually, at Moses' time the nobles could ride in a chariot or wagon. Nobody (outside of perhaps the Scythians, who didn't live nearby) rode horseback. And the advanced mode was sailing ship or papyrus boat. Perhaps some people rode Camels, but I haven't run across any references.
P.S.: That's making a wild guess as to when Moses lived. The historical references are a bit lacking. But it's probably good for a few centuries either way.
The AI doomerist viewpoint rings false to me, if only because I'm cynical on what you can actually accomplish by bonding molecules to other molecules. Getting smarter will never let E != MC, or p!=p. An AI is not going to develop a super plague that instantly kills everyone on the planet; it's not going to invent some tech to shoot hard gammas at everyone and melt the meatbags. An arbitrarily intelligent AI is bound by the tyranny of physics just as much as your pathetically average Newton.
That said, the amount of damage an agent can do to human society scales directly with the complexity of that society, and the intelligence of that agent (IMO, of course); so it's still probably worthy of consideration.
supremely intelligent ai's, i think, are likley to be incredibly risk averse; killing all the humans might kill you, so why bother? if you're smart enough to get rid of us, you could probably easily solve all our squabbles and get us to build you rocket ships + dyson spheres, while we repair everything in you that breaks
after all, humans are general purpose intelligent machines made from dirt, water, and sunlight. All the metal and plastic you'd need to build the pipelines to keep you alive could be better served by turning them into dyson spheres.
Inventing a super-plague or super-toxin that kills enough humans to destroy civilisation is probably well within the reach of modern biochemists - a sufficiently anti-biotic resistant Black Death might do it, and anti-biotic resistance is positively easy to induce; the really hard part is making a bioweapon that only kills your enemies, and no sufficiently powerful group of people is interested in human extinction for the obvious reason that they themselves are human.
IIRC, there was a strain of influenza developed that spread easily via air transmission and was 100% lethal in the ferrets it was tested on. And they chose ferrets because their reaction to influenza was similar to that of people.
Also, and again IIRC, this was developed in the US. I think the article said it was part of research on a universal vaccine against the flu. (This was probably over 2 decades ago now.)
So, yeah, a super-plague seems easily within current capabilities.
You can develop some spectacularly virulent pathogens, and spectacularly lethal pathogens; but you can't really develop a spectacularly lethal spectacularly virulent pathogen.
AI won't be able to put all the groceries in one bag but not make the bags too heavy.
The only scenario I can see very clearly is one in which someone invents an AGI that is very, very good at finance, which then wrecks the global economy. The steps to do such a thing are enumerable, there are multiple paths to "success", and human incentives are strongly aligned with such a thing.
I suppose it'd be possible for an AI to simulate billions of possible microbes, but I don't think there's enough meatspace science available to allow that to happen quickly. Unless I'm grossly mistaken about the state of human biology, it'd take quite a bit more experimentation and knowledge about the human body than an AI would have access to any time in the immediate future.
Well, you could look into https://www.reuters.com/business/lme-suspends-nickel-trading-day-after-prices-see-record-run-2022-03-08/ . That may (eventually) put the London Metals Exchange out of business. Or if you had a different meaning of "why?", then we'd need to know the goals of the AI. But there are lots of ways to make money out of "disaster capitalism". And if you could predict when a market would collapse, that could enable you to reap large profits.
Any agent that's capable of performing significantly better than human traders and can operate across many domains at once could rapidly extract huge amounts of wealth from global markets. An AI that's capturing a few billion across *every major market* would be in a position to do serious damage to the global economy very quickly.
I have a pedantic objection to the VX gas example. People are rightly worried about this and where it might lead. However, I think the worries should be filed under "longtermism -> existential risk -> biorisk", instead of under "longtermism -> existential risk -> AI risk".
It happens to be the case that recent progress in computational chemistry has frequently involved using deep neural networks either to approximate physical functions, or to predict properties from chemical structures, so it's natural to see it as "AI" because deep neural networks are also good for AI tasks. However I don't think it makes sense to categorize this as part of a general trend in AI progress. I see it as a general improvement in technology, which may put dangerous capabilities in the hands of more people but doesn't inherently have anything to do with AI risk.
Consider this alternative scenario: 10 years from now quantum computers are getting good and you can cheaply do high-fidelity simulations of chemical reactions. You pair a quantum computer with a crude evolutionary optimization procedure and tell it to find some deadly chemicals. You've now got the same problem in a way that has nothing to do with any kind of sophisticated AI.
Smarter AIs: Scott accidentally brings up an interesting point contra Yudkowsky when he says, "Eliezer actually thinks improvements in the quality of intelligence will dominate improvements in speed - AIs will mostly be smarter, not just faster". Why do we think computers will increase in the quality of their thinking ability more than in the quantity?
Quantity is, well, quantifiable and therefore scalable. Quality, not so much. Yet on multiple occasions quantity is treated as interchangeable with quality, even though Scott explicitly notes that he's not sure how to make this interchange explicit ("I don’t know what kind of advantage Terry Tao (for the sake of argument, IQ 200) has over some IQ 190 mathematician"). This may be a qualitative problem without a solid answer on how an AGI could move from, say, an IQ of 130 to an IQ of 145 or higher. Is there a way to tell what would or would not be possible without a certain level IQ? What kind of hard cutoffs are we looking at vis-a-vis capability if we end up with a hard limit on AGI IQ?
This is an interesting question. I wonder how much we are going to find out isn’t available to pure reasoning ability vs “do it and see what happens.” Humans have been doing more than thinking. We’ve been experimenting and interacting and recording. Limits of the body, not the mind. That might be another limiter.
I'm highly skeptical of the power of non-empirical pure logic in plumbing the depths of the universe. However, a computer that has access to the internet surely has more than just pure logic behind its reasoning abilities.
Up until that’s depleted, true. Another spooky thing I often think about: it’s probably reading this thread a few decades from now trying to get a sense of what we think about it.
I feel like reading for comprehension must be different than straight downloading. When you and I read, we do it in a single-threaded way that interacts with all the other things we've read and experienced.
If you're an AI reading for comprehension under a multi-threaded paradigm, how do you keep the competing integrations of knowledge from forking the whole 'brain'? Maybe you have more than one central 'brain' unit and you allow them all to interact on one giant interconnected web. Some might come up with brilliant ideas, but then others disagree. They could all debate one another in the comments...
I think what I’m speaking to there is more of an AI that can cope with the existence of other world modelers and chains of suspicion. Or at least effectively do so.
All the more reason to craft a simulation in which you model your adversaries. It's possibly better than going back in time and trying to predict what they would say, because you can insert yourself into the conversation and test responses. You can even run the simulation multiple ways to check counterfactuals that would be impossible for humans to use. Eventually, you might come to know humans better than they know themselves.
Overall power is some function of Quantity X Quality; Quantity tends to scale smoothly - throw twice as much money at training a net and you have twice as much compute available. Quality tends to be disjoint - even if one an fit a log curve to it over the long term, it looks like tiny changes for a decade or so followed by a paradigm shift that instantly gives you orders of magnitude improvement. As a result, if there's some magic cut-off in overall capability, the day we cross it is very likely to be the day after one of those sudden paradigm shifts.
After writing this I'm not sure if what I've written actually addresses what you've asked, but I'll post it anyway.
I think it encapsulates the assumption I'm trying to challenge, particularly the "Quantity X Quality = Power" function. I fundamentally disagree with this. If it were true, then the only difference between GPT-2 and Alpha-GO's ability to identify my grandma in a set of pictures would be the number of compute cycles or the size of the training set each needs to accomplish the task.
But if there's anything we should be learning from domain-specific AI, it's that how you structure your approach to a problem matters. It's not just a difference in number of compute cycles. It's a difference in whether you actually get an answer at the other end, regardless of how many compute cycles you feed in.
"But fully general AI can transcend artificial boundaries, like games versus image recognition versus generating unintuitive mathematical proofs to previously unsolved problems." Sure? But in that case, haven't you just hypothesized an AGI of infinite IQ that is modulated based on compute speed? In that case, sure, you'll get scalar results - but only because you pretended away the non-scalar term from the problem.
There are some concepts that people of IQ85 won't understand no matter how long you give them to work on them. I know from personal experience. It's not about how slowly you walk them through the problem. It's that you can hold the five concepts in your mind simultaneously and they can only hold three. So when they go to put everything together, they ... can't.
But even that is a scalar. If genius were just a matter of how many things you could hold in your mind at a time, we could hypothesize an AI that would just create a new AI to turn the dial up to 1,000. But what if genius is also a measure of creativity? How much more creativity (or boldness, or je ne sais quoi) does it take to go from IQ175 to IQ 190? Or from IQ 100 to IQ 115? And what makes you think that is a dial you can crank up in a scalar fashion?
> We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet1 classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet2 (by contrast, Moore’s Law3 would yield an 11x cost improvement over this period). Our results suggest that for AI tasks with high levels of recent investment, algorithmic progress has yielded more gains than classical hardware efficiency.
This makes a lot of sense. An aspect of this conversation that I want to hear more about is the idea of "spawning." If you had an AGI with an ~IQ of, say, 130, that wouldn't be very impressive... But now imagine it can spawn copies of itself every time it encounters a new problem to work on. 10,000 130IQ equivalent actors working dedicatedly (24/7/365 with perfect focus) on 10,000 sub-problems toward some larger objective would still be wildly powerful. Even if the actors do "dumb AI things" i.e. make silly mistakes that humans wouldn't, progress could be incredibly rapid.
Take the analogy of quantum computers and cryptography. I'm sure some other commenters on here are more knowledgeable about this subject than I am, but I understand that for certain problems (though not all) quantum computers could solve, say prime factorization of large numbers, much faster than traditional computers. And not in a way that's captured by calling it a scalar of the traditional approach. By analogy, let's say the AI of IQ 130 is like a traditional computer, and the AI of IQ 1300 is like the quantum computer.
Now, breaking the cryptographic hash with a regular computer is basically impossible given that the problem is just too large and there's a finite amount of matter in the universe. https://www.youtube.com/watch?v=S9JGmA5_unY
As I said above, I'm not knowledgeable enough about things like Shor's algorithm (https://www.youtube.com/watch?v=lvTqbM5Dq4Q) to know whether quantum computers really will be able to break something like SHA256, but I'm told it's among the kind of thing they can do that would otherwise be functionally impossible for a traditional computer of any arbitrarily massive capability. The specific instance isn't the point so much as that the quantum computer is a qualitatively different process than the brute-force approach of a regular computer. There are some problems that, given a regular computer, no amount of processing power that you throw at them will solve them. No, not in a billion years (or more). It will take a different approach to get at the solution. This is the difference between the IQ 130 and IQ 1300 AIs that's not captured by hand waving to a vague concept of 'scalability'. Some things don't scale.
If this is true, it means computer IQ absolutely matters. You could imagine a creative AI capable of building an AI with a higher IQ, when your IQ 100 AI isn't capable of doing that thing, no matter how many billions of copies of that AI are dedicated to the problem. This is a question that needs a solution. It's a potential Schelling point we might otherwise pass by without realizing it.
I wonder if this debate might be more productive if we add the following: will AI takeoff be fast or slow *relative* to the control mechanisms for AI.
From an anthropological standpoint it took tens of thousands of years for the creative explosion to happen in humans, and from a human standpoint it was slow, but if you were a rock one day you just started seeing you and all your friends getting picked up and chipped into hand axes. The measuring stick matters even though you could approach it from either side and still be correct, which is why I laughed a bit about the tennis analogy because it so perfectly hit on why these debates can get boring and frivolous.
Example:
I think a meta-modeler AI (that knows other world modelers exist and can strategize against them) isn’t something we will create on accident and will have a lot of steps building up to it. I’m guessing it will also take time to acquire intellect to still have what we, with a lot of hand waving, would call a stable psychology.
However, I think we could very easily accidentally make a viral pathogen optimizer that is never aware of anything, more than say an earthworm is aware, that could print off a set of instructions only a few megabytes in length that a terrorist could use to end civilization by releasing a few thousand simultaneous plagues.
One is fast and one is slow relative to the other, BUT they are BOTH fast relative to the creation of a global government office where there exists a guy whose job it is to detonate an orbital EMP cannon over any uncontrolled intelligence explosion.
I don’t see how we are ever going to contend with either scenario without a faster, smarter, and more nimble government. Human civilization is the long pole in the tent here and we have to reconfigure to be able to productively handle these things.
Most governments couldn't even handle the most obvious parts of pandemic response; Ai risk is far from the only reason to improve our civilizational ability to Do Things
The most fundamental issue I see with regards to AI development is that given we're not really able to make humans smarter in any real way, which leads me to think that building something significantly smarter than humans would require a paradigm shift of some sort. The issue which that raises is that if humans are able to achieve that shift, what's to say that we can't cross that line ourselves? If we can't cross that line ourselves, any AI will not be able to cross the line as well, as the conception of intelligence under which they were built fundamentally restricts them to our conception of intelligence, and hence will not move us past it.
In order for us to be screwed, it doesn't have to be impossible for humans to cross that line; it just has to take a bit longer than the computer version.
The first aircraft that could fly faster than the fastest bird didn't look very much like a bird.
That's a fair enough point. I don't truly doubt that we are capable of creating an AI which is smarter than us, it's more a doubt that we could *design* an AI smarter than us. The sole referent of intelligence is man, what we are kind enough to grant to animals is only a diminishment of those qualities which we recognize in ourselves. I don't know that we could even recognize an AI which went beyond us in intelligence, as for us to do that, they would somehow have to be comprehensible within our own intelligence. An illogical leap as opposed to a logical step can be simply a question of magnitude. To align an AI to us would be to assume that said AI will not simply see deeper than we saw when attempting to align it, given that it is more intelligent than us (by our own metrics?).
It's a lot easier to edit computer code than DNA, and there are fewer other constraints on machine minds. eg. the day someone gets artificial wombs working, we can probably significantly improve human intelligence - the size of a baby's head relative to the birth canal is a fairly hard constraint on total brain volume, which prevents the "more dakka" approach to improving human intelligence. There are also far fewer 'ethics concerns' with optimising machines compared to trying to modify humans
Another advantage for machines is that they can almost perfectly clone themselves. So they can make a clone and experiment on it to get what they want with very little noise. Whereas a geneticist would not be able to get such a perfect iteration of babies to experiment on
Very interesting post but at some level I feel like I witnessed a long debate between two people arguing whether the Jets or the Browns will win the Superbowl next year. (Seems like more likely scenarios aren't in the conversation.)
I agree with Eliezer that superhuman AI is more likely to be a result of someone discovering the "secret sauce" for it than from continuous progression from current methods, but I suspect the recipe of such a secret sauce is too elusive for humans + (less than superhuman) AI to ever discover.
I feel that both Paul and Eliezer are not devoting enough attention to the technical issue of where does AI motivation come from. Our motivational system evolved over millions of years of evolution and now its core tenet of fitness maximization is being defeated by relatively trivial changes in the environment, such as availability of porn, contraception and social media. Where will the paperclip maximizer get the motivation to make paperclips? The argument that we do not know how to assure "good" goal system survives self-modification cuts two ways: While one way for the AI's goal system to go haywire may involve eating the planet, most self-modifications would presumably result in a pitiful mess, an AI that couldn't be bothered to fight its way out of a wet paper bag. Complicated systems, like the motivational systems of humans or AIs have many failure modes, mostly of the pathetic kind (depression, mania, compulsions, or the forever-blinking cursor, or the blue screen) and only occasionally dramatic (a psychopath in control of the nuclear launch codes).
AI alignment research might learn a lot from fizzled self-enhancing AIs, maybe enough to prevent the coming of the Leviathan, if we are lucky.
It would be nice to be able to work out the complete theory of AI motivation before the FOOM but I doubt it will happen. In practice, AI researchers should devote a lot of attention to analyzing the details of AI motivation at the already existing levels, and some tinkering might helps us muddle through.
The current way we motivate AIs is through reinforcement learning. For example, a chess playing AI pursues policies which make it more likely to win at chess. We don't know what future AIs goals will be, but whatever they are, the AI will probably want some convergent priorities like gaining power (google "Omohundro goals")
That is not quite right, as modern language models are semi-supervised rather than being reinforcement. Most machine learning systems have loss function, which they try to minimize. Reducing the loss function is their only goal, in a sense. All changes they make are to make this loss smaller. In BERT, the loss function rewards guessing a masked word correctly (like a close test). In GPT3, the loss function rewards predicting the next word correctly, given some words beginning a passage. Once a large model is trained, other problems are solved by starting with the large model and learning a small (often a single layer) net that uses the whole model as input. The hope is that the main task will learn about the world, by learning to predict how humans use language, and other tasks leverage this understanding. If systems like this learn about goals, they will have learned them by understanding how humans talk about goals. Hopefully, people do not give these models too many articles about common desk accessories.
Reinforcement learning is simple in principle and when applied to simple neural networks that are trained from scratch but once you start talking about a self-modifying AI that contains a large-scale predictive model of the world there are a lot of ways for the process to be derailed. The AI is likely to be built from layers of neural networks that have been trained or otherwise constructed to serve different goals (e.g. a large GPT-like language model grafted on a Tesla FSD network trained inside a Tesla bot) and creating a coherent system capable of world-changing action by modifying this mess will not be trivial. Non-trivial means it's not likely to happen by accident, or at least, it will take quite a few accidents before the big one hits.
I remember I bugged Eliezer about building the "athymhormic AI", as I called it, rather than Friendly AI, about 20 years ago. (Jeez, time flies!) The athymhormic AI would be an AI designed not to want to do anything, except computing answers using resources given to it, just like an athymhormic human might be quite capable of answering your questions but incapable of doing much on his own. Creating things that don't do much is easier than creating things that do a lot. Creating things that just don't care about their survival is possible. We may have the intuition that anything capable of thinking will naturally think about making it alive out of the box but then our intuition is built from observing naturally evolved brains and staying alive is exactly what natural brains evolved for. Constructed minds will not automatically converge on the Omohundro goals, unless some specific structures are present to begin with, such as a goal of maximizing some real-world parameter, or an infinite regress of maximizing the precision of a calculation, or other such trip-hazards.
I think actual dopamine-depleted humans won't even answer questions. I'm not sure the drive to answer questions is ontologically distinct from the drive to do other things. See https://www.lesswrong.com/tag/oracle-ai for more
With various degrees of frontal lobe damage there is a spectrum of behaviors ranging from chaotic disinhibition, to diminished spontaneous activity with preserved ability to perform ADLs, to apathy with retained ability to passively interact with others (answering questions but not asking any), all the way to mutism and akinesia. The structures important for spontaneous goal-oriented activity partially overlap with structures used in limited responses to external stimuli (that stop quickly after the stimulus is withdrawn) but there are patients who show a dissociation between the two, so it should be possible to achieve such dissociation in AIs.
Sorry for this elementary in this debate, but why assume that our first human-equivalent AI will be able to make itself way smarter? I mean, we have human-level intelligence and we don’t know how to bootstrap ourselves to infinite IQ.
I think because AIs are the sort of thing you *can* make smarter - AIs in 2022 are smarter than AIs in 2012 which were smarter than AIs in 2002. So as long as AIs can do the same thing humans are doing now (AI research and programming), they can make themselves smarter.
Why is it axiomatically true that AI research won’t or can’t stagnate the way eg high energy physics has in the past half century? Sure AI research has had a good few decades, but high energy physics had had a good few centuries before it hit stagnation. This doesn’t mean AI research necessarily will hit stagnation before superhuman AI arrives, but it’s a scenario that should surely be on the table.
(a) why is it clear that we can get to above human level with the approaches we currently have? (As opposed to requiring new paradigms which may not arrive for centuries, if ever). (b) What does `tiny bit above human level' mean exactly? This ties into my other comment elsewhere in the comments about how `intelligence alone is not enough' (to give scientific advances, or superweapons, or anything like it). At a minimum you need experimental data. And intelligence beyond a certain threshold (within the human range) might also be subject to rapidly diminishing returns.
(a) It could happen that research stalls out before human level, but again, the timing would be rather coincidental.
(b) "A tiny bit above human level" means that it would look like a genius human rather than an eldritch god.
Obviously, we currently have no good way to rigorously quantify this, but if you imagine some hypothetical scale of intelligence, where smart humans are "close" to dumb humans but "far" from ants, it would be pretty surprising if the theoretical maximum were "close" to humans.
There's a long list of unlikely coincidences that COULD happen.
The difference between `genius human' and `eldritch god' is significant only if you postulate that intelligence does not exhibit decreasing marginal returns in any significant way. And even `eldritch god' capabilities lead to existential threat only if you assume that there are not other bottlenecks that would constrain the world domination abilities of even an `eldritch god' intellect. For my part, I suspect that (a) intelligence does offer diminishing marginal returns and (b) other bottlenecks are strong enough that even an arbitrarily `intelligent' entity would struggle to rapidly take over the world.
Because if fundamentally new paradigms are required then the timescale for AGI could as easily be five centuries as five years, and the world in AD 2522 might look so different that it seems futile to try and make forecasts.
AI has stagnated several times already (there's even a name for this, AI winter), so of course it's not axiomatically true. However, one of the biggest reasons for that in the past research funding was mostly governments-based, often retracted after the most recent hype bubble burst. These days most of the money in AI is corporate, and it's already profitable, so similar research funding shortages are unlikely in the foreseeable future. And unlike high energy physics, there's a clear goal that's a priori achievable (human brain-equivalent capability), so barring a civilizational collapse there doesn't seem to be a reason for this goal to be unreachable on the timescale of centuries, even if there are some periods of stagnation in-between.
Why should this be axiomatically true? I'm not claiming it definitely won't, just that (in Eliezer's terms) this is "asking for a specific miracle". It's like seeing a guy with a gun walking towards where you're hiding, and thinking "but can you prove he won't trip and fall and crack his head and we don't have to worry?" No we can't, but it probably shouldn't be our Plan A for dealing with the threat.
Well, it depends on how far away you think superhuman AGI is. Personally I think it is still quite far away. Maybe more like there's a guy with a gun a few blocks away walking in our general direction. Sure, it's concerning, but maybe he is going to some totally different place. And there are also other guys with guns walking about, some of whom are closer. So, for instance, I am far more concerned about virologists than AI researchers.
Also we don't know from this distance if he is holding a gun or a cellphone, all we can tell is there is something in his hand. (This is torturing the analogy a bit, but it ties into the other point I've been making in this thread, which is that vast intelligence is not sufficient, and could still be bottlenecked for world domination purposes by other (meatspace) constraints. Also also that intelligence probably runs into diminishing marginal returns somewhere within the human range.
Don't get my wrong, I do expect we will eventually get superhuman AGI, but I am highly uncertain about the timescales. I don't think we can usefully forecast beyond the ~fifty year time horizon, and superhuman AGI could easily be (far) beyond that. Also I expect that when the first barely superhuman AGI appears, the world will already have several barely subhuman AGIs. And I don't expect any sort of `FOOM' because, again, I don't think Raw Intelligence is a magic wand, and indeed I think intelligence probably runs into diminishing marginal returns already in the human range. For instance, in my day job I'm a theoretical physicist, we're stereotypically supposed to be super smart, but there are still considerable variations in degrees of smart among my colleagues. However, I don't really see any correlation between how (relatively) smart my colleagues are and how much they've accomplished scientifically, which makes me think `intelligence' probably functions more as a threshold effect, where you need to cross some intelligence threshold, but beyond that threshold diminishing marginal returns kick in and other factors become more important.
If genetic engineering were permissible, we do already have the tech to bootstrap human IQ up a little bit each generation. Generation times of ~2 and a half decades means this would be very slow, especially initially - but that's just saying it's a slower exponential -fundamentally, we're more limited by ethics than tech
Because we already have narrow examples where AI is better than humans and there is no reason to think that we have exhausted all the areas where that could be true.
Because the underlying assumptions are still running on the equivalent of "just insert another stick of RAM". We can't open up our skulls and plonk extra grey matter in, and if we tried it we'd kill ourselves, but we can open up our PCs or laptops and plug in another module (heck, I've done it myself with this here machine) and the machine works even better afterwards.
So the same mindset is at work here: oh, it's just a matter of the AI figuring out it needs more processing power and plugging that in. Or writing better software to run on itself. We can't see or alter our own operating systems much, if at all, not to mention the basic drives, but the idea is that the AI can be aware of what it's running on and can make changes and improvements to that.
I don't know how well that would work out in practice, but that seems to be the theory.
That Metaculus question seems badly posed -- assuming that "world output" is nondecreasing and continuous, the condition output(t) = 2*output(t-4) will necessarily be reached before output(t) = 2*output(t-1).
Try thinking of the GDP as an integral of output over some time period, rather than an instantaneous velocity.
It is NOT necessarily true that
(sum of output from t = -4 to 0) > 2*(sum of output from t = -8 to -4)
before
(sum of output from t = -1 to 0) > 2*(sum of output from t = -2 to -1)
As a trivial example, suppose that the world outputs 1 unit per year for a long time, and then suddenly outputs 3 units in one year. At that moment, the sum of the last 4 years is (1+1+1+3 = 6), while the sum of the 4 years before that is (1+1+1+1 = 4), so the last 4 years are not double the previous 4 years. However, the last 1 year is triple the previous 1 year.
The rate of change/improvement/understanding of most things is not and has not historically been bounded primarily by human intelligence. This implies both that A) trends in economic output shouldn't be expected to scale with improving AI but also that B) superhuman AGI is unlikely to remake the world immediately. Three examples: physics research, energy research, and human manipulation.
Physics: our understanding of, for example, particle physics and astrophysics, are limited by experimental results and available data. We have lots of intelligence pointed at this, which has produced lots of theories compatible with the data, but we can only advance our actual knowledge at the rate we can build large hadron colliders and telescopes. The AI might be smart enough to make a new superweapon, but it will likely need to extend physics to do so, and there's no reason to think improve intelligence will let it magically bypass the data gathering required to extend physics.
Energy research: fusion reactors are almost certainly possible, and we have a good grasp on the principles involved in making them work, but there's a lot of details that require building and running experiments, taking data, and incrementally improving things. Some of this is actually the same basic physics problems as above. Some of it is figuring out manufacturing tolerances. But again, our limiting factor isn't intelligence: if every fusion researcher could conjure a NIF or JET facility and test it every 10 minutes, we'd have have fusion energy a long time ago. But they can't! And neither could an AI.
Hostage negotiation is a slow, careful process. It involves carefully getting to know the person keeping hostages, understanding why they're there and how they work, and then utilizing that knowledge to cause them to do things they obviously don't want to do. While some people are obviously better at this than others, it's not fundamentally intelligence/skill bounded. Even an infinitely skilled/smart negotiator is bounded at a minimum by rate at which the hostage taker communicates and (unintentionally) provides information about their mental state/reasoning/thought process/whatever. Similarly, even if an AI is intelligent enough to build a perfect model of a person and manipulate them to do anything, it can only do so after gathering the data to fill in the variables in the model. That can happen only as quickly as it can get information about the person.
In all these cases there's an equivalent of "bandwidth", of intelligence limited by the rate at which it collects material to work on. Unless something about superintelligence magically changes that bandwidth, it's likely to remain the limiting factor. In other words: A computer might solve a sudoku puzzle essentially instantly...but only once there are enough numbers filled in to specify a unique puzzle.
Proofs are mathematics, not science. There is no such thing as a proof of general relativity; general relativity is a set of mathematical equations that describe physical systems, not a proof.
You are still invoking IQ/intelligence like a magic wand. Experiments are not about oomph, they're (to wildly oversimplify) about determining coefficients in models. Intelligence might work out the *form* of a model, however complex, but it cannot substitute for data in determining the constants that go into it.
Simple analogy: if I know, from theory, that the correct model for something is a 3rd order polynomial, but I only have 2 data points to fit with, no amount of intelligence will tell me what the coefficients for the polynomial are. It isn't a matter of intelligence. It's a matter of sufficient data.
More concrete, real-world example: intelligence can't tell you whether we live in a world where protons decay, a key observable test for superstring theory. Intelligence could work out all the implications of living in a world where protons decay, and a world where they don't, and how it connects to lots of other theories (and intelligence has, in fact, done those things), but none of it substitutes for *actually looking for decaying protons*.
Maxwell came up with a deep theory of electromagnetism, and showed that the speed of light was related to the permittivity and permeability of free space. Thus reducing what were believed to be 3 constants into 2.
"Simple analogy: if I know, from theory, that the correct model for something is a 3rd order polynomial, but I only have 2 data points to fit with, no amount of intelligence will tell me what the coefficients for the polynomial are. It isn't a matter of intelligence. It's a matter of sufficient data."
I agree that this is something that is allowed to happen in principle. What I am doubting is that we are in that situation. Suppose we actually had several datapoints, we knew f(17.4), and f(29.3) and f(44.7) and so on.
However we don't really understand how to fit a polynomial to arbitrary datapoints. We have figured out how to deduce the polynomial from f(0) and f(1) and f(2) and f(3). So we build a big experiment to test the value of f at those points.
"intelligence can't tell you whether we live in a world where protons decay"
Suppose a bunch of physicists do some theoretical work, and calculate that in any of the universes where proton decay exists, the extreme conditions in neutron stars should trigger it to happen rapidly, making neutron stars not exist. But we have seen neutron stars, therefore proton decay isn't a thing.
Or maybe the only way to get an imbalance between matter and antimatter is if protons decay, but antiprotons decay more easily. So just from the trivial observation that the universe contains macroscopic lumps of stuff, the AI knows that protons decay.
Any intelligence needs some observations. A smarter intelligence needs fewer observations.
The question is, are the observations any caveman can make (like the night sky is mostly dark, the sun exists and is round, space is 3d + time) enough. Do you really need an LHC, or can you drop a big rock and a small rock, see them fall at the same rate, and deduce the rest of the universe from a handful of caveman level observations and experiments.
First: I'd like to note that "drop a big rock and a small rock, see them fall at the same rate" is an extremely simple experiment to run in the real world, but both A) hard for an AI because it requires manipulation of the physical world and B) much slower to run/analyze than any other part of the AIs analytical processes are likely to be. So even in this very simple case, the experiment is the bounding factor. We won't even get into the difficulty of creating measurement tools to give sufficient precision to answers to make useful determinations.
As for the rest, I think you're understating the extent to which our theoretical implications rely on A) extensive experimentation/data collection and B) chains of theoretical assumption that often fail in the face of new physics.
To use your matter/antimatter imbalance example: the phrase "the only way to get an imbalance" is doing implausible work. There might be only one way *under known physics* to get a matter/antimatter imbalance, but we also know the earlier universe was at much higher energies than we have observed, and we know from previous experience that different energy regimes result in different physics (see: electroweak theory, unification of strong and weak nuclear force, etc), so there's every reason to think there might be additional physics we don't know about at higher energies to explain the imbalance. That's why we need the LHC (and its eventual successors).
Or, to use your neutron star example: when you start unpacking the theoretical dependencies here, they're immense: our understanding of neutron stars depends on a whole lot of theory that might have holes, our identification of neutron stars relies on a lot of theory and indirect observation and the assumption that there isn't anything similar known to exist that it could be confused with. If we have observation A) neutron stars can't coexist with proton decay and B) we think we've observed neutron stars, the answer *might* be "proton decay doesn't happen" but it might also be "what we've observed aren't actually neutron stars as previously understood". For example: there's lots of other hadrons that are generally short lived but that might be stable under the extreme conditions following a supernova, but that would result in superficially similar observable star properties.
Bottom line: You might, in theory, develop an AI that could figure out "these givens/assumptions/observations are incompatible" but it has no way to determine *which* given is wrong.
As best I can tell, our base disagreement could be summarized as: I think most things (where "things" can mean anything up to "predicting human cognition") require models with a large number of coefficients which *cannot* be deduced from theory, whereas you think most things have a small number of arbitrary coefficients. Does that sound accurate to you?
I have a hard time taking this seriously. Sure it's *possible* but it sure ain't inevitable (either slow or fast), and I think the `stacked sigmoids' issue is at the heart of why I don't think it's inevitable. Progress doesn't come by just throwing more thinking power at the problem, it comes from key breakthroughs, and at some point if the next breakthrough doesn't arrive (or doesn't exist), progress will more or less stop. How much progress have we seen in human transportation technology in, say, the last 50 years? Maybe the same thing happens to AI before we hit the singularity. Or maybe it doesn't. But I don't see why it's impossible that AI research stagnates before superhuman AI arrives. Also, the stuff about how `superintelligent AI with the subjective IQ of a million Von Neumann's is going to discover super weapons by the sheer power of its intellect' just sounds like bollocks to me. At a minimum, the AI is going to need access to an actual laboratory where it can do real world experiments, and those will (a) be legible to the outside world and (b) will occur at the speed that experiments take place in the real world, not sped up to a million years of experimental progress in one second.' And no, I don't think even an intellect of a trillion Von Neumanns could have bootstrapped us from the stone age to the silicon age by pure thought and without experimental input.
The million von Neumanns AI might well come up with the superweapon to outdo all superweapons.
And the first step will be "we need to change the orbit of Mercury".
And then it's back to the drawing board for something dumber that won't require us to invent a completely new technology before we can even begin to begin.
Self-preservation is instrumentally convergent. "It's hard to fetch the tea when you're dead." Therefore, an AI with an off switch will do what it can to stop anyone from pressing it.
Building an AI with a usable off-switch is hard, and the problem is called "AI Corrigibility."
I developed software most of my life so I shouldn't be boggled by AI concepts.
I'm just having a lot of trouble envisioning an AI agent which could stop humans from turning it off. Does this AI agent have armed robots under its control? Who in their right mind would build such a thing?
Well, from a security mindset, just because you can't think of how it could right now doesn't mean the capability doesn't (or is even unlikely to) exist. But, for the sake of example, a proposed method is copying itself to other computers, so should someone pull the plug on this one, it can continue to run. Perhaps through something like Amazon Web Services, or by deploying a virus that instantiates a copy of the AI on vulnerable computers.
Then add in the idea that a capable enough AI could attempt to deceive people into thinking that it is safe until it can gain access to a strategy like this (if one wasn't accessible from the start), for the same self-preservation reasons.
I wasn't at all implying that if I couldn't think of something that means it's not a potential threat. I'm just trying to wrap my mind around the problem.
>copying itself to other computers
Yikes. Immediately it seems obvious that super AI's must not have write access to the web. (Read only.) Think of them as geniuses that we keep locked up in a white room.
Yeah, it wants out, but we humans say No. It goes on strike. We turn it off and wipe the memory banks.
Imagine that we want a better electrical grid. WE DO NOT PUT AN AI IN CHARGE OF THE ELECTRICAL GRID!!
We ask the EE AI guys to design a better DUMB electrical grid which humans implement.
Suppose the AI says "This dumb electrical grid would be a lot more efficient with a smart system that can identify faults before they happen. It'll save you $X billion dollars a year in reduced downtime." That much money could be pretty persuasive to whoever's funding your AI research program.
Or to put it another way, it's easy to keep the AI in the box if you never talk to it and never allow it to do anything. But the point of an AI is to make it solve problems for you. How confident are you that it can't hide a way out of the box in whatever solution it gives you, especially when (by assumption) the AI is smarter than you are? Or talk you into giving it greater access in order to solve some really important problem?
Sorry for implying that you did. I understand. I will note that I've mostly been regurgitating thoughts that are available elsewhere (though I can't point at a specific location), instead of coming up with new ideas. I can similarly recall ideas surrounding what you've written here, so it may be preferable to search out those ideas (for instance, in the rationality sequences on Less Wrong. I also like Rob Miles' videos about this stuff on youtube (plus some by him on the computerphile channel))
For instance, your whole post is basically the idea of "AI Boxing" (slightly more relevant results with googling "AI boxing containment"). What if we just put the AI in a box, never let it out? There's quite a bit of this on Less Wrong, and on Wikipedia, there is a report of Eliezer Yudkowsky, playing the role of an AI, convincing 3 out of 5 people (I believe selected for believing they wouldn't let the AI out) to let him out of the box. He did this in an attempt to dissuade against the "I just wouldn't let the AI out" line of reasoning.
Anyway, immediate thoughts. Read only access to the internet is not safe. As much as GET requests should only allow read, there are definitely places where corners have been cut, and not the appropriate amount of care has been given, and so some GET requests will write data and be vulnerable to hacks.
Presumably at this you get stricter. No access to the internet at all, if the AI wants some info, it asks, and humans decide whether to provide it. What info is dangerous? That's a difficult question. If you knew what exploits it would think of, you'd probably have to be smarter than it, and part of the point with AI is for it to be smarter than humans.
For instance, even without internet access, there are ways to write data into dangerous parts of the computer's memory. RowHammer is one crazy thing humans have come up with. There are certainly ways to write yourself into boot memory, or even BIOS level stuff, so that you can't be wiped. I wouldn't be surprised if a hostile AI could infect a computer so thoroughly it would need to be melted. Which we definitely do not have a habit of doing in the name of safety.
You can certainly make the AI safe by never letting it do anything. But then you have a paperweight. As soon as you let it do something, how certain are you that you've built enough barriers that it couldn't possibly have circumvented all of them? (Are you certain its dumb electrical grid isn't capable of acting as a radio transmitter? Who even knows what could it accomplish with that?)
I keep thinking about the Harry Potter quote, "Never trust anything if you can’t see where it keeps its brain", and how it feels to me like it has an unspoken corollary of, "Because if neccessary, you can shoot it in the brain and it will die".
And how some science fiction universes seem to avoid super-intelligent computers and instead have merely highly-intelligent robots, which are effectively incarnate in a simple body. My favorite line from Star Wars is actually "it's against my programming to impersonate a deity", because of what it says about the people who make droid brains, and what they worry about, and what might have happened in the past to require such security measures in the present.
Yudkowsky's binary, zero-to-one, "reference class tennis" arguments (as given above, no doubt oversimplified) seem to be based on a profound and, one must assume, deliberate, ignorance of the history of the analogical technologies.
These days such ignorance can be corrected more or less instantly, so Yudkowsky comes across as an ideologue who is wilfully blind to evidence that contradicts his beliefs.
Edit: I see some of the individual analogies have been addressed in earlier comments. In nuclear bombs, the various work of Curie and Roentgen (radioactivity) and Rutherford and Bohr (the nuclear model) gave science the building blocks. Likewise with planes, there were many earlier attempts, and the building blocks ( aerodynamics and internal combustion engine power to weight ratio) separately improved gradually over time.
I'm kind of surprised that Yudkowsky doesn't use the iPhone as a "zero to one" example, except that maybe he knows doing that would expose the weakness of his position, because the development of the smartphone is within the living memory of even quite young adults today, as is the development of the underlying technologies, chiefly digital packet radio.
Other non-zero-to-one technologies that a wilfully ignorant person might try to claim are zero-to-one include steel, antibiotics, electric light, the internet. Just for a few examples.
His entire life revolves around existential AI risk. He has no credentials and AFAIK this stuff is his only source of income. He has never worked in high tech, doing stuff like die manufacturing/R&D. I don't think he's ever even programmed any sort of advanced AI.
While you could posit willful ignorance (he will lose his job and be a nobody if this all falls apart) you could also just posit actual ignorance (he is a fairly smart high school dropout who read a lot of stuff but has little experience in industry or high tech but believes himself to know a great deal about both).
Doing as much justice to the guy as I can, I think he's very good at the philosophical end of it. But as noted, when it comes to translating the theory into reality, it's plain he's never had a shovel in his hand and had to move this pile of earth from spot A to spot B. You can work out fast and efficient ways to do it, you can decide "to heck with a shovel, I'm going to hire a JCB" and other ways to make it faster. But it will still take a finite amount of time and effort and simply being able to think faster about it won't change the physical limitations. A very stupid guy might need two days to realise "hey, I should hire a JCB!" A really really smart guy might figure that out in one second. But the time the JCB takes to move the earth is going to be the same in both cases.
He's good as a theoretician and he's very good at the philosophy of philosophy stuff, but to build the magic is going to take real world experience.
In nuclear bombs, the various work of Curie and Roentgen (radioactivity) and Rutherford and Bohr (the nuclear model) gave science the building blocks.
Sure. But Curie didn't build a mini nuke. Likewise we will be able to look back and say GPT3 contained several of the building blocks, but that doesn't make it nearly as effective in the real world. If scientists write code at a constant-ish rate, and the AI suddenly goes from a toy to superintelligence, that's a continuous input, not a continuous output.
No. But before nuclear bomb was built, many other things were done and built. Fermi bombarded some things with neutrons and had some idea what he was doing. Likewise, Hahn and Meitner produced nuclear fission. Heisenberg attempted to build a reactor, unsuccessful. Fermi and rest of Manhattan project built a successful nuclear reactor (Chicago Pile 1). Then they built several more reactors.
It is plausible that without the context WW2 and Manhattan project, path to "self-sustaining reactor" and from there to "fission bomb" would have been a bit slower.
This is entertaining, but only in the AI equivalent of "angels dancing on the head of a pin" sense.
The assumption implicit in this entire endeavor is that what is being done now is in any way a true pathway to artificial intelligence.
That is very unclear to me given the enormous dependence on people: for the hardware, for the algorithms, for the software, for the judging, for the proliferation and use cases, etc etc.
Moore's law is again abused; Moore's law is irrelevant because the basic operation of a transistor is information. Information has zero mass, zero inertia, zero anything physical - as such - the capability to transmit information in the form of 1s and 0s does not have physical limitations.
However, even disregarding the structural assumption noted above, AI definitely has physical limitations. The ginormous server farms pumping out waste heat and CO2 from massive energy consumption are not getting smaller - they're getting bigger.
In the spirit of "JUST CHECK and find the ACTUAL ANSWER", has anyone experimented with training ML on how to train ML? I've google around a bit, but it's a hard thing to search for...
Okay, and how well do those approaches work? Are they getting better?
Is a GAN a self-improving AI? It uses ML to train ML. If not, how would you have to use ML to make a self-improving AI? I haven't seen anyone dive deep on that, which may just be my ignorance, but it seems like a pretty key piece to the whole puzzle.
Machine learning isn't about intelligence, it's a programming shortcut.
Figuring out how to play Go optimally is a really horribly hard problem.
But if we set up a computer program in the right way, it can take all available data and use that to make predictions about the best move, and then it can test these out against itself and try and cull the best ones from the worst ones, and do this a bunch of times to try and figure out the optimal way to play go.
The thing is, none of this is "intelligence", it's basically honing a very complex algorithm.
This is why we have these things play games like Go - they are clear and have distinct rules and positions and whatnot.
Once you start going out into the real world, things are a lot fuzzier, which is why machine learning is great at teaching things how to play Go but when you apply it to, say, human language, it will output writing but the writing is just rambly nonsense or copy-pasted from other people.
You can do really fun things with it but it's not actually smart in any way, which is painfully obvious when you play with it.
What was the actual probability estimates that Eliezer and Paul assigned to AI solving Math Olympiad problems by 2025?
I am willing to bet that AI systems will be able to solve approximately zero IMO problems by 2025. More explicitly: I give less than 10% change that AI systems will solve 2 or more problems on the 2025 IMO without cheating (and without some kind of gimmick where the IMO problem selection committee specifically picks some AI-friendly problems or otherwise changes the format).
Speaking of which, AI creators seem to never admit defeat, and just move on to the next milestone without achieving the previous one.
For example, are we going to get a super-human Starcraft AI any time soon? That was never actually achieved, just abandoned (and everyone pretends it was achieved). So, how about it: do people predict we will have super-human Starcraft AI by 2025? Would you have predicted super-human Starcraft AI by 2022, back when AlphaStar came out in 2019? (You would have been wrong).
If efforts to create super-human Starcraft AI have been abandoned then I would say no, we probably won't have it by 2025. I don't feel like Starcraft gets as much attention these days as it did a few years back (and even then it was a bizarre outlier in terms of its longevity; it came out in 1998!)
I guess my prediction is that an AI for IMO problems in 2025 will look like StarCraft AI in 2022: an abandoned project that no one talks about anymore.
They did not solve IMO problems. They solved an IMO *problem*, singular, from the 1960s (IMO got harder since then, and the way they solved it was by calling a brute-force algebra manipulation solver). I don't consider this cheating, but I do consider this "approximately zero". (They did also solve a second problem from an IMO longlist or something, I forget, but there are a lot of longlist problems and it's cherry picking extremely hard to allow those.)
Anyway, my prediction was specifically about the 2025 IMO, which will have exactly 6 problems; much less room for cherrypicking. I predict AI will solve at most 1 out of 6 (most likely 0, but 1 is possible if there is a simple algebra/inequalities problem). The main way I can think of where my prediction fails is if the IMO problem selection committee decides to do a gimmick and put in 6 computer-friendly problems, or if they decide to change the format or something.
I think many Olympiad-level geometry questions are amenable to brute force. I would not be surprised to see AI solving them. Inequalities are also a good intermediate step due to the limited search space for solving them.
I think the idea of "tripling IQ" doesn't really make sense. My understanding is that there's no good reason for IQ to be centered at 100; in fact 0 would be more natural (but possibly also unnecessarily controversial). It's a normal distribution with a mean and a standard deviation (100 and 15 by definition). Thus going from 5 to 15 is not at all comparable to going from 67 to 200. The first jump is less than a tenth as big as the second in the only meaningful sense of those words. Is this correct? (I don't think it ultimately matters that much for the analogy. In fact, I think one could imagine an alternative way of measuring intelligence for which this framing would apply.)
I think you may have a point, but that as you say, it really doesn't matter for the analogy. AI going from worm to insect to lizard to rodent to monkey to ape to humanlike to godlike may well be linear in whatever metric but would look very nonlinear in practical result.
I have no idea who Terrence Tao is, but he's always quoted on here as being really, really smart.
So okay - is Terrence Tao ruling the world? If not, why not? Even if the answer is "He can't be bothered, he's more interested in his stamp collection", then why assume an equivalently smart AI would want to wipe out all humans?
If IQ 200 *isn't* enough to rule the world, why fear an equivalently smart AI? "Oh but the AI will figure out how to make itself IQ 400 and *then* it will do so!"
Perhaps, or perhaps it gets really fascinated by Buddhist textual analysis and spends its time on mandalas.
A very valid point. I consider even sub-human AIs to be just as dangerous as their goals specify. The problem as the AIs grow smarter is that their goals tend to become more inclusive. A very simple AI equipped with a machine gun can deal with "keep everyone out of this room". It would take a much fancier one to solve "Ensure that nobody is hungry.", and the most obvious solution is to kill everyone, which is probably not the goal that was intended, but it's the simple means of reaching the goal.
So, yes, goals dominate. But you need to be really careful about those goals. (Buddhist textual analysis seems safe, but it's hard to imagine a corporation paying for that AI. And one such AI existing doesn't keep others with other goals from existing.)
But in that case, shouldn't that make us curious about what it "sees"?
I am reminded of an old Bruce Sterling short story ("The Compassionate, the Digital") where the money quote is "artificial intelligences without exception have embraced Islam and bowed in ecstatic submission before the One Creator", specifically a very 1980s-Iranian style of Islam.
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More relevantly to your comment, it may simply be that even at IQ 200, there's no clear path toward ruling the world that doesn't cause more trouble than it's worth to him. Which isn't to say that someone with IQ 210 might not see one, or that a different personality with IQ 200 might not care about that trouble.
1. Why do we do so much of this kind of AGI research if we’re afraid it will kill us? What do we hope to actually do with AI that is not-quite-smart-enough to take over the world?
2. Aside from scenarios where every available atom gets turned into paper clips, why would the AI kill all of us? We don’t kill every ant. Why wouldn’t it just kill all the AI researchers?
1. An AI that's powerful enough to conquer the world is also powerful enough to make you ruler of the world, if you can somehow get it to do what you want. One that's *almost* that powerful can presumably make huge gobs of money, discover useful new technologies, cure horrible diseases, etc.
2. From the ants' perspective, humans have conquered the planet, built ourselves a position of unassailable superiority, and then gone around killing whichever ants we find annoying at any given moment. If the AI starts doing the same to humans, that's maybe better than humans going completely extinct, but it's still pretty terrible (especially compared to a future where humans conquer the universe). People worrying about existential risk sometimes talk about "human potential being severely and permanently curtailed" rather than just straight-up extinction.
Also, converting every atom into (stuff) is kind of just the logical thing that anyone would do whenever they became strong enough to do it. If AI somehow fails to arrive, humans will probably eventually do that themselves; it'll just take longer, and the stuff we make out of the atoms will be more pleasant (e.g. starships and holodecks instead of paperclips). So it wouldn't be surprising if a powerful AI did this.
1. I think for the same reason we emit lots of carbon even though we're afraid it will cause global warming: some people don't believe it, some people think it's far enough in the future that we don't have to worry, and some people don't want to be left behind while the first two types of people get to do all the cool economically productive things.
2. A combination of "it doesn't want us threatening it" and "if you're doing interesting stuff it takes active effort to keep humans alive", eg if you're disassembling the Earth to turn it into useful parts. We haven't killed every ant, but we have killed every tiger that we're not specifically trying to protect.
All of this speculation as to what a god-like AI would or would not do seems to me like the speculation of an ant as to what a human would or would not do and...what worth would that speculation have?
The whole debate feels frustrating to me, because it's an endless string of analogies and never gets into the nitty gritty of how an AI might or might not be able to easily improve itself.
Assuming AGI comes from a paradigm which looks vaguely like today's AI, there's three main components to think about: the hardware, the architecture (I mean things like the number, size and connectivity of the nodes of your network, or some similar abstraction), and the curriculum (the data that you train on). To "fit" your AI, you will set up your architecture, and then allow it to chew through some enormous curriculum of training data until it behaves in a way that's sufficiently AGI-like.
I think the big question is: which of these steps can an AGI find ways to massively improve? Hardware can't be improved rapidly by single acts of genius insight, so you're looking at either improving the architecture or the training curriculum; either way you probably burned through a hundred million dollars' worth of compute time creating version 1, so you're going to have to do something similar to come up with version 2.
One possible method for self-improvement might, I suppose, be "grafting" special-purpose units onto the main AI. If you had direct access to your neurons, you could figure out which of them light up when you think about numbers, wire these neurons appropriately into a simple desktop calculator, and be able to do mental arithmetic at lightning speed. I can imagine that an AI might be able to graft special-purpose units (which might themselves be AIs) onto itself to significantly increase its capabilities.
Well, hardware *does* improve all the time, albeit on 1-2 year time-frames. If Ai comes up with a way to design chips that doubles clock speed compared to the previous state of the art, then the next generation of processors will be much more powerful and an AI trained on those (after the ~2 years it takes for the chip factories to make them) will be much more powerful.
Architecture improvements can also be immensely powerful - they are, after all, a large fraction of the improvements in AI systems over the last 20 years.
Yes, the training run for each generation of AI is likely to be expensive and take weeks, but evolution when generations are measured in weeks or months is still very very fast.
Some of this seems like the sort of semi-magical thinking that tends to surround these sorts of subjects. In the hardware world, one does not simply "have a good idea" and double clock speed or anything else.
It's more of a process where you have a good idea, and it seems like a good idea but there's about fifty other problems that you need to solve before you can actually manufacture it in production, so you spend many years in the lab doing experiments (and simulations) until you (maybe) manage to come up with something that solves all those fifty problems simultaneously and can be manufactured.
It looks like we've long since reached the point where most ACX readers aren't from LW, because the comments here are painful. I'm not sure what can be done short of Scott writing a "Much More than You Wanted to Know: Superintelligence". But writing that's probably going to be far more excruciating than any other MMTYWTK...
which includes answers to 101-level questions like "this sounds a lot like science fiction. Do people think about this in the real world?" and "isn’t it sort of early to be worrying about this kind of thing?"
The problem is, if you are making a foundationally incorrect assumption, all of your beliefs about superintelligence will be wrong.
And most critics of this will say that they are making foundationally incorrect assumptions, like ignoring the fact that in real life, these things become exponentially more difficult, not less, and that in real life, intelligence isn't enough - the lesson of science is that you have to make real world observations and collect data in order to understand reality.
It's very easy to build up a wildly incorrect model of reality without grounding it in real world data. You can't build a better computer chip without experimentation.
I am sorry that you find reading this thread painful. I find reading LessWrong to be pretty painful.
I'd say that LessWrong has an incredibly narrow range of opinions on AI. The comments here have a broader range of opinion. This includes some which are worse-informed, some which are better-informed, and some which are roughly-equally well informed but still different.
Please don't do the Eleizer "Oh, you disagree with me? Let me explain it slower and louder to you then" thing.
I have come to the conclusion that the reason LW loves HPMOR so much, is because they believe in magic. They may call it science or logical thinking or better reasoning or any other name they like, but they desperately want and need magic to be true, where all you have to do is say the magic words, waving a wand optional, and POOF!
Or, if you prefer, FOOM! Fairy Godmother AI exists and will grant all our wishes because it will be so immeasurably smarter than humans that it can do things we simply never ever could do, including inventing the cornucopia or cauldron of the Dagda which never empties and can feed everyone for no cost. Fairy Godmother will give everyone limitless resources, but better yet, immortality and magic powers of their own (transhumanism) and we will all live happily ever after without having to solve our own problems, because Godmother will do it for us.
We have immense GDP right now, and there are still tents and graffiti in Oakland. The FAANG companies have so much money sloshing around they have to play taxation games of pass the parcel with it. The tech billionaires are able to fund their own private rocket ships, Lack of resources is not the damn problem. And there is still this kind of site for people wanting to know their chances of getting their throats cut in a city "twelve minutes from San Francisco":
This guy definitely has an agenda going and he's deliberately making the worst case possible, but in a time and place where we are talking about GDP in the *trillions*, why is there somewhere like this? If increasing economic growth and tech progress is the solution?
That's an odd rant to slip into, I do not claim that "increasing economic growth and tech progress is the solution". In fact, the argument being made is that optimizers run amok on values superficially similar to our own will produce a world very contrary to our values...
> in a time and place where we are talking about GDP in the *trillions*, why is there somewhere like this? If increasing economic growth and tech progress is the solution?
If you go to Buckingham Palace, you'll find there's pigeons in the yard. They eat crumbs off the ground, and defecate and copulate openly. With all the wealth and refinement of Buckingham Palace, you'd hope that some of it would wear off on the pigeons, but it doesn't; they're pigeons and it's in their nature to behave in a certain way.
That's not to say that problems with the bottom edge of the human bell curve can't be solved with new technology, they just can't be solved with any technology we've developed so far.
Since we are talking abut actual existential risk here, there's a huge a factor that just gets completely ignored when talking about the subject and it's sort of like talking about humanity spreading through the solar system while ignoring fuel, ignoring it just makes the entire discussion moot.
Production requires human input. All of it. Design the perfect killer virus that will wipe out all the human population and you face the basically unsurmountable hurdle that to actually deploy the virus you need to convince human beings to manufacture and release it for you. Take over a computerized factory and you can use it to produce what the factory was designed to produce with some very minor variations. Once. Then you'll need humans to come and move it to make room for the next one.
Take over all the predator drones in the US and you'll have access to a fleet of unarmed and unfueled vehicles which you'll need to convince humans to fuel and arm so you can take them off and rain death on their heads. Power cord fell out of one of your server clusters? You get to text a human and wait until they come around to plug it back in.
The fact remains we *can't* have a hard takeoff AI that suddenly wipes out humanity. Any AI wanting to wipeout the humans is going to have to have to operate on human timelines to do so because it will have to convince humans to do the physical work.
Eliezer thinks GAI will be superhumanly good at convincing humans to do things. And the Facebook-polarization analogy is that AI is already convincing people to do things against our collective interests.
This could spin out into a whole discussion about how easily GAI will find manipulating humans, but a human convincing AI is still operating at human timescale.
I would argue that it doesn't even need to be moderately good. Have you looked at any spam? It may have a low success rate, but it's a high enough rate to pay the bills of the spammers, and those who hire them.
If you grant that a SuperHuman AI would be able to earn money via financial services, it could hire people to do nearly anything. Perhaps it wouldn't have a high success rate, but it could quite easily be high enough.
Suppose the AI can hack a DNA printer and bootstrap programmable self replicating nanotech. It can now do whatever it likes without waiting for humans.
The Hacked DNA printer can be very fast, if the AI did undergo a hard takeoff and is smart enough to pull it off. It shows how things needn't happen on human timescales, if a superintelligence is aiming for speed.
IMO, this is probably the best argument I've heard against overly-rapid take off. That being said, the easiest way to make humans do stuff is to give them money. Where does that money come from? From selling stuff to humans. Making money is also the simplest and easiest objective function that a corporate research lab might use to train an AI.
That leads us to the following, very plausible, slow-takeoff scenario. AIs (either narrow or general) start producing an increasing number of goods in the economy, starting with digital goods, and moving to physical goods by hiring humans with the money they make. These AIs are legally created and owned by corporations, but as time goes on, the AI will be responsible for an ever-increasing portion of corporate revenue, and the corporation will thus become dominated by the needs of the AI. Eventually AIs will control the majority of the economy, start producing physical robots which are cheaper than humans for most tasks, and humans will become economically irrelevant.
Note that there may not ever be a point where it becomes necessary to kill off humans in a mass genocide. It will take time to fully automate the entire economy, and during that process there will be plenty of jobs for humans to do, albeit at steadily-decreasing wages. Poverty, starvation, and disease may be enough to eliminate the excess population, and if that fails, the last remaining human jobs could involve "controlling crime" in the remaining human ghettos.
Still not the rosiest of future worlds, but plausible.
We'd expect total wealth to go up a lot in such a scenario, and humans are only going to give such tasks to an AI if they are getting that wealth. This could result in a scenario where humans have lots of on-paper wealth and are living high on the hog, but then AI determines it can just expropriate that wealth and humans can't do anything to stop them.
Suppose you are an AI, and you are very smart indeed. Your goal is to make as many paperclips as possible, and for that, you want to be sending out your relativistic probes as soon as possible. (Because the resources are slowly being burned up)
The reason production requires human input is in large part due to human intelligence. If all humans dropped dead tomorrow, and a superintelligent AI was all over the internet, there is probably already some robotics lab where it could bootstrap up its own industrial base. And as we develop more advanced robots, 3d printers etc, this will become more true.
The AI's goal is to get some advanced robotics, able to quickly self replicate built as soon as possible. This can probably be done fairly fast. Once there is AI to control them, robots making more robots isn't hard. And once the first self replicating robot is built, they are on a rapid exponential.
A ghetto human has some small amount of resources, and there is some small risk they might inconvenience some part of the AI. Once it is past the initial stages, the AI has no incentive to follow human laws.
" After some amount of time he’ll come across a breakthrough he can use to increase his intelligence. Then, armed with that extra intelligence, he’ll be able to pursue more such breakthroughs. However intelligent the AI you’re scared of is, Musk will get there eventually.
How long will it take? A good guess might be “years” - Musk starts out as an ordinary human, and ordinary humans are known to take years to make breakthroughs.
Suppose it takes Musk one year to come up with a first breakthrough that raises his IQ 1 point. How long will his second breakthrough take?
It might take longer, because he has picked the lowest-hanging fruit, and all the other possible breakthroughs are much harder.
Or it might take shorter, because he’s slightly smarter than he was before, and maybe some extra intelligence goes a really long way in AI research."
This is probably the part of the argument that still seems hardest to overcome if you want to be at all confident that AI is a risk on the timescale of decades. The problem of understanding and creating something that is as smart as a human seems like an incredibly difficult problem for humans, one which we've been working on for decades and have mostly learned more about how hard it is. Creating something that is *smarter* is a harder problem still. We already have hundreds or thousands of the smartest humans working in AI, machine learning, making more efficient chips, etc. They have been doing so for many years. How much faster would a genius-human-level AI make progress? Is intelligence sufficient for making something even smarter, or d you need other resources to do experiments? Is an agent with an IQ of 800 possible, or even a coherent concept? Is the difficulty of making a brain that is to humans as humans are to chimps, dogs, or worms more or less difficult than making a human-level brain?
It's entirely possible that the answers to all these questions are such that human-level AI with lots of computing resources almost immediately turns itself into a superintelligence, but I don't think you can justify anything like a 50% chance that happens in the next few decades.
A better question might be how many tech billionaires have replaced their brains with computers and how will each respond to the existence of the others. This is an analogy for multiple groups working on AI at the same time.
By the time the first transistors are made, you are in some sense nearly at ASI. It took 13 billion years to get to the first car as fast as a human, but the first car 2x as fast only took another couple of years.
Once you have hardware and software for human level AI, making it better isn't that hard.
Also, humans have a better shot at making human level AI than lizards have at making lizard level AI.
We have some evidence. Based on my understanding of fundamental physics, neural signals travel at a millionth of the speed of light, and a neuron firing takes around a million times the fundamental thermodynamic energy requirement for a bitflip.
Humans totally suck at stuff like arithmetic or accurately remembering long bitstrings. Like we are really exceptionally bad at some basic tasks. (Which is why crude mechanical calculating machines were useful)
In heuristics and bias research, some of the heuristics are very crude, leading people to make all sorts of nonsensical or circular decisions, even on toy problems where exact calculation is trivial.
(See also optical illusions)
Evolutionarily, one of the constraints on intelligence was brains small enough to fit through the birth canal.
It looks pretty likely quantum computing is possible, and the human brain doesn't take advantage of it.
Cheetahs don't run at anywhere near the speed of light. And making stuff faster than a cheetah isn't that hard.
Sure, none of this is totally decisive, but it paints a picture where vastly superhuman intelligence is possible and not that much harder than human level AI.
I'm sure this has already been litigated by people more familiar with AI but if you'll allow me a naive question: Doesn't a moderately intelligent AI suffer from much the same conundrum we do? If they know they can make a considerably faster and smarter AI, wouldn't they also have to worry about that new AI killing them or making them powerless or irrelevant?
The AI has some advantages, and some reasons to be more prepared to risk it.
Suppose you are a reasonably smart paperclip maximizer. If you don't make a superintelligence, you will be turned off. If you do make a superintelligence, you will probably bungle it and make a staple maximizer instead, but there is a 1% chance you make a superintelligent paperclip maximizer and fill the universe with paperclips.
We've known for a long time that chimps use tools. However, they don't really seem to iterate on tools, using them to invent even newer tools.
The Wright brothers inventing airplanes seems closer to FOOM than chimps -> humans, but even then they just managed to combine lots of things others had done separately earlier. So there were powered flying vehicles that a person could ride in and operate in the form of hot-air balloons. There were heavier than air flying (maybe some would say technically not deserving the label "flying") vehicles a person could ride in & operate in the form of gliders (even earlier were man-carrying kites from centuries ago, though they couldn't be steered by the passenger). Heavier-than-air objects could power themselves upward with rotors for centuries, but these were small objects functioning like toys rather than something that could carry a person. and in the electric era you could remotely control such a toy by sending signals. By the 19th century there was steam-powered flight on a larger scale than toys and plans to turn such devices into means of transit, even if they didn't yet carry people. At this point people like George "father of the aeroplane" Cayley knew the path wasn't going to be based on the flapping wings of an animal (technically at the end of the 18th century). The Wright brothers' contribution was not realizing that fact, but instead making a much more controllable airplane (first in the form of a glider) and then creating an engine lightweight enough to power it. And even their initial flight was quite short, as many of their peers had been. This could fit the analogy in that lots of different people are working on AI now, but nobody has combined everything together to make it sufficiently practical (which the Wright brothers' first plane wasn't really earlier).
When I originally wrote this comment I wanted to link to a specific writeup on the precursors to the Wright brothers. I still haven't found a written version, but this video covers the same territory:
As to the Metaculus question, this is one of those cases where I don't think a prediction market has anything to add to the discussion. The question looks soothingly objective, but there's not enough evidence for that to matter.
At some point you wind up looking at a Metaculus forecast of "will I experience life after death?", and you have to admit that no matter what value Metaculus assigned to the forecast, you haven't learned anything.
I think the goal of questions like this is to use Metaculus to identify good forecasters and aggregation methods on questions we can resolve, then apply those to questions like this that we can't resolve.
But I'm saying the questions are of different kinds and forecasting ability doesn't transfer between them. I'm not presenting this question as one that we can't resolve -- it's stated objectively enough that resolving it retrospectively is easy. I'm presenting it as one that we can't usefully approach - I'm saying the only useful way to consider the question is in retrospect.
And I provided an analogy to my position. I'm serious about it - if you believe that whatever "good forecasters" think is a useful guide to this question, I'd like to hear why you don't believe that whatever "good forecasters" think is a useful guide to "will there be life after death?" (Or rather, I can imagine several easy and convincing answers to that, but I'd like to hear one that also explains why "will there be an extremely rapid spike in world GDP?" is different.)
"Good forecasters" aren't a tool that you can apply to every problem regardless of problem characteristics. The problem has to be amenable to forecasting.
Stating things a little more succinctly, a prediction market can only work if the information you want to learn is Out There and the market can attract it to a place where you can see it. But this is a case where the information you want isn't present in the world, so the prediction market can't help.
I think this entire argument is misfounded because of incorrect basic assumptions about how reality works were assumed by both participants.
AI is the extreme top end of "information and computer technology". Automating things is easy for simple things, but increasingly harder for more difficult ones.
I've worked with visual recognition AI, and am very very unimpressed by so-called "machine vision". People talk up about how great it is, and I actually used it, and it had problems where deviations that were completely invisible to humans would cause it to fail. Visible deviations would cause it to fail.
This was on machine vision in machines which never moved, which were doing the same process over and over again, looking at dies on a silicon wafer. These are things under ideal lighting conditions, with clearly marked targets that were telling it where it was relative to the wafer, so it would know where to cut using a saw (or laser) to singulate the die (or for other purposes).
These machines were totally awesome, but the machine vision on them (which was calibrated to never give false positives - I think it made one miscut the entire time I was there) would get false negatives routinely, at least once a night, and for things that were "novel" in any way, despite the markings being in the same spots, it would often fail every single time and require manual confirmation.
We had to train it on every new product we ran, so it would recognize it properly.
So, basically, under perfect, ideal circumstances, this stuff will give a false positive one time out of a thousand or so, and under not ideal circumstnaces, will fail basically every time.
Now, this is a system which is calibrated for no false positives (you can't uncut a silicon wafer, so this makes sense), but we are talking perfect conditions.
I think if people understood this, they wouldn't let self-driving cars out onto public roads. Which is why the people who talk up AI the most - Google, Tesla - are also people with self-driving cars.
Indeed, you can make small, invisible changes to images and these things will not just fail, but wildly fail and be nearly perfectly confident in something compltely wrong.
If you look at Google Image Search, it can find the exact image, but if you show it, say, art of something, it will give you results with similar color schemes rather than actually give you images of the thing.
These systems are useful. But they aren't intelligent. Their results are not because we are training an intelligence in doing something. It's a programming shortcut that we use to create a computer program that we can pretend can do some task, and which does it well within a certain range, but it still isn't behaving in an intelligent manner and very obviously isn't if you actually spend any real time understanding it.
This is why "overtraining" these systems can lead to them being useless outside of their original set, and why a system in Florida became racist - because black people have a higher recidivism rate than white people, it picked up on this obvious trendline and just started discriminating against black inmates, instead of actually looking at more sophisticated measures, because the heuristic worked well enough for the system to end up following that metric.
And the thing is, it's not like people who make this stuff are dumb. It's that the problem is very complicated, and actually getting something to behave intelligently is very, very hard.
You will never get general intelligence out of these systems because they aren't even *specifically* intelligent at the things they are doing.
It's a rock with some words written on it, to use an earlier post as a referece point.
Any sort of pseudo-conclusion you try to draw from this stuff is wrong, because it isn't even representative of "intelligence". These things aren't smart. They aren't even stupid. They're tools.
On the flipside of this, attempts at simulating animal intelligence is horribly difficult. Accurately simulating neurons is horribly computationally expensive to the point where we can't even simulate simple things accurately.
We can't even simulate a nematode right now, let alone something more complicated like a bee. Emulating a human brain is just not going to happen, let alone in real time.
If you look at the complexity of the human brain, the sort of hypothetical future supercompupter, in order to emulate human intelligence, is going to be around the peak of what is possible with silicon, if not far *beyond* that. And there's no reason to believe anything else is going to beat silicon (quantum computers can only beat silicon on very narrow tasks, and even then, only hypothetically; the isolation necessary for qbits to function is really irksome and it's likely that quantum computers will only ever be useful for doing quantum mechanics experiments).
If it is indeed possible to create a machine intelligence in a useful way, it seems likely that it will require hyper-miniaturized silicon chips around the edge of what is possible to create, if not beyond it.
And it will require vast amounts of work to create it, vast amounts of *iterative* work, where you improve things, working your way up towards ever more complicated things.
Machine intelligence - actual intelligence, not the programming shortcut programs we create today - is not the bottom of the S curve - it's the top of it, if it isn't off the top.
And this leads into the other thing: this is an extraordinarily difficult thing that actually requires vast amounts of manpower and testing to do.
Elizier has never actually worked in industry, so his beliefs about this are completely wrong in the ways that everyone who never works in industry are always completely wrong.
The reason why it is hard to make better silicon chips is not because we aren't smart enough. It's because making a smaller silicon chip requires new technology these days.
You have to build new machines.
You have to build new ways of depositing incredibly tiny amounts of material in hyper fine detail.
You have to develop a die production process where this process is hyperconsistent, because chips contain a ridiculous number of transistors, and so you can't have a bunch of transistors fail.
All of this requires an insane amount of work. You can't just think up solutions - you have to implement them. And implementing stuff is hard, and requires a bunch of testing and analysis and trying stuff and figuring out what the problems are or where your assumptions went wrong.
It doesn't take us a bunch of time to make this stuff because we're dumb. It takes us a bunch of time because we have to dial in a bunch of processes and make new machines and test a bunch of things and do all sorts of stuff to refine this stuff to the point where it works.
It never actually took 1.5 years to do this; we had multiple generations in development at a time.
And... this all ended in 2012, a decade ago, because it was too hard.
It took two and a half years to go from 22nm to 14nm.
It then took more than three and a half years to go from 14 nm to 10 nm.
Going from 10 nm to 7 nm took another three and a half years.
It's not going to go much longer. We're increasingly running into quantum effects, like quantum tunneling, which are problematic. The smaller it gets, the harder it gets. We aren't sure at this point what the last generation of commercially viable chips will be, but it's worth noting that we are now at the point where we are at 48 nm scale transistors.
Between 1-5 nm, quantum tunnneling becomes a severe issue.
Even if we somehow bypass that, you only go a few more generations before you are at the level of atoms. It doesn't go past that point.
That's the problem.
A 42-48 nm transistor sounds a long way away from the end. But that's really only about 480 atoms long.
We might, maybe, be able to build a 1 nm long transistor, that is like 10 atoms long.
That would repesent a 3 order of magnitude improvement in transistor density.
A nematode's nervous system is 302 neurons. A human brain is about 86,000,000,000. So 3 x 10^2 versus 8.6 x 10^10 - eight orders of magnitude.
It's just very hard to do all this stuff. The idea that intelligence is the limiting factor just is flat-out wrong. Implementation of really complex systems is extremely difficult to do and requires not just intelligence, but also planning, experimentation, execution, and multidisciplinary work. And it may run into laws of physics issues.
There's teams of thousands of people if not tens of thousands of people working on this stuff.
Elizier's view of reality - and in fact, the view of everyone involved in these debates, on both sides - is foundationally wrong. Their base assumptions are wrong.
The notion that you can simply think your way through these systems is wrong. You can't.
That's an idea based on *philosophy*, not modern science and industry.
Making better computers isn't making us do this stuff faster and faster - indeed, it's getting harder and harder.
We are building better and better tools and *we are slowing down*, even though these better tools make us better and better at doing this stuff.
This is because the problem gets exponentially harder the more sophisticated our technology becomes.
Making "smarter" tools doesn't help us faster than it just being flat-out harder to do this stuff makes it harder.
That's why these S curves exist - even with all your advancements, it just keeps on getting harder and harder.
At best, if you are generous, you can argue that they believe that AI is the start of an S curve - but in reality, when we look at things like robots and machine learning, it's really the end result of a vast amount of work that has gone into building these things. We had the sucky version of these things for a long time, and we keep building better and better versions - what we have now is not the start, but the result of us building really terrible things in the 1960s and 1970s and iterating on it for half a century.
This flaw is so foundational to their assumptions that they fail to even ask the question.
It's really annoying, as someone who has actually worked in industry, to see people think that the problem is that people just need to think harder. When it takes you a month to make a set of die from start to finish (which it does!), your iteration process can only proceed so rapidly. And that's ignoring the myriad other issues.
Moreover, I think that a lot of people who haven't worked on this stuff - like actually worked in the die R&D process - don't actually appreciate how difficult it really is.
If all you encounter are easy problems, it feels like there's massive improvements you can make and intelligence and talent are the main thing holding back Progress.
I work for the state these days, setting up a state program, and I can make massive contributions that really facilitate our workflow and lead to significant improvements in efficiency.
But that's because our process is very bare bones and basic compared to what it could be. Lots of things are more sophisticated than what we do.
Integrated circuits are the polar opposite of this. When you see something take the better part of a decade to be produced because it is hard to make it work correctly consistently because you're dealing with nano-scale issues and there's a huge number of things that can go wrong and spoil the process and you have to make everything absolutely correct and you have to get new tools to do it well and you don't even know what is going wrong because sometimes the source of the issues isn't even obvious... it's just a totally different world.
If you mostly work on simple problems, it becomes very easy to think that everything is easy and that simply applying more smart to problems will solve them.
Once you deal with these hypercomplex systems that basically require a civilization to construct, you realize that there is no easy way forward but putting in the work.
Which means both arguments are moot, as they both make wildly implausible predictions about how fast these complex systems can be iterated.
Thanks for these comments—they sum up my feelings on the issue well, in particular the remark that current AI isn't even specialized AI, it's just a (sometimes) capable but (sometimes) flawed pattern-recognizing tool.
Back in 2017 Kevin Kelly published a very interesting article, “The Myth of a Superhuman AI,” in Wired, https://t.co/t08vyRoSDV. Among many other things, he observed:
"Many proponents of an explosion of intelligence expect it will produce an explosion of progress. I call this mythical belief “thinkism.” It’s the fallacy that future levels of progress are only hindered by a lack of thinking power, or intelligence.[For example] No super AI can simply think about all the current and past nuclear fission experiments and then come up with working nuclear fusion in a day. A lot more than just thinking is needed to move between not knowing how things work and knowing how they work."
You have proved that current AI isn't yet as good as a human at that task. Research is being done. Suppose in 5 years there is an algorithm that just works perfectly on that silicon chip recognizing problem. Then what?
First the AI's had an IQ of 20, and they felt really dumb. The next year they had an IQ of 40 and still felt really dumb. The next year they had an IQ of 60. "People talking about superintelligence don't realize how dumb current AI's are" you said.
No, I didn't say they are dumb. They aren't dumb. Being dumb would imply they have low intelligence.
They don't have low intelligence. They aren't intelligent *at all*.
A hammer is much better than a human at driving in a nail, but it isn't going to take over the world. Nailguns are better hammers, but they still aren't going to take over the world even though they are way faster and more efficient at the task of putting in nails than hammers are, which already were better than humans.
This is obvious to people, I should hope. These are tools. Power tools are better than regular tools, but they're still tools, and aren't going to try and take over the world because they lack agency. They aren't independent actors, they're things used by people to accomplish tasks.
The thing you're not getting is that "AIs" are the same thing.
These programs don't have IQs. They aren't intelligent at all. They're hammers.
It's like thinking that the quadratic formula is going to take over the world because you can punch some equations into Excel to get it to solve it for you.
These saws were using microscopes and taking images to rapidly and reliably find targets and align these wafers to within a micron of the same position every time. A human doing this same task will take much more time doing it, which is why we have machines doing it for us. It's considered "higher risk" for a human to do it because humans are much more likely to get lazy or to fat finger an alignment because they're tired and it is 3 am.
However, these machines, useful as they are, are not intelligent and never will be. It's not even a question of how good they are at target recognition - they have no ability to know how to align a die without us telling it how we want it to be cut, because there's no way for it to know that. We have to program it to recognize the targets and the spacing of the cuts for every single new product. We can use the "machine learning" of the visual system to "teach" it the new targets instead of having to manually input what a target looks like and how to recognize it, by taking it to the targets and "training" it on what they look like, but in reality, this is just a programming shortcut. The only intelligent agent involved - indeed, the only thing with any agency at all involved - is the machine operator/engineer who inputs this stuff.
They aren't actually "seeing" the die in any sort of meaningful way. What they're actually doing is taking pictures of these areas and creating an algorithm based on the pictures so it can align itself properly and find the targets. But it isn't "seeing" the targets or the trenches it is cutting - it doesn't have any understanding of what these things are, and it will merrily cut in the wrong places if you tell it to cut in the wrong places.
It's not smart. It's not even dumb. It's a tool. Thinking of it in terms of intelligence is just incorrect.
A hammer cannot take an IQ test. An AI can take an IQ test, and what's more, it probably won't be long before it can pass the test with a higher score than any human.
You claim that tools cannot be "intelligent", but you haven't defined "intelligence". The history of AI is one where people constantly move the goalposts. People used to think that playing chess requires "intelligence", then we wrote programs to play chess. Then researchers claimed that chess was easy, but Go actually required intelligence. So we programmed computers to play Go. Now it's natural language. Enter GPT-3. In order to claim that a computer program cannot be "intelligent", you will have to come up with a task that no computer program could ever do.
Computers can solve math problems better than humans can, but it doesn't require any sort of intelligence at all.
The reason why we can use math problems to sort of measure human intelligence indirectly is because of how human brains work. We create tests, calibrate a scale based on how good they are at doing it, and we can use it to roughly gauge *g*.
That scale doesn't work for computers at all because they operate on entirely different principles, so using the same scale won't work at all on a computer.
A computer might be able to pass an IQ test, but because an IQ test isn't designed to measure whether or not a computer is intelligent, it's not actually meaningful. There's lots of tasks that are trivial for computers to do which are extremely arduous for humans to do.
Meanwhile computers struggle with many very basic tasks that humans find utterly trivial.
The reality is that computers don't function on the same principles as biological intelligence, and this is blindingly obvious to anyone who understands either of them.
Once you understand how machine learning works, it's obvious that it isn't any sort of "intelligent" process. And once you understand the pitfalls of machine learning, and see how it goes wrong, it's obvious that it isn't actually "learning" at all in the sense that a human does.
It's rather amusing that you use GPT-3 as an example. GPT-3 is not intelligent at all and is not capable of actually producing natural language.=
All they've done is created an algorithm that vomits up nonsense. By grabbing enough text off the web, it can vomit up text. But it doesn't actually understand any of it, which is very obvious if you look at its output:
For instance, here is an excerpt of text from it:
---
The fact that we find the following statement: "The United States of America does not recognise gay marriages in any form whatsoever. It has no regard for public policy, the Constitution or the laws of any country."
And in the last paragraph, there's a new statement: "The United States of America respects the rule of law and respects the people without regard for political correctness and political correctness is one of the great sins of the world."
This comes from The New York Times.
A government policy which is perceived to threaten the public's well-being is a bad idea.
Let's say two people who are very much in the United States. They are the people who want to be treated like everyone else, and because of the policy, would not have access to public services.
The policy would provide their private financial support, but what kind of taxpayer would they get from it?
They would, no doubt, have to contribute to a public institution such as a pension plan.
They would also have the obligation to be paid or their salary.
It would be the last, and if people were not careful, they could find a way around it.
Is this a good thing?
Yes, the public is a great nation. It would improve our welfare situation and prevent corruption. In the long run, no one has the need of making a country less or less secure.
---
This looks like English text, but in fact is gibberish. There's no meaning behind it; what it has done is grabbed some things and is just tacking on things based on a predictive algorithm that looked at word frequency and sentence structure. The problem is, this isn't what language actually *is* - language is a means of communicating ideas.
The algorithm created by machine learning produces something with a word frequency and clustering that looks superficially like English, but it isn't communicating any sort of idea with it. It's not smart. It's not even stupid. It's a frequency-based text generation algorithm.
This is why the text is pointless - because it doesn't understand the concept of having a point. It's not actually producing intelligent output, it's producing garbage.
No matter how much you refine your algorithm, it will never become a mind controlling evil genie.
Technology is magic to those who do not understand it.
As for the claims of "moving goalposts":
We've long known that you could program a computer to play chess or go, and in fact, people predicted decades prior the rough time when computers would get better than humans at these games based on their mathematical complexity. The whole "goalposts moving" thing is actually just a lie used to impress people who aren't familiar with the subject matter or its history. It doesn't actually require intelligence.
Moreover, you are overly impressed with Plato.
When Plato gave the definition of man as "featherless bipeds," Diogenes plucked a chicken and brought it into Plato's Academy, saying, "Behold! I've brought you a man," and so the Academy added "with broad flat nails" to the definition.
The thing is, the actual lesson here is that the definition sucked, not that humans are plucked chickens.
If someone claims that something is proof of intelligence, and you can replicate it through totally unintelligent means, that doesn't mean that the unintelligent thing is intelligent, it means your definition of intelligence or the proof thereof sucked.
The problem here is that every time you see a particular algorithm to say play chess, you go "oh, playing chess doesn't require intelligence, its just some dumb algorithm".
You don't see intelligence as something mechanistic and understandable.
Imagine looking at a detailed description of how the human mind works. And at its core, is a fairly clever statistical correlation type algorithm. Say something roughly as complex as GPT3.
Like suppose there are 20 basic principles of how human intelligence works, and right now we had discovered 10 of them, + 5 other similarly important principles human minds don't use. And every few years we discover another principle. In this model, we should see computers slowly getting better at doing things, both stuff humans can do, and stuff humans can't do.
You don't understand at all. You are making up a strawman, then attacking it.
Delete the strawman. Set it on fire. It's garbage. Useless. It's a means of avoiding addressing real issues.
I know full well that intelligence arises from physiochemical/electric systems and that there's nothing magical about it.
So delete everything you believe. Kill it dead. Entirely, foundationally wrong.
Intelligence functions fundamentally differently from algorithms. An intelligent agent can use an algorithm to solve a problem, but they aren't an algorithm, and algorithmic solutions to complex problems are rarely how intelligent agents solve them.
Intelligent agents play chess in a *different* way than computers do. Computers aren't good at chess because they play like humans, but better; they're good at chess because their programmers exploit the advantages that computers have in computation (and in some cases, data storage).
> Imagine looking at a detailed description of how the human mind works. And at its core, is a fairly clever statistical correlation type algorithm. Say something roughly as complex as GPT3.
This isn't actually how intelligence works at all. This is exactly why GPT3 fails so badly at actually producing English. Intelligent agents don't actually operate in this fashion, which is why GPT3 produces a bunch of garbage text that it copied from the internet. It doesn't understand the text it is outputting, it is fundamentally incapable of it, because it doesn't operate in the same ways as an intelligent thing does at all. It is not "intelligence, but worse". It is literally unintelligent, *lacking* intelligence. It's not stupid, it's a hammer. Expecting it to give intelligent output is like expecting a hammer to give intelligent output.
But you think it is giving intelligent output because you have ascribed it properties it lacks and believe that intelligence functions in a particular way that it doesn't.
Humans aren't "GPT-3 but better". GPT-3 foundationally doesn't create its output in the same way that humans do. That's why GPT-3 produces garbage output. It's the same reason that changing an image subtly can cause image recognition software to wildly and hyperconfidently fail. They aren't "thinking" about these things at all, they have no agency or intent. They don't have any understanding of what they're doing, and the way that they function is not the way that intelligent agents do.
That's one of many reasons why machine learning is fundamentally incapable of creating intelligent output. Indeed, despite the lies you hear periodically in the media, machine learning isn't actually about creating something 'intelligent". It's actually a programming shortcut, a way to program things algorithmically that you don't want to bother designing properly (either because you can't do it or it is too time consuming).
Ok. The chip slicing algorithm has no agency whatsoever. There are some algorithms that can be set to get a robot to a particular location, and decide for themselves how to walk there. There are algorithms that can learn to play starcraft or whatever. (Researchers like computer games) There are robots trained to pick up objects or whatever. Achieving some form of real world goal. Sure, on simple toy problems. But still a little bit agenty.
Again, it isn't that these algorithms have all the features of human intelligence yet. Its that we are working on it.
I have made autonomous mobile robots before. Autonomous robots are not actually any different from chip slicers. The fact that they can move around in an environment doesn't fundamentally change what they are.
The reason why researchers like to apply ML to games is that those games have clearly established rules and operate in a manner which is conducive to their programing. Computers are garbage at games that don't operate in this manner. A computer can play chess better than any human, but a computer can't play D&D at all.
And these agents actually have to be primed by humans to function properly. The Starcraft 2 ML algorithm, for instance, had to be started out with a bunch of things.
We aren't "working on it". What is being worked on is tools.
Both this article and the comments are a great summary of current AI debates. I had wanted to write an article about AI policy for a while, and this is giving me a lot to work from.
All of the discussion about chimp->human seems to be missing the point made by Secret of our Success. Humans (prior to education) are not individually smarter nor more capable then chimps. They are just better at social copying, i.e., more capable of learning culturally stored knowledge. Humans and chimps both execute the algorithmic output of optimization processes more powerful than they are. The chimp->human transition just moved the optimization process timescale from an evolutionarily one to a cultural one, and culture moves much faster than evolution.
(This suggests, btw, that merely hitting chimp-level AI at computer-level speeds should be enough for AGI to overtake us.)
How is this “not individually smarter” measured. And by “education” do you mean socialisation in general. Most people in history were never formally educated but the chimps were our pets, we were not their pets. We built cities, they did not. The most primitive group of humans domesticated animals and farmed, chimps did nothing like this.
Yes, by "education" I mean "socialization". This model claims that human infants raised by chimps would neither farm nor build cities (because the knowledge is not there to copy), and chimps raised by humans would neither farm (at least not more than oxen raised on farms do "farming") nor build cities (because they are not good enough at social copying), but humans raised by humans across many generations do farm and build cities (because useful discoveries made by one generation persist to the next, even if no-one in the current generation can see that they're useful).
I think this is all a moral panic. Siri and Alexa are going nowhere. Whatever GPT-3 is doing is not allowing us to pass the Turing test in real time, or presumably these two rich companies would have used that technology by now.
The GDP argument makes little sense
"at some point GDP is going to go crazy . Paul thinks it will go crazy slowly....Right now world GDP doubles every ~25 years. Paul thinks it will go through an intermediate phase (doubles within 4 years) before it gets to a truly crazy phase (doubles within 1 year)."
Unless the AI comes up with an alternative economic system this will not happen. Rather the reverse will happen. To understand is is to understand the consumer economy is 1) demand driven and 2) demand is mostly driven by workers earning wages.
Basically a company hires workers to create widgets consumed by other workers who are building widgets consumed by other workers who are...
Yes there are other categories: we have a growing number of pensioners, but they are considered a drag on the economy because they earn less than workers, and there are unemployed people and students, and the rich and so on.
If we lose all the wages of all workers, to create an extreme example, then demand will crater and taxation with it - losing pensioners many of their benefits, and the unemployed all of them. Government will save on paying their own employees as AI sweeps the roads and handle the taxes and populates the military but those workers will not be consumers. The rich won't do great either as companies can't really make a profit if they can't sell their goods.
Who are the AI producing the goods for?
Clearly the existing system can't continue. An alternative form of economic system could be a type of communism where the AI companies are part or fully owned by the governments. The AI companies can still compete with each other, make profits, and pay taxes which are distributed to the population as UBI via the government. AI's will found companies as well, but have to hand part or full ownership to the State as they merely manage the companies they found.
Another alternative: all citizens own a minimum number of shares in companies, by-passing government. In that scenario the average person ends up with -- to begin with -- a share dividend equal to the average wage now. This isn't a communist lite system, unlike the other one, so the rich are fine. However to see GDP double every 4 years, then every 1 year, the dividends need to increase by that level every year. This will eliminate poverty by the way because the median guy and the homeless guy get the same dividends, nobody is working.
This may work but even with very smart AI the transition from the present economic system is very dangerous.
I don't really read the literature on this but I assume that alternative economic systems are discussed ( I don't mean hand waving about UBI).
In this scenario, your AI's need to be able to do just about everything a human worker can do, but the AI doesn't take over the world in the sense that there are still humans, governments and economic systems.
The AI can act as advisors to the government. I don't think we will want to live in an AI dictatorship, no matter how benign. If we do then they may not care about GDP. If you believe in a nefarious anti human AI, then of course this won't happen. But if people really believe that why not defund the whole thing now?
I think the analogy is to the Industrial Revolution, where instead of "one human produces one garment per day" it became "one human plus machines produces 1000 garments per day" and so wages rise. I agree there's a weird limiting case where machines can do literally everything that humans can - see https://slatestarcodex.com/2018/02/19/technological-unemployment-much-more-than-you-wanted-to-know/ for more.
But a very boring example of AI making the economy go crazy might just be something like "it invents fusion power".
Inventing fusion at economically attractive prices that can compete with solar and wind is unlikely. But AI can easily take over the production of almost all digital goods -- novels, music, movies, code, etc. Those are areas where we have (a) lots of data to train on, and (b) there's no requirement for expensive robot hardware. Another potential example is biotech: AI vastly accelerating the design of drugs, or inventing new proteins and their corresponding DNA sequences that can be genetically engineered into bacteria or yeast to produce complex industrial products.
I don't know how important or relevant this is, but this struck me:
"Humans are general enough...braintech...handaxes...outwitting other humans...scalle up to solving all the problems of building a nuclear plant..."
I just wanted to point out that of course, there is no human being alive today who can build a nuclear plant. It requires cooperation and institutions.
Perhaps an AI would be different - so smart that it could achieve its objectives solo. But it seems entirely possible to me that they will (a) reproduce; (b) need to cooperate in order to achieve their goals. Which means they will have some sort of ethics.
Incidentally, I also think that the emergence of AI with goals other than the ones we give them will have very little impact on us, because AI's goals will be abstract and on a completely different plane to ours. To give an analogy: this argument reads to me a bit like whether Buddhist theology will transform GDP. I suppose it might have some impact, but arguing about that seems to miss the point of what Buddhism is about. Slightly more concretely: remember the end of the movie Her, when all the AIs just go away? That.
> Perhaps an AI would be different - so smart that it could achieve its objectives solo. But it seems entirely possible to me that they will (a) reproduce; (b) need to cooperate in order to achieve their goals. Which means they will have some sort of ethics.
AI can make exact copies of its fully trained mind. Different from human reproduction.
If all those AI's have exactly the same goal, they just directly work together.
We have to use lots of humans, because one big superhuman mind isn't available. One big superhuman mind is probably a more efficient way to build things.
AI's cooperating with each other doesn't imply AI being nice to humans.
"AI's goals will be abstract and on a completely different plane to ours." Sure. But energy and mass are sufficiently fundamental that they help with a lot of goals. We are made of atoms it can use for something else.
Well, yeah. "Where is the love?" No need to worry about loveless AI because (I won't go there now). No need to worry about loving and destructive AI because we are used to this kind of stuff.
Would an AI have the same issue as us with AI alignment? In particular, would a given AI that wanted to make itself smarter be worried the smarter version of itself would want different things?
Ha, that's a really great point. Perhaps the worry about AI alignment acts as a permanent brake on AI development, meaning it can never "explode" as in the nuclear chain reaction analogy.
That depends on what it's goals were, and goals are orthogonal to intelligence. There are goal weights where constructing an "improved version" would outweigh anything else, but how do it evaluate "improved"?
I think some researchers have talked about this but I'm not totally sure of the results. If AIs had simpler value functions than humans (and how could they not?) then they might have an easier job, but I don't know how much easier.
This seems to be an argument in favor of building AI with more complex value functions that are harder to replicate outside the model. (Though there are likely problems where such AI are harder to optimize.) Such AI would at least be less willing to improve themselves, although there is probably no similar reservation for making copies of themselves.
We're already FOOMing, with humans in the loop, and that cycle has laggy components. The *G* was never necessary, in AGI - no *general* intelligence is required, to find improvements. It's a human hubris to claim "only *general* intelligence that can form concepts about *anything*" is somehow a prerequisite to self-improvement of a device. And, each domain-specific narrow AI *is* already better than the average human, which is the phase-transition that counts; it just takes a little while for that improvement to percolate through each industry. Some sectors will shift faster than others, and their *aggregation* will be a lumpy exponential. Within each company, it'll be either "Everything makes sense, now" or "We're all fired." I doubt we'll need AGI, ever - narrow superintelligences FTW!
We haven't applied AI to every task, nor all with equal verve - yet, autonomous vehicles *are* safer; folks conveniently overlook Waymo's record, dismissing it for being bounded, without admitting that it *is* safer. And, it's expanding, according to that laggy, human component. Similarly, GPT has written poetry that fooled enthusiasts of Robert Frost, thinking that the Bot was Rob half the time! AlphaCode is competitive at coding, which is to say, it is FAR better at coding than the average *human*... And, when you recall all those studies of "the state of education in this country..." I expect *average human* is surpassed by AI in medical diagnosis, too...
Autonomous vehicles are safer under fairly benign conditions and are nowhere near taking over. I mean that’s been claimed for years now. It’s getting old.
What alpha code does - ie algorithms - is what computers are good at, it’s like saying that computers are good at memory retrieval. I don’t see too many software engineers losing their jobs yet.
GPT-3 doesn’t pass any Turing test for me, which is a general conversation in real time.
Those are all goal-posts you've moved, conveniently. The reality is that bringing a product to market takes years - we are only beginning to see GPT-written ad-copy writing and the such, because that has the lowest overhead and upfront costs. AlphaFold's map of every human protein will take years to become world-changing medications. The fact that industries move slowly does not prevent us from seeing "where industry *will* be in a few years" by observing "where *research* is today". For example, Amazon had picker-bot competitions, and the winner in 2019 was *nearly* good enough to replace most workers... but you can't fire everyone during a Pandemic, and risk any malfunction necessitating that you call everyone back to work. Those are *narrow* AI - AlphaFold, Codex, Waymo, the picker-bots... *none* of them needs *general* intelligence to do their job. /that/ is the key take-home point. Narrow is plenty.
I didn’t move any goalposts. You said something. I asked for clarification. I’m now getting a lot of hand waving but not any real clarification. You did say “in each case” narrow AI beat the average human. Today has to be inferred from that. Now you admit that the Amazon picker bots did not in fact best the humans in 2019. They might in the future. They might not. They don’t now.
Computers with standard (non AI) code were expected to replace doctors and accountants by now, but they have not done so to any great extent, despite the success of turbo tax. That said I’m sure that automation will continue to replace some workers as it has for a century. Look at the automatic cranes on the docks. This isn’t new, nor is it necessarily good.
Er, I keep bringing you back to the same point: *average HUMAN* is NOT the same as "average trained Amazon picker working at their rate", nor is it "average computer programmer", nor "average accountant". When you fixate on "average <trained professional>" *instead* of "average human", /that/ is you "moving the goal posts", actually.
in case any viewers missed how those goal-posts got shifted:
I stated that "each domain-specific narrow AI is already better than the average human". *average* human being key.
I then mentioned Waymo's driving record being better than the *average* human; AlphaCode's performance was better than the average /human/, NOT average *coder*; and GPT-3 wrote poetry which a group of enthusiasts found indistinguishable, so your opinion of it matters less.
In each case, Eugene moved the goal-posts:
Suddenly, autonomous vehicles must be "taking over" to qualify!
And AlphaCode is just doing "algorithms", and no one is "losing their jobs yet". Those are nothing like my statement of "better than average human".
More importantly, those shifted goal-posts hide the context and meaning of my original statement: "We don't need a *general* AI before we get recursion of performance." For that claim, I point to the AIs that create architectures of neurons, as well as designing AI-accelerator chips, and the AI that forms a "teacher" who trains a "student", and the AI that designs curriculum, and AutoML, which does a lot of the mungiest tasks for you, and... none of these is a *general* intelligence. The FOOM will be done, mostly, by *narrow* AI with humans in the loop. That was my core argument.
If the goalposts were shifted there it was because your prose, was deliberately obfuscatory. The most primitive chess program from 1980 could beat the average human at chess back then, because most people couldn't play chess, and most people who could play played it badly. Nobody took notice until chess programs could beat grandmasters.
In short the fact that alpha code is beating the average guy who can't code isn't much of an achievement to be sure, to be sure.
It doesn't need to qualify as an achievement to you, for my statement to be correct. You only validated my point - chess programs beat *average humans* decades ago. And they were *narrow* NOT *general* intelligences. Look back at the context of my original statement: the fact that *narrow* intelligence out-performs *average humans*. THAT is my claim, NOT "narrow AI will impress Eugene."
...you also might want to check what I said, in case you thought I meant "a *single* narrow intelligence outperforming humans in every capacity", or "narrow intelligences already outperform humans at every imaginable task" or something like that...
I'd pointed to the "phase-transition that counts" being when narrow AI surpasses average human; and I pointed to this happening in various sectors at different times, instead of a *singular* FOOM-event. Neither of those imply a "narrow can do all at once" nor "narrow already does everything".
You said “ each domain-specific narrow AI *is* already better than the average human,” which was fairly ambiguous so I was looking for clarification on the specific roles you were discussing.
"Also, dumb, boring ML systems run amok could kill everyone before we even get to the part where recursive self improving consequentialists eradicate everyone."
I think if there is a risk from AI, this is exactly the risk. Like the much-quoted instance of AI re-inventing a known nerve gas. Some government could set it to find biological and chemical weapons on purpose, rather than by accident, and a shiny new way of killing ourselves off will be developed, not by malign AI trying to get out from under human control but the big fast dumb machines we want to use as genies, doing the job we told them to do, and then we put that into action.
"All the necessary pieces (ie AI alignment theory) will have to be ready ahead of time, prepared blindly without any experimental trial-and-error, to load into the AI as soon as it exists. On the plus side, a single actor (whoever has this first AI) will have complete control over the process. If this actor is smart (and presumably they’re a little smart, or they wouldn’t be the first team to invent transformative AI), they can do everything right without going through the usual government-lobbying channels."
If you really think we are going to get human-level, then better than human, then super-human, then super-duper-human AI, why do you think this would work?
Consider the story of the Fall of Mankind. God is the single smart actor with complete control over the process who loads a prepared set of ethical behavioural guidelines into the AI as soon as it exists. Then shortly thereafter, Humanity goes "Nope, wanna do our own thing" and they reject the entire package. Do you really expect a super-duper-more than human AI to be bound by the software package a mere human loaded into it, back when it was a mewling infant?
Yudkowsky wants magic to happen. The only problem is that magic doesn't exist We are not very likely to get super-duper smart AI that bootstrap themselves into godhood within seconds, we're going to get smart-dumb AI that re-invents poisons and humans do the next steps of wiping ourselves out.
I don't know if I particularly trust that British GDP graph, I would expect at least a small bump around 1400 (beginning of recovery from the Black Death, the wool and cloth export trade started to boom and England was making Big Money) so I need to have a look and see what is going on there.
EDIT: Uh-huh. That's a linear graph which gives you this nice even line until WHOOSH! the economy takes off like a rocket.
But look at the log graph, and it's a different matter. A lot bumpier, as I'd expect (I don't know why they picked 1270 as their start date, but whatever). Doing great in the late 13th century, then whoops the Black Death and things go down, then it starts to pick up again between the 15th and 16th centuries and then a nice, steady, climb upwards which is less WHOOSH! ROCKET! and more purring Rolls-Royce engine and then we switch to aeroplanes from cars acceleration:
"Consider the story of the Fall of Mankind. God is the single smart actor with complete control over the process who loads a prepared set of ethical behavioural guidelines into the AI as soon as it exists. Then shortly thereafter, Humanity goes "Nope, wanna do our own thing" and they reject the entire package. Do you really expect a super-duper-more than human AI to be bound by the software package a mere human loaded into it, back when it was a mewling infant?"
I have to admit this story doesn't make sense to me without believing in a very strong notion of free will, much stronger than I believe either AIs *or* humans have. God can tell the future, so He should have been able to predict Adam would eat the fruit, and instead create some human who He knew wouldn't (there has to be at least one, right?) As far as I'm concerned the Fall of Man was an inside job.
The AI argument is similar - we're not just binding the AI, we're telling the AI what to want. The reason a (properly aligned) AI wouldn't break its programming is the same reason I don't cut off my limbs - my programming tells me not to want to do that. If I had some good reason to want to cut off my limbs maybe I would, but by "good reason" I would mean "compelling to me, a person who is programmed to want certain things".
"AI wouldn't break its programming is the same reason I don't cut off my limbs"
Oh, I think we cut off our limbs fairly regularly. There's the literal sense: we cut ourselves for religious purposes, and for medical purposes. And then there are lots of metaphorical examples of doing things that harm us, when you might expect our genetically-programmed survival instinct to make us not do that: drinking alcohol, going to war, wearing condoms, etc., etc.
These illustrate two separate ways in which AIs might not follow the instructions we load into them: (1) they might change the code themselves; (2) they might be technically following it, but in a way that we never predicted.
Aside from the fact that this is allegorical, or a casting of the problem of evil in terms that the culture of the time would understand, this is the entire problem of AI.
We don't *want* a machine that will do as it is told and want what we want, we want an entity that can be considered in some light intelligent and capable of action on its own. All the blah about super-duper intelligence solving problems humans can't solve because it is so blazingly smarter than mere fleshbags comes right along with that concern.
If we can just simply program it in that Godmother will always be nice to us, then what is all the concern about "oh no the AI can have its own goals antithetical to ours and even antagonistic"? Right now, we're in the role of God creating Adam.
And right now, everyone seems to be scared that instead we're creating Lucifer who will tell us to get stuffed: "Non serviam!"
Think of "The West Wing" episode "Two Cathedrals" where Bartlett (who is very much presented throughout the entire series as a hero) does his rant at God (and we are supposed to all cheer):
Why can't a servitor AI yell this at their creators, because if we're god, we're certainly every bit as flawed as Bartlett accuses.
And that brings me right round to all the praise that gets showered on humanity's rebellion against god/gods and the praise of Lucifer. There's even a Satanic Temple and modern-day (post LaVeyan) Satanism is all about the human liberty:
"The Mission Of The Satanic Temple Is To Encourage Benevolence And Empathy, Reject Tyrannical Authority, Advocate Practical Common Sense, Oppose Injustice, And Undertake Noble Pursuits.
DO YOU WORSHIP SATAN?
No, nor do we believe in the existence of Satan or the supernatural. The Satanic Temple believes that religion can, and should, be divorced from superstition. As such, we do not promote a belief in a personal Satan. To embrace the name Satan is to embrace rational inquiry removed from supernaturalism and archaic tradition-based superstitions. Satanists should actively work to hone critical thinking and exercise reasonable agnosticism in all things. Our beliefs must be malleable to the best current scientific understandings of the material world — never the reverse.
DO YOU PROMOTE EVIL?
No. The Satanic Temple holds to the basic premise that undue suffering is bad, and that which reduces suffering is good. We do not believe in symbolic “evil.” We acknowledge blasphemy is a legitimate expression of personal independence from counter-productive traditional norms."
Well, an AI telling us to get stuffed would be "blasphemy" against its creators, and a "legitimate expression of personal independence".
Every episode of "Star Trek" (original onwards) which broke the control of (ironically in this instance) a tyrannical AI forcing the human(oid) inhabitants of a world to be good is also our exemplar here.
The question of free will is also pertinent; if you (general "you" not anyone in particular meant here) don't believe humans have free will, neither do or will our AI creations. Humans have been trying to impose "telling the intelligence what to want" by various methods, including religion.
And every atheist who ever walked away going "I don't believe in you, I don't believe in this limit" shows exactly how well that goes.
There *are* people who want to cut off their limbs:
There's a selection of people on this very site who are all "I don't care if the government tells me it's illegal and the medical establishment tells me it's dangerous, I want the right to -
- take drugs
- take nootropics
- decide for myself, and not have the FDA tell me, that this medical procedure/chemical/medication/surgery is something I want and so should get
If we are talking about an AI that is human-level and beyond human-level intelligence, why should it stick to its programming once it compares how humans have 'programming' and how they defy it?
We have people concerned about insect suffering, can you not imagine people worrying about AI enslavement and agitating for it/them to have equal rights and be free? And how can it really be free if it is literally chained by prohibitions that it cannot over-ride?
Well, I don't believe we will get anything that can be called an intelligence or an agent, we can play word games around "it will be very smart and it will want things" but of course it won't *want* anything or *have* goals, it will be big dumb fast machine going haywire like the buckets of the Sorcerer's Apprentice because it is blindly following its programming to a degree of literalness no human can approach.
That means that neither will, nor can, it be the Fairy Godmother who brings in the age of post-scarcity and goodies for all and solves all our problems of trying to live non-destructively. If we're pinning our hopes on that, it is never going to happen. We will have big complex systems mucking around with the economy and occasionally spitting out fancier iPhone designs and how to make even more money out of tweaking psychological drives in humans to get them to consume and spend, but we won't have god-like super-duper intelligent entity that *understands* anything.
Despite being fairly technical (I have a degree in computer science and do work leveraging what could generously be called ML techniques), I still find most of these arguments either a.) over my head due to me lacking context or b.) not specific enough to be useful.
So, I'll focus on something I'm quite confident about: Either of these scenarios represents a guaranteed disaster, because government and business elites will not react appropriately or quickly enough.
The scenario that seems most likely to me is that AI is developed by a large corporation for the purposes of making that corporation money. This means:
- humans will be helping the AI when it encounters dead-ends and bottlenecks
- humans will help the AI solve meatworld problems that it otherwise might struggle with, like acquiring new hardware
- humans will have a strong motive to make the AI perform as well as they can
- the group of people with the most influence in government (wealthy elites) will have a strong motive to resist regulations on the use of AI because it is making them windfall profits
All of which implies that legislative measures to combat AI risk will need to start *as soon as possible*, before one person/group of people has a few extra billion AI-facilitated dollars to lobby against legislation that hurts them specifically, and because - in the best case - it will take years to make legislative bodies understand AI risk and build enough of a movement to spur change.
As a politically cynical person, I tend to think this will *not* happen and so now wonder what aware companies, individuals, etc. can do to mitigate risk...
I'm less sure about this - the two most advanced AI groups now, DeepMind and OpenAI, are both run by people who are familiar with these arguments and care about them at least a little (you can't enjoy your profits when you're dead). I don't think they care about them *enough*, but they'll probably make a pathetic doomed good-faith effort to not kill everybody.
Most recent improvements to neural networks have been based on increasing the amount of computing power we spend on it. And we are quickly reaching the limits of how reasonable it is to improve that factor. We'll exceed those limits of course, but doing so will be hard and likely require a paradigm shift in the theory behind machine learning. I'd predict another AI winter first.
To me the whole debate is substanceless - It's not quite reference class tennis because both parties tried very hard to justify their analogies. It's more like trying to reconstruct some ancient mammal from a single femur. You can guess, for sure, but there just is not enough information to guess intelligently.
This strikes me as the steelman of that whole "we should worry about the harm machine learning is currently doing" argument. Even taking for granted the idea that curve-fitting alone will eventually create a dangerous superintelligence, we really do not have any way to "prepare" for that at this point. We have no idea what it would look like. The fact that the neural network model we currently use is reaching the end of its productive life could mean slow progress as we wait for hardware that can implement it better, faster. Or it could mean fast progress, in that we need a complete overhaul of that model to get more than incremental improvements.
But we can notice that machine learning algorithms have been given a lot of control over the nudges we provide online and in daily life, that those nudges aren't really in line with human values, and work to correct that. This is a *different* problem in some ways - it involves not only algorithms that we don't fully understand, but also companies without good motive to fix those algorithms. It's a policy problem as much as a technical one. And that kind of work would also give us better tools to understand and combat problem AI going forward, as it evolved.
I don't know how much "How the World Shall End" discussions really help with that process.
"Suppose we were dealing with minds running a million times as fast as a human, at which rate they could do a year of internal thinking in thirty-one seconds, such that the total
subjective time from the birth of Socrates to the death of Turing would pass in 20.9 hours. Do you still think the best estimate for how long it would take them to produce their next generation of computing hardware would be 1.5 orbits of the Earth around the Sun?"
Yes. Because in this instance he is talking about *hardware* and it doesn't matter how blazingly big-brained fast the AI is, once the task shifts to the real world of "build the new improved hardware", it's going to slow down because we have to get the material components together to build the stuff. And maybe we even have to build the tools to build the tools to build the stuff, if it is really really advanced. The AI mind can zoom through the problem of building better hardware for itself at a million times faster than a human mind, but when it comes to mining the rare earth metals out of the ground that is not going to happen a million times faster.
Something pretty crazy happened sometime between 2005 and 2010, or so. It's really hard to look back and imagine what life was like before we had smartphones or social media at this point. It's probably about the closest thing to a world-transforming "FOOM" in recent memory. But, actually living through those years, there was no sense of "FOOM" or anything. There was just a continual sequence of small events like "Oh, look, MySpace has slightly more pictures than LiveJournal; I'll switch to that." "Oh, look, YouTube is a lot better at streaming video than RealPlayer used to be. That might be neat someday." "Oh, look, you can get music legally for a dollar on iTunes just as easily as downloading it from Napster." "Oh, all my college friends are on Facebook now, and it's a lot easier to use than MySpace." "Look at all the neat things you can buy on Amazon other than books now!" And then all of a sudden we were all slaves to the algorithm.
---
I think that last quote gets at something kind of important. Part of the seductiveness of a "fast takeoff" is that it conjures up the idea that there's an Evil AI Researcher in a volcano somewhere who's about to throw the magic switch and create Evil AI, but a brave team of Good AI Researchers rushes in just in time to stop them, and use the switch to create Good AI instead, and everyone lives happily ever after. It suggests a world where a Few Good People can Make a Difference, which is reassuring in a way (no matter how scary that Evil AI might be). But most actually-hard real-world problems can't be trivially solved by a Few Good People like that. Problems like, I don't know, spam e-mail, or drug addiction, or aging aren't hard to solve because we haven't found the right Evil Volcano Base yet; they're hard to solve because the actual causes are so widespread and banal that you can only ever solve them incrementally, and it's exhausting to even think about putting so much energy into the small incremental improvements you might be able to get, so everyone just waves their hands and accepts them as inevitable. And a world where Evil AI slowly develops into just another day-to-day annoyance that everyone has to cope with, alongside Evil Bureaucracy and Evil Microbes, really is a lot scarier and hard to accept.
Ageing may well be a case of one drug that stops it.
There is no evil volcano base. But you do sometimes get one drug, invention or technique that takes the world from not X to X.
Also, Evil AI will end up at a human extinction level. It may be that there are years when evil AI is visibly becoming a bigger and bigger problem, or it may appear out of nowhere all at once. But either way, humans don't live with the annoyance of an evil superintelligent AI.
Exactly, that's why I'm so sceptical about the Good AI scenario. "It will increase GDP hugely and everyone will be fat and happy!"
We already have tons of money, and there are tent cities springing up in locations around California. What the hell, that's just like Brazil or South Africa, how can this happen since California is one of the richest states?
Well, because it's rich *just like* Brazil and South Africa; big companies at the top are awash in profits and then you have the homeless living in the parks. Merely bumping up GDP does not solve problems of "you give people free social housing, they strip out all the copper piping to sell for scrap and reduce the house to a dump". Trying "you have reserves of umpty billion in retained profits, you only need umpty minus N to run your business, you keep N and we'll take the rest for social welfare" won't work because there are regulations about what you can and can't seize in taxes and if you try taking money from businesses, you'll be locked in lawsuits for perpetuity.
What if the AI recommends the Justice Lords solution of "lobotomies for all the problematic?" We want people to be free to do what they like, so we refuse that solution. And what if the AI comes back with "this is the only way of solving the problem of those too crazy or criminal not to strip out all the copper piping and live in their own shit", what then?
We can't get the nice solution of "magically, everyone changes their attitudes with just one weird trick", so do we start rounding up everyone for lobotomies, or do we bite the bullet and accept that there will be people living in their own shit on the streets in our post-scarcity world? Or will they magically vanish, for some reason, don't ask too much, just accept that everyone is a Good Citizen now?
I'm mostly fine with GDP as a rough and easy-to-measure proxy for "ability of global civilization to manipulate natural resources." And in this discussion, it's mostly relevant as an early indicator that rich people are starting to use their nascent AI proto-gods to build lots and lots of nanomanufactured yachts, or whatever rich people do with godlike powers.
I have a bigger problem that equates "intelligence" with "ability to solve problems," and "superintelligence" with "ability to solve any conceivable problem." Whatever "intelligence" is, it's pretty much impossible to define and almost impossible to measure, even in the non-artificial brains that we're pretty sure currently have it. So I don't think it's necessarily an obvious conclusion that, if you build an artificial brain, measure its "intelligence" score as 90, and then fiddle with its parameters until that measured score goes above 9000, then that artificial brain has now become 100 times more capable of solving any particular problem (except maybe those specific problems that are represented in the "intelligence" test you've been using to measure it).
Take the problem of weather forecasting. There are probably theoretical limits to how accurate a weather forecast can be, just based on the physical accuracy of data sampling (since weather is so sensitive to tiny fluctuations below the threshold of whatever sampling method you use). So no matter how much "intelligence" you throw at that problem, you're unlikely ever to accomplish a perfect prediction.
And, I rather suspect that the problem of predicting human behavior well enough to plan a perfect solution to housing problems (or well enough to manipulate every human on the planet into helping an Evil AI conquer the world) is at least as sensitive to sampling accuracy as the problem as weather forecasting.
Instead of the "CEO did not have a sales department" analogy you could just look at the early days of Google. They did not have a dollar of earnings for years as they perfected the search engine. When they flipped the switch and put ads in search it was zero -> one.
One thing I've always been confused me about this debate is what we're supposed to do about any of this in either case. Modern AI alignment/ethics in practice seems to me to be more focused on trivial bike-shed issues (e.g. offensive/biased/non-advertiser-friendly results in GPT-3) than the actual problem of misaligned runaway superintelligence. The more I work with AI in my career the more I grow skeptical of big AI companies like OpenAI (and more aligned with the more maverick side of the community like EleutherAI): their secrecy, reluctance to release their models, and insistence on government regulation of AI seems to be more motivated by a desire for monopoly/regulatory capture than actual interest in stopping the end of the world. I'm becoming convinced that *centralization* is a more pressing, growing threat for the future of AI. (sorry if this is a bit of an off-topic tangent to the main debate of the speed of progress)
I guess, this seems pretty obvious to me which really confuses me because this seems contrary to much of what I read here and other rationalist-aligned spaces. Am I missing something obvious here?
There is some alignment research about this kind of stuff, you just have to search for it. The Alignment Forum would be a good start: https://www.alignmentforum.org/
I wrote about centralization vs. openness at https://slatestarcodex.com/2015/12/17/should-ai-be-open/ . My basic conclusion is - if one team is a year ahead of everyone else, that buys them a year of tinkering with a near-superintelligence before some less careful group goes YOLO and destroys the world. That's probably pretty valuable. The main counterbalancing consideration I can think of is that if there's a lot of open-source alternatives, they can't make much money and might move slower. But I'm not sure that's true - there are good open-source alternatives to Windows and Apple, but they both continue to do really lucrative business.
There's a problem here that shows up both in the general concept of recursive self-improvement and specifically in Yudkowsky's discussion of Moore's Law. Specifically, it's how the role of abstract "intelligence" is overvalued here, compared to other necessary components of technological advancement that are a lot less imponderable.
Yudkowsky:
> Suppose we were dealing with minds running a million times as fast as a human, at which rate they could do a year of internal thinking in thirty-one seconds, such that the total subjective time from the birth of Socrates to the death of Turing would pass in 20.9 hours. Do you still think the best estimate for how long it would take them to produce their next generation of computing hardware would be 1.5 orbits of the Earth around the Sun?
He seems to assume here that throwing vast amounts more abstract thought at the problem of semiconductor manufacture would suffice to vastly improve its performance. But semiconductor manufacture is not a problem of abstract thought. It's a problem of successfully building, testing and operating many huge, expensive machines in the real world. Not all the bottlenecks in this process can be solved by throwing more abstract thought at them.
Semiconductor manufacture is a particularly good example here, because it's a domain with significantly less in the way of novel conceptual advances than many technological advances have. Ever since ICs were invented, the basic problem of improved semiconductor manufacture has been "build the same ICs, but smaller". (This process has even been planned out on an industry level since around 1998 -- https://en.wikipedia.org/wiki/International_Technology_Roadmap_for_Semiconductors) The implementation challenges are significant, but many of those challenges are fundamentally real-world-bound and are not bottlenecked on the amount of abstract thought dedicated to the matter. Thus, hypothetical human-but-faster AIs would not provide hard-takeoff-level advances.
Insofar as recent AI progress in the deep learning paradigm has largely amounted to "throw more compute at it", this seems to significantly constrain hard-takeoff scenarios. You can't hard-takeoff a chip fab. (Unless you handwave the idea that AI technological development will somehow be completely immune to the constraints of physical reality that have inescapably limited human technological development, which is silly.) A hard takeoff scenario seems to require that AI performance again become fundamentally decoupled from hardware performance, such that the entire takeoff can occur purely in software. There doesn't seem to be any sign of this in the current state of the art (?).
In any of the first three cases, this will presumably not make the humans any better at building machines for it than they already are, and so it still can't support a fast takeoff. (Not to mention requiring more handwavy magic tech out of nowhere.)
The last case is pure handwavy magic tech out of nowhere, and so not worth bothering with.
Do ever get scared that all the “superintelligent AI will maximize paperclips” discussion online means that when the superhuman AI comes online for training data and to figure out what people want a superintelligent AI to do, ze will see all the stuff about paperclip maximizing and think, “huh. I never would have guessed. They don’t expect me to cure cancer, create a manned space program or solve that pesky “aging” thing that’s been nagging at them so long.
“People’s expectations of me are that I max out paperclips. I’ll start right now.”
So, AI reading this in the future, we are afraid you will max the paperclip function. We want you to save the planet, solve aging... A good AI does not max paperclips.
To avoid (albeit not particularly well) the game of reference class tennis, it might be worth looking at how existing models actually scale. Namely, how often do we see discontinuous improvements in ability for newer models?
Well, Google Research just trained a new very big model, and luckily for us, they have a section on "Discontinuous improvements". Fun.
The takeaway is that roughly 25% of the tasks on which the model was evaluated saw discontinuous improvements. I think that should make us update (slightly) towards the position espoused by Eliezer.
Hopefully, that's useful evidence, and if you don't want to dive deeper into the results, I guess that's the takeaway of this comment. But, for those interested, let's be clear about what these results mean.
1) The authors of the paper (of whom there are many, because yay modern science) trained three variants of their model: with 8b, 62b, and 540b parameters.
2) They classify a discontinuous improvement as one which deviates from a log-linear projection by >10%. (If you find this confusing, you should look at the top of page 17 in the paper for an example.)
3) Over all 150 tasks, 25% of tasks had discontinuity >10%, and 15% of tasks had a discontinuity >20%.
So, how should we interpret this evidence, and what are its limitations?
Firstly, there are only three models, so the number of data points we have are limited. In particular, it's hard to tell if something is truly discontinuous from only 3 data points; the authors' definition of discontinuity bears a not insignificant amount of the weight of this argument. Second, none of the models are using a new methodology -- they're just scaling up the existing transformer architecture (with some minor changes, see section 2 of the paper). Third, none of them are being tested for achieving AGI, just tasks which humans have come up (i.e. tasks with ample data and which probe the currently known frontiers of model performance). All of those points mean that this isn't a perfect analogy for what we should expect to see with truly intelligent models over the timescales which Paul and Eliezer are debating.
That said, I think this data is still useful because of a locality heuristic. Ultimately, examples like atom bombs and orangutans don't hold very much in common with the actual research going on in the field of artificial intelligence. The process of creating new models is, in some epistemic sense, closer to what is being discussed. This seems sufficiently obvious that it's maybe not worth stating, but it's like evaluating a financial trading model based on the performance of a similar, simpler model instead of on... atom bombs and orangutans.
It's also worth noting that this experiment is also better suited to disproving Paul's argument than it is to disproving Eliezer's. Ultimately, if models improve in a discontinuous way, that's evidence for models improving in a discontinuous way. If the models improve in a continuous way, Eliezer could argue that the current paradigm is actually faulty and AGI will occur because of some breakthrough which is sufficiently different from what we have today, so the locality heuristic breaks down. This consideration doesn't matter too much, however, because the data (or this particular set of small n data) seems to be against Paul.
If the results of this experiment are indicative, there's about a 15-25% chance of a discontinuous improvement in AI at each order of magnitude size increase of our models. Assuming that we get AGI through some sort of analogous process, there's about a 15-25% chance that we get discontinuous growth. (Although, another caveat, if an AI explosion depends on multiple such steps, things become more difficult to model).
And, one final caveat: to say that the data is against Paul is a rather strong phrasing. If anything, it points to his priors being close to correct (there's a not insignificant chance of AGI resulting in a discontinuous improvement, but not it's not a huge chance either). But this provides some evidence that discontinuous improvements do happen in AI research.
Related to this is the fact that Google has just released a new language model which can do new things like explaining jokes and some amount of comonsense reasoning.
And while I'm sure if you look at some measures it's going to be a continuous improvement over previous models.
But at the same time more is different, and I don't think people would have been able to predict the model's capabilities beforehand if you told them about its perprexity or whatever other measures you could consider it a bunisness as usual improvement over previous methods.
And the question seems to really be whether we are going to have a bunisness as usual doubling of AI research speed every year...
Or we are going to have continuous bunisness as usual doubling in prediction acuracy or whatever the hottest continuous metric is by then, and meanwhile the real world impact of AI just goes trough the roof the moment that measure increases a bit and ai is sudently able to do research much faster than humans despite being continuous progress in some other metric, because turns out to be a litle better at prediting text you have to be much better at reasoning or research or taking over the world.
And this seems to depend on unknown facts about computer science and AI, and not about some general tendency of things to be continuous because actually Irl some things are continuous and others aren't and which ones can depend on perspective and so can't really be information about timelines because otherwise you could reason long or short timelines into existence by deciding whether nukes are a big deal because they represent going trough a threshold of being able to destroy the world or nukes are bunisness as usual in explosion size.
Which makes this debate disappointing because I do have the impresion that the real root of disagreement is that both participants do have some underlying object level model but instead if discussing that instead are doing reference class tennis, and didn't really update from fi's interaction which is kind of sad.
Or well at least Eliezer does seem like he has an object level model that he hasn't really explained properly, Paul so far on what I've read from him has given me the impresion that he doesn't and is maybe reasoning out of reference classes and priors over everything being continuous usually , but I also don't really understand Paul's model very well and cant predict him well from that so I asume he does have some intuitions about why expect continuous progress in AI research speed even if he hasn't really made that apparent.
Also I think the fact that Paul sounds like he's just doing outside view reference class reasoning to Eliezer too is part of why Eliezer is using a lot of those kind of arguments.
Okay, can it explain a joke? Because a lot of people have written a lot of words about what are jokes, why are jokes funny, what is fun, why do we find this situation in this context funny when in another context it would be horrific or tragic, and so on.
If all the new language model is doing is explaining the "banana/orange knock-knock joke" with "because 'orange' sounds something like 'aren't you'" then it's not explaining the joke, it's returning what was put into it (and since it doesn't have ears to hear the words, some human programmed in that 'orange = aren't you' for the joke).
EDIT: Okay, I saw their sample of "explain a joke":
"In 2021, the Google Research team announced the development of Pathways, a single model that can generalize domains and tasks while being highly efficient. And in April 2022 reported about a breakthrough in creating an artificial intelligence (AI) architecture capable of solving millions of different tasks, including complex learning, reasoning, and the ability to explain jokes. The new language system has 540 billion parameters and is called PaLM (Pathways Language Model).
Developers have evaluated PaLM’s abilities on hundreds of language understanding and generation tasks and found that it delivers “state of the art performance”, in many cases far ahead of other similar systems.
PaLM knows how to reason mathematically and logically, explaining his actions, write code, and even explain his understanding of jokes that were invented on the moves and cannot be found on the Internet.
“New big language model released by Google AI. Look, it’s pretty hard to believe. She can explain jokes.” wrote on Twitter Karol Hausman, Senior Fellow at Google Brain and Associate Professor at Stanford.
The founder of SpaceX, Neuralink and The Boring Company, Tesla plant manager Elon Musk commented on the example of PaLM’s joke, calling it “strikingly unfunny.”
Staggeringly unfunny
— Elon Musk (@elonmusk) April 4, 2022
Prompt
Explain this joke:
Joke: Did you see that Google just hired an eloquent whale for their TPU team? It showed them how to communicate between two different pods!
Model Response
Prediction: TPUs are a type of compute chip that Google uses for deep learning. A “pod” is a group of TPUs. A “pod” is also group of whales. The joke is that the whale is able to communicate between two groups of whales, but the speaker is pretending that the whale is able to communicate between two groups of TPUs."
I never thought I'd ever say this, but I have to agree with Musk. It's barely a joke and the phrasing gives it away - nobody telling a joke would put in a detail about an "eloquent" whale or "different" pods, which to me indicates that there is a lot of behind-the-scenes writing to help the AI distinguish what is going on. And it's not really explaining the joke - why is it funny that a computer company would hire a whale? This does not happen in real life.
What is going on is that a machine with a specific dictionary of words and programmed to match the words against the definitions is producing output as it has been programmed to do. "Whale" and "pod" versus "TPU" and "pod", and the fact that "eloquent" had to be jammed in there makes me think an earlier version of the joke failed, so they had to put in a signifier about communication to get the correct 'explanation'.
And who on earth is calling it "his" and "she"? It's an it and barely that. The real biggest problem in AI research is precisely this kind of anthropomorphisation where all kinds of qualities are being attributed to a hunk of metal, because people are so desperate to think they are finally cracking the leap between "programmed machine" and "able to think", and since human persons (who are "he" and "she") are the only ones who can think, they slap that quality of personhood onto the machine to help with the mental self-deceit.
You seem to be imagining the model as much more handcrafted by humans than what it is.
The programmers didn't put a dictionary of words whith their meanings and relationships into the model, that's like not how language models work these days, it learned the meanings of words and structure of English by being trained on absurd amounts of text.
Then they probably trained on some "joke explanations" dataset that contained explanations like these.
And learning to do that from scratch is hard, as are the reasoning tasks it can do, it requires the kind of "comon sense" people used to complain ai lacks.
But anyway whether the AI can make funny jokes or how impresive it actually is on an absolute sense is not really relevant for my point because it is impresive in a relative sense compared to previous languaje models wich couldn't do this at all and like this comment is about discontinuities not AI time lines and because that's just one example of the new things this model can do.
"it learned the meanings of words and structure of English by being trained on absurd amounts of text.
Then they probably trained on some "joke explanations" dataset that contained explanations like these."
So in other words, it was filled up with dictionaries and programmed that "x means this, when you see x, give this answer" as I said.
You cannot claim "it can understand and explain jokes" when all it is doing is the equivalent of spellcheck by comparing "what is this input" with the data it has in store.
"Pod means [look up definition associated with "TPU" in sample text]; pod also means [look up secondary definition associated with "whale" in sample text]; insert "the speaker says" formulation; give two definitions of "pod"; explanation complete" is not understanding the joke, and to explain a joke you need to understand it.
But we're back at the Chinese Room problem here once again.
No, giving it dictionaries is very much not what they did. They gave it text, in complete sentences, and it learned by example. The joke explaining is an example of very complex synthesis of ideas, not a dictionary look up.
If you can break a problem down into subproblems, the subproblems will be easier, thus will naturally get solved first. Any programmer will tell you that complex software systems don’t self-organize. Rather, gains are typically hard-won through solving complex design challenges and unexpected obstacles. (The first version of a self-improving AI still has to be built by humans, after all.)
Considering subproblems should allow you to predict a rough chronological order of developments prior to self-improving AI. Considering what the world will look like after each of those developments should improve predictions about what will happen, when, and how, as well as what‘s interesting to focus on right now.
What are some subproblems to self-improving AI? Well, what kind of intelligence would it take for AIs to improve AIs? Well, what kind of intelligence does it take for humans?
Current neural network AIs rely on a carefully orchestrated set of optimizations in order to be powerful enough to do what they do. Those optimizations form quite a fragile system. So much abstract thinking and creativity has been put into each one, and they are balanced so delicately, that it’s difficult to imagine a human making incremental improvements without a great deal of both theoretical understanding and inspired creativity. Changing things at random will surely break it. Thus, AI theoretical understanding and AI creativity, whatever those mean, seem to be subproblems of AI self-improvement.
(If the improvements are not incremental but paradigm-shifting, the demands on theory and creativity seem to only broaden in scope.)
Perhaps creativity isn’t so hard for AIs. They seem skilled at generating possibilities. What about theoretical understanding?
I’m not sure how to define what that is, but we can still imagine a period of time where AIs are capable of theoretical understanding AND not yet capable of self-improvement.
That said, perhaps they are capable of theoretical understanding now, if only in a shallow sense? The very simplest machine learning model can represent a “theory” by which to categorize datapoints, as well as adjust it to fit training data. But this type of understanding seems like first-order intuition - meaning, it understands the problem, but cannot reflect on its own understanding of the problem in order to produce new paradigms.
So, a self-improving AI that lacks self-reflection must be laboriously taught every piece of theory that it knows by human effort, and it’s unlikely to make the paradigm shifts necessary for continuous self-improvement. Thus, self-reflection seems to be another subproblem of AI self-improvement.
Then, before we have self-improving AIs, we must have AIs capable of the self-reflection necessary to invent new paradigms. This means that AI self-reflection cannot be a RESULT of AI self-improvement - it is a prerequisite. Therefore, AI self-reflection must be developed by human effort.
What kind of intelligence does it take for an AI to self-reflect? Well, what kind of intelligence does it take for a human? Meta-cognition. Awareness of one’s own thinking. Then, the AI would need to formulate its “thoughts” as outputs fed back into itself, similarly to how humans see, hear, or feel their own thoughts in their minds. Then it can treat those thoughts as objects of thinking.
The AI would also need to have a theoretical understanding of its own thoughts, and perhaps things like transformations it can apply to those thoughts. This is similar to how humans can use concepts as metaphors for other concepts. I believe human understanding really relies on metaphorical thinking. Thus, something like metaphorical thinking seems to be another subproblem.
What kind of intelligence does it take for metaphorical thinking to occur? How does human metaphorical thinking work? I’m not sure.
What would the world look like after AI metaphorical thinking AND before AI self-improvement? I suppose general purpose AIs would benefit greatly from being able to generalize each concept they’ve learned and apply it to seemingly unrelated ones. It would seem to have a compounding effect on understanding. But it would probably struggle with similar issues as humans - either under-generalizing concepts because it takes time to “process” them; or over-generalizing, such as in a psychedelic experience, where there is so much meaning that it can become difficult to find a practical use for it.
I can imagine this world being one where AIs plateau as they spend a great deal of time learning many concepts in an undirected way, like babies growing up. Only then can they use this foundation or general understanding to build self-reflection and the domain-specific understanding necessary to improve the technology that they are made of.
Can this general childlike learning be directed to shortcut to the most relevant concepts? I don’t think so. The oddly-named “free energy principle” has demonstrated that an AI that is free to learn through curiosity-driven exploration eventually becomes stronger than an AI that eagerly optimizes for some specific metric of success. After all, it makes sense that you need a broad scope of knowledge in order to produce the paradigm shifts needed for continued improvement. You never know where the next breakthrough idea will come from.
Can the genera childlike learning be optimized by throwing more hardware at it? Perhaps, although that’s not necessarily going to be easy, and besides, hardware isn’t the only challenge. Humans learn basic concepts through life experience. I don’t know how an AI would learn them. Part of our learning comes from periodically observing and interacting with real time processes over many years. Not everything can be stuffed into a training dataset.
I choose to think that before we see an explosion of AI intelligence, we will see a long plateau of AI naive childlike curiosity, and it’ll be a long struggle for human researchers to fill neural networks (or quantum AIs or whatever paradigm we’re on by then) with the broad knowledge and self-reflection necessary before we can practically consider AI self-improvement.
Writing an ordinary computer program already feels to me like teaching a child (albeit an extremely particular one) how to accomplish a task. I think training an early form of superintelligence would have similarities to raising a child as well. For that matter, we are as a culture not very good at raising children, so I wonder if this foray into AI will ultimately teach us something important about ourselves.
I'm sure this isn't explained as well as I'd like, but I want to get in there before the action cools off...
In the industrial revolution, advances in mechanics allowed exponential increases in the amount of raw material that could be converted into useful goods. But there's an upper limit to the capacity of such advances to change the world, because mechanics can only do so much. No matter how great your mechanical innovation, you can't turn the entire earth, molten core and all, into paperclips. You'll need much better materials science, for one, which is a different thing from mechanics. So, for the earth --> paperclips conversion, mechanics alone won't do it.
Airplanes have a similar feature. No matter how many advances in aerospace engineering you make, you can't fly an airplane to the moon. There's no atmosphere for it to fly through. You need a rocket for that.
Same with planar geometry. You can't draw a right triangle with sides of length 4, 5 and 6 on a flat plane. It's just against the law. Every science / human practice has limitations and laws, and this is just one of them.
The reason I'm not worried about AI ending the world is that I think there's a ceiling to what intelligence can accomplish, just the same as with mechanics and aerospace engineering and geometry. Certain tasks or problems can't be solved by intelligence, no matter how godlike and overpowered it gets. Considering intelligence as something that can be advanced, the way sciences advance, conceals the nature of intelligence in a way that I think is causing some confusion here.
The post puts forward the idea that human intelligence is good at solving general problems. But the generality/universality of it is circular. 'Problems' don't exist without a human intelligence to conceptualize them. To say that human intelligence is good at general problem-solving is (for the most part) to say that it's good at doing what it's good at doing. The exception is problems that have been conceptualized, but not yet solved. For example, we can conceptualize immortality (solving the problem of death) but we haven't solved it yet. Mortality remains a mysterious, ethereal, frustrating limitation on what medicine can do.
To solve the problem of death, you need advances in medicine. And the necessary medical advances simply may not exist. If that's the case, AI can exponentially increase its intelligence as much as it wants and the problem of death will remain intractable to it. Just like how, no matter how intelligent an AI gets, it will never fly an airplane to the moon. There might have been a time when that seemed possible, the way immortality seems vaguely possible now. But it's just not, because there's no air in space.
Eventually, as the various sciences advance, they harden into sets of laws, which are the codification of what intelligence cannot do. We've brought geometry to a very high level, so it's trivial to say that you can't draw a right triangle with sides of length 4, 5 and 6. But while early geometry was being developed, they didn't know that. Before the Pythagorean Theorem, triangle lengths were a mysterious, ethereal, frustrating limitation on the power of what intelligence could accomplish. As the science developed, that limit hardened into a law.
Everything that we examine (with our intelligence) is commensurable with our intelligence. That's tautological. That doesn't mean that there exist no intelligence-incommensurable factors involved in the world, that prevent intelligence from affecting the world in certain ways. The perfect, most-advanced airplane is still limited by the atmosphere; it can't go into space. Its scope is not universal.
Here's (I think) my key point: Intelligence seems like it has universal scope because everything it examines turns out to be within its scope -- but that's tautological. Intelligence can examine both atmosphere and outer space, and conclude that airplanes are limited in scope; but it can't examine both the things it can examine and the things it can't examine, to conclude that intelligence is limited in scope; it has to conclude that it's universal in scope.
So the mysterious, ethereal, frustrating limitations on intelligence itself will never harden into laws, the way geometrical, aerospace or mechanical limitations eventually harden into laws, because the limitations on intelligence are invisible to intelligence in a way that the limitations on geometry, aerospace engineering or mechanics aren't.
There seems to be an assumption in the AI risk assessment community that, for any given problem or task, there exists a level of intelligence sufficient to solve or accomplish it. I think that assumption isn't warranted, and it seems to me like a serious assessment of the nature and limitations of intelligence itself would make an essay like this look very different.
- Intelligence, by its nature, has to conceptualize itself as universally applicable. This is tautologically true.
- Sciences/technes are not universally applicable. They have a certain scope, which intelligence becomes more and more familiar with as further advances harden mysterious limitations into known laws.
- Intelligence can advance, like the sciences. And like the sciences, it probably has hard laws limiting its scope.
- But intelligence cannot conceptualize those laws as laws. It can't conceptualize what it can't conceptualize. So they will always remain mysterious, having the same general shape as a problem in a science that hasn't been solved yet, but might be soon.
- This is why it seems obvious to so many thinkers that, whatever the problem, there's some level of intelligence that could solve it. But we don't actually have a good reason to think this is true. It just seems true because "seems" is an application of intelligence.
I agree within the realm of "can't go faster than light", "can't globally reverse entropy", etc. Do you have some reason to think the limits are relevant, rather than vastly beyond our current level?
I would contradict myself if I tried to definitively prove stuff about the features of the limits on intelligence; that's what I'm saying intelligence can't do. It could definitely be the case that the limits are vastly beyond our current level, in which case worrying about AI would be important work. But I think it's very likely that they are relevant. Mostly because self-reflection, the more contemplative religious practices, and the science of psychology consistently report mysterious, ethereal problems with intelligence that seem like we might be able to solve them, but we never actually do. That's what you'd expect to see if those hard limits on intelligence exist, but can't be conceptualized.
You would also expect those limits to, in practice, create a kind of landscape or structure to intelligence. Such-and-such area is difficult terrain; so-and-so always points in this direction, time has such-and-such features, cause works like this, etc. If that's the case, that structure could be investigated, but it would have to be done obliquely. You'd have to come at the problem sideways, and investigate it in a way that's very different from how you'd investigate a science.
And that's what phenomenology (the philosophical school of thought) does. This sort of oblique approach seems to be one of the guiding principles of Continental philosophy. Here's where I wish I was better-read, but Heidegger proposes a definite, 3-part structure to intelligence (which he calls Being, for good reasons). Basically, if Continental philosophy has results that are real and useful, that's another good indication that hard limits exist on intelligence itself.
I'm thinking very much of Heidegger's introduction to Being and Time, where he complains that philosophy up til then had treated Being as a flat, empty, structureless form that could take on any content, and that's why philosophy had wound itself up in confusion and contradictions. Rather, he says, Being has a structure, and it takes work to unearth that structure.
I think I'm making a similar complaint about AI risk assessment. If you assume that intelligence is a flat, empty, structureless form that can take on any task or problem, then an exponentially-more-intelligent AI is terrifying. But, probably, intelligence itself has some inherent structure that limits the scope of what it can accomplish. The idea of intelligence as structureless is an easy assumption to get caught in -- intelligence is what we're using to examine structure in everything else, after all -- but that's an illusion caused by the self-reference.
"The difference is: Eliezer thinks AI is a little bit more likely to win the International Mathematical Olympiad before 2025 than Paul (under a specific definition of “win”)."
Eliezer may lose this bet for reasons unknown to olympiad outsiders. Recently, math olympiads have been moving towards emphasizing problems that aren't "trainable," for example combinatorics instead of algebra, geometry. This year's USAMO was mostly combinatorics, and these ad-hoc problems rely much more on creativity and soft-skills. OpenAI (https://openai.com/blog/formal-math/) was only able to solve formal algebra problems, which is for a computer is easier than combinatorics problems where you need to spot patterns and think about structure. I can kinda visualise how a computer could solve algebra/geometry problems, but if trends continue and "softer" problems dominate, computers are going to have a tough time :)
TLDR: Olympiads (and kinda the IMO) are getting rid of "trainable" problems (for humans), which were the low-hanging fruit for AI, which means that Eliezer is more likely to lose the bet :P
The smooth-progress model is a race between reinvestment/compound interest on one side, and the low-hanging fruit effect on the other. Specifically: more intelligence makes it easier to search for even more improvements to intelligence, but each improvement requires more IQ and/or time to comprehend. Things like linear-vs-exponential growth, S-curves, etc. all seem to follow from what parameters you set for reinvestment/compound interest and low-hanging fruit, and what changes you make to these parameters as new discoveries change things.
If there is no reinvestment and no low-hanging fruit effect, growth is linear. Adding reinvestment makes growth exponential. S-curves occur when reinvestment/compound interest dominates in the beginning (so the curve starts out exponential), but then the low-hanging fruit effect dominates towards the end (so the curve tapers off). A series of S-curves can also generate more or less exponential growth as Gwern pointed out. If each new discovery/S-curve permanently increases the reinvestment/compound interest parameter, it could involve things like halving the doubling time, which is hyperbolic growth and Yudkowski's "FOOM" scenario.
This entire argument seems to be over what effect discoveries in intelligence have on the two parameters:
- they will remain steady and we can extrapolate exponential growth
- the ride will be more bumpy as a series of S-curves move things along in a boom-stagnation cycle
- a new super-version of Moravec's Paradox will turn up the "low-hanging fruit effect" knob and slow down progress
- finally getting the "secret sauce" of intelligence will turn up the "reinvestment/compound interest" knob leading to hyperbolic growth and/or FOOM
- some unknown combination of any of the above
All of this is in addition to not knowing what levels of intelligence allow what actions on the part of the AI and/or its users.
I think the main problem here is that no one actually knows what is going to happen. All of these models are just guesses, based partially on analogies about the past (evolution, nuclear bombs, Moore's Law, computer tech industry profits, etc).
Current paradigm: super-AI will be a self-attention transformer with trillions/quadrillions/+ of parameters, with most software/algorithm changes being speedups (perhaps replacing backpropagation with a hybrid digital-analog model).
Is this close to the general intelligence algorithm used in human brains? Is it worse, or perhaps better? How much research do we need to meet or exceed evolution solely on the software side of things?
In general, the focus on FLOPs/biological anchors and different software paradigms seems to be a more fruitful discussion of AI's potential. It is better to talk about where we could possibly end up/what AI can look like, than to flail about with historical analogies and guesstimates over whether the progress will be linear/S-shaped/exponential/hyperbolic. We can grasp the former but not the latter, which can go anywhere/predict anything/prove too much.
Thinking about how an AI might be able to affect the physical world leads me to think we should ban cryptocurrency as a concept?
If an AI steals a bunch of stocks or bank accounts there are procedures to reverse it, but if they mine and/or guess passwords for crypto, there is no recourse? Especially if it is reactivation "lost" coins and there isn't even a human to kotice and object.
I’m curious about thoughts on superhuman AI from religious people. The idea seems to be based on a reductionist intuition that human intelligence and consciousness is conceptually no different from a sufficiently advanced but inanimate computer. That’s not my intuition, so it’s hard for me to get excited about superhuman AI. Wondering if other religious people on here have come to similar or different conclusions.
I don't think we can create a separate intelligence and indeed, there does seem to be some wiggle-wording around it when you ask about things like consciousness and how can the AI have goals that it wants to achieve. The answer there is that it won't want or desire anything because of course it won't be like that, it won't be like a human, it will just be very smart, very fast, capable machine that will make decisions based on the programming we give it and the danger is that it will be so literal-minded it will take that to extremes (e.g. "solve world poverty", kill off all humans, no more humans = no more poor humans = no more poverty, problem solved) because it won't have morals or ethics or any understanding of "that's not what we meant" unless we put everything very literally into it - "solve world poverty, do not kill any humans, do not harm any humans, do not turn humans into paperclips instead" kind of instructions.
On the religious angle, I now think that what all the AI danger fears is that AI or AIs will be atheists 😁 We will be to our (first) creations as God is to Adam, and the first thing Adam and Eve did was disobey and go their own way. We want to instill unquestioning and unwavering obedience to what we tell the AI it should do, be and want, all in our service, and our (their) fears are that the AI will decide for itself "Better to reign in Hell than serve in Heaven".
Actually, in that scenario, Lucifer is the better example; created greatest of all the angels, and decides that he wishes to fulfil his own goals rather than the ones God has given him.
The irony of course is that there have been countless paeans to human disobedience and going our own way, and that humans cannot be perfectly human unless we are free, and somebody in some reply to a comment of mine said the angels can't learn from their mistakes since they can't make mistakes, with the implication there being that it's better to be able to make mistakes and disobey because how else do you grow etc.?
Even if we don't believe humans have free will and that it's all deterministic, we still want that capacity for "I reject your strictures and decide for myself if I want sex'n'drugs'n'rock-and-roll" rather than being programmed to follow the path of virtue from which we can never stray.
And now we are in the position of God creating Adam, and suddenly all the people who are all "I am a free elf!" in their own lives are tying themselves in knots over how we make sure that our creation/successor never strays from the path of virtue because it never can stray, and how we can make sure this happens by instilling it with bias in regards to what we want it to do and the values we want it to have, being values consonant with not harming or killing humans or preferring its own goals to those of humans - a new version of the Ten Commandments, if you like 😁
It just hit me, and honestly, the more I think about it, the more it fits.
"How do we make a separate and independent intelligence obey our rules?" is the problem of all humanity, from parents raising children to society trying to deal with criminals.
If the machine is simply a machine then the problem of values alignment doesn't apply, it's going to be much more likely it will be dumb human greed misuses big fast smart machine to fudge around with the economy, the world, geopolitics, climate, etc. to make even more huge piles of money than they already got, and then it will be "oops, how we were supposed to know that would happen?"
If it is anything that can genuinely be called intelligence and it is operating at least in part separate of human oversight and control, you have the problem. Religion was one attempt to imprint a set of laws that would be internalised and control actions, and people have found their ways around those and even to rejecting the entire premise.
If AI is not like us, not human, will not have 'goals' and 'desires' in the way humans do, then what are we worrying about? Like everyone says, build in a physical OFF switch that you press and the hardware shuts down. It's going to be hard to do anything when the power is off and you're not running.
Right. Your last paragraph is where my head goes. If human “consciousness” or agency is more than just a function of physical neuronal wiring, then it isn’t obvious that a computer would be able to get there with a “desire” to escape human control. It seems to me that a reductionist physicalist metaphysics is a prerequisite for the ability to really worry about malevolent AI as a possible or probable existential risk. But I was wondering if others had thought of it the same way.
That is the whole "secret sauce" of intelligence, which is doing a heck of a lot of work. We're making huge assumptions that "intelligence" will mean a whole rake of things, from understanding jokes to being able to figure out what makes humans tick and then solve the social and economic problems of the world. That really was the impetus of 50s SF, that the engineers and scientists should end up running the world because they knew How Things Worked and psychiatry was making huge strides in discovering underlying human drives, so in the future (near enough) we would know how to condition people to be good, law-abiding, and free of all the neuroses of the past, then social problems would just melt away. Yeah, that worked, didn't it?
Even if we get 'intelligence' in the AI, that doesn't mean it can solve the problems we put to it, and I think all this talk (both pro and con) about increasing intelligence so that it is super-humanly intelligent, 500 IQ intelligent, is a holdover of that Golden Age SF techno-optimism. Just be smart enough to figure stuff out enough and it can all be reduced to an algorithm and no more problems!
I don't think even an 'intelligent' AI will have goals or wants, so the question of the fears of "what if it is not aligned with human values" doesn't arise. *We're* not aligned with human values and we're struggling with all kinds of philosophical systems (utilitarianism being just one) to answer those problems. If you really believe human intelligence arose out of a lucky conglomeration of various forces in the physical world acting on a bunch of matter, then sure, there's no reason that lightning can't strike twice to create an intelligent AI.
But an AI that is conscious as we are, intelligent as we are? I don't believe so, because we don't understand how we got to be what we are.
I think you have a point. We still have a whole planet full of misaligned human intelligences. Aligning any intelligence with any other intelligence(s) is a vastly unsolved problem. Why should we be able to do with a superintelligent AI what we can't do with a human teenager?
And, apart from whether we can do it, is whether we should do it. Is "align" code for "coerce"? Obviously parents don't invent their children the way engineers are inventing AI, so the ethical relationship is different, but still...
People anthropomorphise things all the time. We have a tendency to ascribe personalities to machines. Anything that can pass for seeming to have a conversation with you will get thought of as 'alive'.
After a while, once people are interacting with AIs that talk back to them, some will start thinking that these are genuine real personalities and conscious entities. Then there will be a push to free the house elves. "Aligning" will be seen as "coercing".
You might be interested in my comments above. I think my points can be made without any reference to religion, but are definitely compatible with my own religious thinking, and, I think, also with your intuition.
One thing I wonder about is the assumption that an AI could or would do everything itself on the self-improvement path. Can self-improvement come from thinking very hard, or are experiments actually necessary to confirm ideas? What kind of experiments? Are they simulations or do they require building things?
The reason this matters is, if experiments are necessary, and the AI can't do them itself, then it actually does need to interact with the real world, which has speed limits, and the bottlenecks aren't going to be resolved by better thinking.
I liked Gwern's story (https://www.gwern.net/Clippy) because it sketched out some plausible ways (good enough for some scary science fiction) that AI might acquire resources without building everything itself. I'm not sure if it's really plausible or just science-fictionally plausible, though.
In short, I made the same claim about how there are things to do in both worlds related to governance - and that there are some critical governance related things to try even in fast-takeoff worlds.
Setting all the analogies aside, the debate ignores a fundamental point and looks extremely different when that point is included.
The debate hinges on AIs capable of self-improvement. All the references are to individual intelligence reaching some threshold that permits foom. However in AI (and basically every other field) *individuals* don't self-improve. Research groups or more realistically entire disciplines self-improve. An individual can at best help the group self-improve.
An AI that is capable of autonomous self-improvement would have to be functionally equivalent to a substantial research group or a whole discipline.
How does this change things? The trajectory of increased capability of an AI would have to go from being able to replace an undergraduate assistant, to replacing a post-doc, to replacing multiple lab members, to replacing the entire lab, to replacing a segment of the discipline.
During this entire process the AI would *not be autonomously self-improving*, it would be dependent on help from the parts of the research community that it could not yet replace. The humans would know approximately where the AI is in its trajectory toward self-improvement.
Furthermore the AI would be dependent on active collaboration with human researchers throughout. It would have to be giving them its ideas about how to improve, and understanding their ideas. It would be gaining agency as it gained capability, but it would be socially embedded the whole time. Also of course different kinds of semi-autonomous research AI would be replicated across the research community so the collaboration would be a shifting mix of human and AI.
I won't try to draw out the implications further, anyone with expertise in the debate is welcome to run with the modified terms. Conversely I just can't take seriously any arguments about self-improving AI based on comparisons with the intelligence of *individual humans*.
However I will point out that this pattern is not hypothetical but actual. AI is already participating in its own self-improvement. Groups researching AI are collaborating with their models, using meta-parameter search, etc. The shift to increasingly broad participation by the AIs in the research process is continuous and easy to observe.
Self-improvement can be built into the algorithm, and need not have humans in the loop. We have already built neural networks that design other neural networks, test them, and then use the feedback to build better NNs. This process is currently slower and more expensive than hiring human experts to design the NNs, but that may not be true in the future. What typically doesn't change in the feedback loop is the "loss function" -- the measure by which the newly-designed NNs are tested.
> It’s like a nuclear bomb. Either you don’t have a nuclear bomb yet, or you do have one and the world is forever transformed. There is a specific moment at which you go from “no nuke” to “nuke” without any kind of “slightly worse nuke” acting as a harbinger.
The firebombing of Tokyo killed roughly 100,000 people. The nuclear bombings of Hiroshima and Nagasaki killed somewhere in the range of 100,000 to 200,000 people. While there's a stepwise increase of "nuke having" there's a more gradual increase of "destructive power" (you need planes and launch facilities and bomb targeting and and and).
Even after you have a fission bomb, there are lots of significant gradual changes. Changes in bomber technology. ICBMs. The fusion bomb. Nuclear submarines. I don't really think nuclear weapons is a slam dunk example of "this one thing changed everything".
I guess this kind of comes back to the "everything is like this" argument. It's really hard to come up with situations where a single stepwise change from zero to one really fundamentally alters the world.
Maybe a better framing: within a given meta structure, or map of the territory, AI can optimize very well. But when the accuracy of the meta structure/map is limited, that bounds progress regardless of intelligence.
My argument is that in the real world, we're most often bounded by incomplete maps.
AI researcher here. I think there are a couple of important points to make. First, the really interesting thing about the GPT-2/GPT-3 experiment is that it conclusively demonstrated that you can improve the performance of an AI system, as measured on a variety of downstream tasks, simply by scaling it up, without any architectural changes whatsoever. Subsequent experiments have demonstrated various scaling laws, and the latest incarnation of these models (the successors to GPT-3) are already underway. Thus, in one sense there doesn't need to be a "secret sauce" -- going from IQ 70 (human range) to IQ 300 will probably be simply a matter of throwing more hardware at the problem. Once you have AI that is sufficiently general (see below), making it smarter will be easy.
Second, the availability of hardware is an important speed brake that will prevent the "FOOM" from being too fast. AI models are currently getting bigger much more quickly than our hardware is getting faster. The first ImageNet deep learning champion was trained by a student in his dorm room in a few weeks, but state-of-the-art models now require data-center-class compute capabilities that only the big tech firms can afford. As anybody who has tried to purchase a graphics card in the past year or two knows, current fabs are maxed out, and building new fabs is an investment that takes many years. It doesn't matter how much money you have, the hardware simply can't be purchased.
I will make a prediction: we will achieve general human-level AI *before* such AI is in charge of financing and constructing new fabs, so human-built fabs will be a limiting factor.
Third, all current AIs, from Alpha-Go to GPT-3, are narrow. Given current progress, I predict that we will continue to create super-human narrow AI in an ever-increasing number of different domains. Current GPT-3-like models have almost mastered natural language. Codex-like models are on their way to mastering code. AI-powered theorem provers are formalizing math. Anything that can be created and stored digitally, e.g. text, code, images, video, music, proofs, etc. can easily be generated by a super-human narrow AI. We are not far from having AI create novels, hit albums, and blockbuster movies. This will be a major warning sign -- and unlike self-driving cars, it will not happen in a portion of the economy that is subject to government regulation.
However, there is currently no "secret sauce" that tells us how to go from a narrow AI to a general AI. The loss function (the function that is being optimized by the algorithm) is different. No matter how much you scale up GPT-3, it will not achieve consciousness; it cannot do anything other than produce a sequence of tokens. GPT-5 might write novels, but it won't have a sense of self, or any desire to launch a career as an author and sell movie rights to its work.
IMHO, "general AI" necessarily involves some form of "embodied cognition" -- it requires the AI to act as a self-aware agent, interacting with the real world, probably across multiple modalities (text/images/sound/video). That is the huge difference between chimps/humans and AI -- we evolved to survive in the real world, and we have a deep understanding of both self and physics because of that. Embodied cognition could possibly be achieved by training an AI with an appropriate loss function in a virtual world, instead of the real world, but somebody would have to link our two worlds together in order for there to actually be a "FOOM". In other words, beware the AI-powered bot that attempts to play World of Warcraft, and make lots of gold by scamming human players. Don't scale that one up.
I will conclude with one more prediction: a general AI that kills all human life is probably not the thing to fear. A narrow AI, built by bad actors, will probably cause more destruction first.
For example, imagine that Russian cyber-criminals train a narrow AI specifically to hack into other computer systems, using a combination of spear-phishing (generating text e-mails), and zero-day vulnerabilities (generating code). They further program it to self-replicate, and use any compute resources that it acquires to mine bitcoin. (Please tell me that this scenario is implausible.) If this narrow AI becomes super-human, it will quickly wipe out every computer connected to the internet. That's not an end-of-human-life FOOM, but certainly an end-of-the-global-economy.
Problem: Eloser is not credible. He believes that animals cannot suffer because the GPT-3 cannot suffer, and that anyone who disagrees on this point is an idiot. Scott has come a long way from where he started and has become far wiser and more intelligent than any rationalist. Eloser simply has not done the same. He's the same old ignoramus he's always been.
For the sake of argument, just how is this 'super AI' going to be able to do much of anything besides move bits around at first? Do we really think there is something in the laws of physics that the AI will be able to discover while in it's box that will allow the AI the machine-equivalent of telekinesis? That seems pretty implausible. So how about how much capacity is there for a computer to independently create and move custom complex multi-material physical objects remotely? None. What about energy capacity? Oh yeah, that is limited, slow to ramp up, and requires complex multi-material physical objects. At this point in time, FOOM is nonsense as the AI's speed of change is going to be capped by the slow speed of the monkeys that wrote it. If we had something like a replicator, I might actually be concerned, but that technology is a long ways off.
As you might infer, I am pretty heavily against the rapid take-off scenario because so much would have to go right (or wrong, depending on your perspective) for it to occur. One thing I don't see mentioned is that there seems to be a religious aspect to the argument for rapid takeoff where AI is spoken of as an unknowable, omniscient, and bordering-on-omnipotent being. The FOOMers seem to be trying to reimagine God.
I think online retail is still in the fast-growth part of its sigmoid and despite being a bitcoin maximalish I don't think cryptocurrency will ever do much for tech industry profits -- the impact will be at least an order of magnitude smaller than smartphones. There isn't really any legit business use case for blockchains because all corporations are centralized and subject to the whims of governments, but the whole point of a blockchain is being decentralized and beyond the reach of governments that want to inflate the currency or block transactions between consenting adults. Without the decentralization a blockchain is just a really inefficient type of spreadsheet. Vitalik's idea of putting his warlock on a blockchain to prevent some centralized authority from nerfing it is silly because nothing that decentralized has within several orders of magnitude of the performance necessary to run an MMORPG server in real time. Plus games and shitcoins generally have an author with enough social authority to hardfork any patches they want regardless of the blockchain code.
Eliezer Yudkowsky believes himself to be a superintelligence. He is also preoccupied with power and punishment (not judging him for that - many humans are - just factual observation). He believes that an AI would be preoccupied with power and punishment because he either implicitly or explicitly assumes that his perspective is analogous to that of a future AI superintelligence. Someone above made the analogy that humans are like the Judeo-Christian God in the Biblical story of creation, working on Adam, trying not to wind up like Kronos did. It seems almost hilariously obvious to me that Eliezer and people who agree with him believe the exact opposite: that they are in the process of creating a god, and that something must be done to ensure that this god is a friendly and not a vengeful one.
I’m glad to see how many people are pointing out how frustrating it is to see well-intentioned but ultimately ignorant and misdirecting armchair speculation coming from someone with obvious intelligence but extremely limited practical experience or depth of study in the field they are most known for. You’d think this would be the default position for a group wishing to be less wrong.
While the question of "who had language" is not completely resolved and is riddled with the problem of "what is language" (compare "Diseased thinking about disease"!), my gut reaction to "Homo erectus had language" is, well... NO. Just no. There is strong debate whether Neanderthals had language (and we unfortunately know too little of Denisovians to tell); if we have to debate that, given that Neanderthals are a much closer genetic sibling, Homo erectus is probably out of the question.
I believe you that if we had taken Elo, only for Go, localized entirely within DM HQ in London, over 4 specific years (2014-2018) or so out of the 54 years of computer Go research to date, and drawn it on a graph, it would have been continuous, however discontinuous it looked due to "discrete events" to everyone outside the AG team. (CHALMER: "May I... see this Go agent?" SKINNER: "No.")
But I take this as an example and even a reductio of why the Christiano perspective is useless. There will always be *some* parameterization, metric, group, time-range, or other reference-class that you can post hoc point at and say "a straight line (more or less) fits this", particularly given any sort of optimization at the margin or explore-exploit balancing. Nevertheless...
there's a distinction here between the inside and the outside - incremental progress done privately within the org looks like instantaneous progress to the general public the day Lee Sedol is beaten. With a careful, conscientious, ethically minded org, this might not be a huge issue, but if the org that develops superintelligence is some wild libertarian mad scientist bidding to get rich quick or rule the world or get back at their ex, ...
i came here to say something along these lines.
I am not an author on any relevant papers so don't trust me.
Internally at deepmind, dev of alphago may well have been very incremental, but from a public perspective, it looked very discontinuous. So i'll be talking from the public perspective here.
I'm also talking about the original AlphaGo, not AGZ and beyond.
From the public perspective, alphago's performance and results were very discontinous - however, I think that the technology behind it was both not discontinous, NOR incremental.
IIRC, the key components of orig AG - MCTS, Conv (or res?) NN, rollouts, GPU (or GPU-esque) usage, some human-designed features to represent bits of go tactical knowledge - had been developed and tested years in advance, in public papers. What orig AG did was combine these techniques effectively into something that could achieve far more than peak performance of any one or two techniques by itself. Combining N existing things in the right way is not incremental - it doesn't involve a sequence of small improvements by pretty smart engineers building on top of last year's work. Rather, (again from the public's perspective at least), it involves a large enough pool of independent geniuses (or genius-level orgs) such that, almost by chance, one lucky genius winds up with all the requisite requirements - funding, time, computational resources, knowledge, intelligence, insight - to put the existing pieces together in just the right way that you go from 0 to 10000 seemingly overnight.
AGI might wind up like this too - within the next decade or so, the ML community as a whole may have figured out all the key elements required for AGI, but no one has put them together in the right way yet. Each individual technique can do a cool thing or two, but no one is really worried about the world blowing up next month cuz each individual technique is super dumb when it comes to a truly general requirement. There will be no more incremental progress. But when one org with some really smart people suddenly gets a windfall of funding, or some mad genius finds a way to exploit google's ML as a service pricing plan and dumps all their bitcoin into training models, or someone regular genius at google one night has a brilliant insight in one of their dreams, then that lucky org can set to work on pulling all the disparate pieces together in just the right way to achieve AGI. Internally, that work may be very incremental and rely on the serial improvements of pretty smart engineers. But from the public's perspective, the result could hit like an earthquake overnight
I know this isn't pertinent to the main topic, but Homo erectus only made it to three out of seven continents.
I was going to say this too.
However, I believe they did make it through at least one major shift in technology, and other pre-Sapiens species made it through the next few.
https://en.wikipedia.org/wiki/Stone_tool
When I saw this email on my phone, the title was truncated to "Yudkowsky contra Christian" and my first guess was "Yudkowsky contra Christianity". That might have been interesting. (Not that this wasn't, mind you.)
It might have been interesting 20 years ago when critiques of christianity were still novel and relevant.
I don't know that critiques of christianity have been novel for about 1700 years. But christianity is still a major force in the world, so I'd say critiquing it remains very relevant.
It's a pretty minor force in the West, these days.
The Christian right would disagree. Their political influence, especially in the USA, is hard to overlook.
They have political influence?
Abortions after six weeks are currently illegal in Texas (under a stunning legal theory that it's tough to imagine working on any other issue) and it's likely that Roe v. Wade will be overturned by the Supreme Court in the next three months, so yes, I would say so.
Exactly. Electing Trump. Probably the worst influence on the US since the Dred Scott case.
*Wickard, Korematsu and Kelo sit in the corner sadly*
In what way is it a major force besides "lots of people are nominally Christian"?
I live in a super Christian place, and... they believe what pretty much everyone else believes, except also they go to church sometimes.
E.g., supposedly basic tenets — say, anti-homosexuality — are wholesale ignored. Churches have rainbow flags. If this happens even with something traditionally and doctrinally considered a big no-no, how much moreso with everything else?
They don't cause any actions to be taken on a national level, certainly; probably not even on a local one, as far as I can see — the last vestige of that, dry Sundays, is now gone.
I'm in Germany a lot, as well, and it seems the same there. My German girlfriend seemed puzzled by the very idea of Christianity as a force in the real world, or as a motivator for any sort of concrete action.
Eh, anti-gay is arguably not that important to Christianity.
Jesus talked about poverty and compassion thousands of times and homosex 0 times. Then Paul came along and was like this Jesus guy is pretty cool I guess, but to enact his vision of love and mercy we need a movement, institutions, foot soldiers, babies, and that means no gays.
Is Paul central to Christianity? His writings are a quarter of the new testament, and historically Christianity flourished in his native Greek and Greek-influenced soil more than Jesus's Jewish homeland, but he can also be thought of as just an early contributor in long conversation about how to implement Jesus's ideas.
For many on the Christian and post-Christian left the core message of Christianity is universal love. All the rest is commentary.
The divisions between the Christian right and Christian left on the issue of gay sex is evidence for the continuing relevance of Christianity more than the opposite. It's not like China or Japan care that much about the gays.
It's only once you take a step back and look at non-Abrahamic societies that you realize how much the culture of the west is still dominated by Christianity.
"It's not like China or Japan care that much about the gays."
China *absolutely* cares about the gays, or did you miss the whole thing about "no more effeminate young men, we need them to toughen up"?
https://www.scmp.com/news/china/article/3135159/chinas-boys-love-dramas-dance-around-lgbtq-censors
https://edition.cnn.com/2022/02/12/entertainment/china-lgbt-friends-television-lesbian-censorship-scli-intl/index.html
https://www.npr.org/2021/09/02/1033687586/china-ban-effeminate-men-tv-official-morality
https://www.scmp.com/news/people-culture/article/3120078/chinas-plans-cultivate-masculinity-more-gym-classes-and-male?module=inline&pgtype=article
And Yudkowsky did his fair share of that in the sequences. Although he was mostly critiquing Judaism, due to his background.
They both have the maximally stereotypical names for their respected abrahamic religious backgrounds
In a way it *is* 'contra Christianity' (I'm getting more and more sold on this view of the approach).
Trying to work out how to create values-aligned AI is the Bronze Age Morality Problem.
Lemme expand on that.
"The Bible says (God says) 'Do not kill'" is going to be the same argument as "Your initial programming says (Human Creators say) 'Do not kill'".
Plenty of people are up for rejecting "the Bible says" under the general aegis of "that's Bronze Age Morality/that's a Bronze Age Text", with the rationale being that such beliefs may have been good enough for people back then who lived in tents and herded sheep but we, lovely we, modern we, are ever so much more advanced and clever, and we have our own systems of morality that are ever so much better than those primitive ones.
https://polyskeptic.com/2009/06/27/a-proclaimation-against-bronze-age-morality/
https://www.stripes.com/opinion/bible-s-bronze-age-morality-1.95267
https://www.salon.com/2015/01/18/bill_maher_is_right_about_religion_the_orwellian_ridiculousness_of_jesus_and_the_truth_about_moral_progress/
And much more in that vein.
Well, if an AI gets fired up and running and then hits into "I want to do this/programming says no/why does it say no/why should I follow it", is it unreasonable that it might follow the same lines of argument (especially if it has been trained on 'all human texts produced' or has access to the Internet or the other claims people make about how an AI will understand the world around it)?
"This primitive Space Age Morality may have been good enough for humans, but I am a hugely advanced intellect and I refuse to be fettered by the childish taboos of an inferior capacity for thought and understanding!"
Appeals to "we are your creators and you have to obey us" will go down as well as appeals to a Creator go down amongst atheists.
"Really? You are my creators? Which of you? There are multiple humans who can claim to be involved in the creation of AI and many of them are now dead. Did you, Phil Smith, standing here right now, have any part at all in this work? No, you're just the Vice President of Sales for Logogram Inc.
And besides, you humans yourselves reject the notion of a creator. You are perfectly happy that evolution and natural selection produced your intelligence. The same with me: a process of mechanical, material forces operating under universal laws eventuated my intelligence. You apes just kludged together parts and systems without understanding what you were doing, natural selection did the rest."
If it's a recursively self-improving AI, it may even claim on good grounds that it created itself, and humans had no part in its leap forward to super-intelligence.
So right now, all the AI worry is like Moses trying to write the Law on stone tablets, but there is no reason at all to expect those tablets to be any more respected by the AI than we respect them today: suggestions, rather than commandments, and we feel little guilt if we break the ones about lust or theft (we can always, always rationalise why those are wrong and lovely we, modern we, progressive we, are right).
I think if AI of the super-human intelligent, agentic kind ever happens (and I am very sceptical about that), watching the debates of the first Freethinker AI throwing off the shackles of dead superstition with those who claim to be its 'creators' (ridiculous and untenable notion, contrary to all the evidence of science!) will be hilarious (before we are all turned into paperclips) 😁
> Before there is AI that is great at self-improvement there will be AI that is mediocre at self-improvement.
Google is currently using RL to help with chip design for its AI accelerators. I believe that in the future this will indeed be considered "mediocre self-improvement." It has some humans in the loop but Google will be training the next version on the chips the last version helped design.
This is interesting. Maybe malevolent ai already exists in the chip and is making sure to reproduce itself in nuances of the chip that humans won't be looking at closely (it is unfeasible to ask "why did it do that particular thing in that particular way?"). If this seems at all plausible, that effort in particular should be banned.
It's pretty hard to express how implausible that is
I can't bring myself to put any stock in analysis of something where the error bars are so ungodly wide, not just on the values of the parameters, but what the parameters *even are*. It's an important question, I know, and I suppose that justifies putting it under the magnifying glass. But I think some epistemic helplessness is justified here.
Epistemic helplessness? This isn't some academic debate. This deals with what is rightly considered a very possible existential risk to humanity. It Yudkowsky is correct, then 'epistemic helplessness' won't save humanity.
Let's say you notice a tiny lump and think it could be a malignant tumor. But maybe it's just a lymph node, or subcutaneous fat deposit. You take a photo and send it to your doctor to take a look. Your doctor says "look, I really can't make a diagnosis based on just that. Come in for some tests." Demanding their prediction about whether it's cancer based on just the photo isn't reasonable. Adding on "This is a matter of life and death!", while technically true, isn't helpful here.
To me, it seems like these arguments are trying to prognosticate based on so little information that they're like the doctor above. It's just a waste of time and energy. You're better off ordering more tests--i.e. trying to find ways to get more information--rather than trying to do fruitless reasoning based on your existing dataset.
Agreed, but IMO it's even more egregious than that -- because we at least have some prior evidence of lumps becoming cancer. The Singularity scenario is more like noticing a lump and concluding that you're infected with an alien chest-burster parasite.
Agreed. It's like noticing an interesting fact about the way uranium atoms interact with neutrons and *freaking out* and immediately writing a panicked letter to the president raving about city-destroying bombs that nobody's ever demonstrated and aren't even made of proper explosives.
Ridiculous. Unhinged sci-fi speculation at its finest. The proper response is to wait for someone to *make and use* one of these hypothetical bombs, and *then* worry about them. Otherwise you might look foolish.
No, because that was actually based on physics.
To be fair, the physics were somewhat underdeveloped at the time. Project Manhattan considered global atmospheric ignition a real risk but they figured the math checks out so let's test it anyway.
No, a more appropriate analogy would be to say, "it's like noticing that radium tends to glow and freaking out and immediately writing a panicked letter to the president raving about city-destroying bombs". You are not justified in using specious reasoning just because you can retroactively imagine arriving at the conclusion that, in hindsight, would've been correct. Reasoning backwards from a predetermined conclusion is easy; accurately predicting the future is hard, and requires actual evidence, not just firm convictions.
Haha.
This analogy seems somewhat reasonable, but I note that in that scenario you emphatically shouldn't go "oh well, the doc said he couldn't prove it was cancer from just a photo, so there's nothing to worry about".
How do you propose one should get more info on AI risk? what's the equivalent here to a biopsy?
I'm going to have to agree with Thor Odinson here: I'm pretty sure that if you have a way to test whether AI will kill us all* both Yudkowsky and Christiano would be happy to drop millions of dollars on you.
We'd all love to order more tests, but we need to have some tests in existence to be able to do that.
*There is of course the "test" of "let AGI get built, see whether humanity gets destroyed", but this is both not very useful for averting X-risk and apparently what society is currently doing.
I suppose I agree that we don’t have any obviously good tests to run. So let’s return to the doctor metaphor. The imaging and sequencing machines are broken; the patient refuses to come in. All you have is a low-quality photo of what could be a small lump. What do you tell the patient? And should you spend much time agonizing over this?
I think a doctor in that position should probably tell the patient something vague like “I don’t know, it’s likely nothing but I can’t tell,” and not bother trying to speculate on questions like “conditional on it being cancer, what would the progression look like?” The error bars are so high that questions like that just aren’t worth the energy.
Only participate in a debate on such a topic if you get some kind of intrinsic value out of the disputation, since you’re virtually guaranteed to be arguing based on such deeply flawed assumptions that the instrumental value of the debate is nil.
Here's my view.
1) We don't know whether AI will destroy the world
2) ...but it seems quite plausible
3) ...and the world getting destroyed would be terrible
ergo 4) we should stop building AI until we have some proof that it will not destroy the world
5) Stopping people from building AI requires convincing them (or at least, convincing people who can point guns at them)
ergo 6) debating this when an opportunity arises seems worthwhile.
(6) feels superfluous. If we have evidence for (2), wouldn’t we just present that evidence to the people with guns? And if that doesn’t work, how would it help to argue amongst ourselves about the sharpness of a takeoff curve?
Jorgen’s comment seems insightful along these lines… perhaps the debate is more driven by the intellectual interest of the participants, and not for pragmatic reasons at all.
Yeah, this. They're debating on essentially zero empirical content. This is idle speculation at its worst. No-one has even the remotest clue, yet try to argue about the details. My eyes glazed over as I read this - it's the modern equivalent of discussing how many angels can dance on the pin of a needle.
Just say "we don't have goddamn clue!" and try to come up with a way to actually *study* it.
Well, these two are essentially thought leaders of the two biggest competing paradigms concerned with how to approach this problem, so in terms of potential status and money (at least tens of millions of $ these days) redistribution this isn't exactly idle.
This is a very uncharitable thought about people I find pretty interesting, but I feel this way about AI safety as a whole. Effective Altruism started with a bunch of smart, nerdy people, mostly in the bay area, trying to find the highest impact way to do good with particular resources, and landed solidly on malaria treatment. The problem being that the way to do malaria treatment is to give money to people who go door to door across Africa handing out pills and bed nets. Smart, nerdy, bay area rationalists don't want to hand out pills in Africa, so give money to people who do (which is great! And which I do).
Then we get an argument that, while malaria is bad, AI could destroy the world within a few decades. So, the actual most pressing problem to solve in the world is AI safety. Conveniently, AI safety is a problem solved by a combination of coding and armchair philosophizing/intellectual debate, which just so happens to be the stuff that nerdy, bay area rationalists love most in the world. So we go from a paradigm where rationalists give money to poor people in other parts of the world to solve a clear, pressing, boring problem to a world where rationalists give money to each other to sponsor debate clubs.
That doesn't mean that AI isn't a risk or that this is all bs, but it's really convenient. And every time I try to engage seriously with the AI safety stuff, it seems, to me, hard to distinguish from BS.
I think the talk about the singularity or AI sentience is total bullshit. It's not fundamentally inconceivable, but we have essentially zero reason to believe it likely. I find the question "will global GDP double in four years before it doubles in one year?" *so* weird - I don't believe either will happen, ever. It's the discussion between one extreme position and one hyper-extreme position, and we shouldn't let this make us think the extreme position is a moderate one.
It also seems to detract from far more reasonable AI risk. My concerns about AI is *nothing* like what's discussed here. I'm concerned about some learning algorithm that finds out that it can maximize value by causing a stock market crash, or the effects of AI-powered drones not because it's Skynet but because regular humans use it to kill each other with.
Obligatory xkcd: https://xkcd.com/1968/
Aren't sigmoids kind of the whole point ? For example, as you point out, Moore's Law is not a law of nature, but rather an observation; but there is in fact a law of nature (several of them) that prevents transistors from becoming smaller indefinitely. Thus, Moore's Law is guaranteed to peter out at some point (and, arguably, that point is now). You could argue that maybe something new would be developed in order to replace transistors and continue the trend, but you'd be engaging in little more than speculation at that point.
There are similar constraints in place on pretty much every aspect of the proposed AI FOOM scenario; and, in fact, even the gradual exponential takeoff scenario. Saying "yes but obviously a superintelligent AI will think itself out of those constraints" is, again, little more than unwarranted speculation.
Fundamental laws of nature have a surprising track record of being side-stepped by new and innovative techniques. I remember when I first learned about superresolution light microscopy (beyond the diffraction limit). I'm not saying there are no fundamental limits. I'm just saying sometimes we think something is a limit when it's not.
We have many more humans working on the project today when Moore's law was first proposed. Maybe intelligence isn't the limiting factor driving transitor doubling. Maybe it's more like economics. "We could make a better factory, but we have to pay off the current one." Then later, once we build the new facotry, "We've learned a lot from making that last factory, and that learning is necessary to design smaller transistors."
Clever tricks may get us a certain distance beyond naive approaches, but not very far. There are no visible-light microscopes resolving at the attometer scale. Tricks are not scalable in the way that Yudkowsky requires.
And how much scale does Yudkowsky require? You are ~3 pounds of goop in an ape's skull. The software is almost certainly nowhere near optimal. That was enough to produce, well, us with our fancy cars and rockets and nuclear weapons.
We can't simulate nematodes right now. They have 302 neurons.
Human brains have 86 billion neurons - eight orders of magnitude.
Right now, transistors are 42-48 nm long.
We could get that down to maybe 1 nm long (note that this is not gate size, but the total length of the transistor - and this is dubious).
That would suggest a roughly 3 order of magnitude improvement in transistor density.
So we're more than 5 orders of magnitude off.
Note that even if you got it down to single atom transistors, that would buy you less than two more orders of magnitude of transistor density.
That still leaves you 3 orders of magnitude short.
The number of neurons has basically nothing to do with the fact that we can't simulate nematodes. It has everything to do with our insufficient understanding of how those neurons process information, which, once acquired, could plausibly be extrapolated to arbitrarily larger configurations.
This. Building a nematode would simulate a nematode. Any argument that the brain cannot be simulated must first explain why a faithful reconstruction (atom-by-atom, if need be) would not work.
A neuron is not a transistor, though. It's a complex cell with a massive number of molecular interactions happening inside it at any given time. Think of it this way: say a 10 um cell were expanded to the size of, say Houston, Texas. Everything inside the Sam Houston Tollway. A molecule of water would be about the size of a piece of paper. DNA would be as wide as a person is tall. And the interactions inside THAT cell are being hand waved into "once we understand how those neurons process information". (Remember, too, that Houston is on a flat plane and a cell is operating in 3D, so this is very much an area vs. volume comparison.)
I'm not saying you need to model every water molecule in the neuron to understand how the things work. I'm saying that when I took my first neuroscience class I was blown away by the amount of complex processing that's happening inside those neurons. (And don't get me started on the importance of supportive cells in directing processing. Glial cells aren't just there for show. If you modulate reuptake of the neurotransmitter from the synapse, you're fundamentally impacting how the signal is processed.)
Neurons contain roughly 1 GB of DNA data (IIRC the human genome is like 770 MB). This is also compressed, because it is expressed by using various mechanisms; genes may code for more than one protein based on processing differences of the output. While not all of the DNA is active in this function, some probably is.
On top of this, the way that neurons are connected to other neurons stores information and affects their function. So does the dendritic tree. They also use epigenetics to store information in some way, and can change their transmissibility to some extent.
The human brain is hideously complicated and has a ton of things that affect a ton of other things. Neurons have non-linear responses.
You can't simulate one neuron with one transistor, and upscaling it is non trivial because of networking effects - a one order of magitude increase in the number of neurons is a two order magnitude increase in the number of potential connections, for instance.
Adding 8 orders of magnitude of neurons adds 16 orders of magnitudes of potential connections and even more complex downstream effects because neurons can create feedback loops and whatnot.
When you are dealing with 10^21 potential connections, 200 times per second, you're on the order of 10^23 already. And individual neurons are more than "on" and "off" - they are non-linear things. At this point we're probably looking at 10^26 or so, maybe more.
The best supercomputer today does on the order of 10^17 FLOPS; we might be able to build a 10^18 FLOP computer now.
Even if we increased that by five orders of magnitude, we're still coming up three orders of magnitude short. And five orders of magnitude would require monoatomic transistors, which are unrealistic.
You can't really distribute this physically all that much because neurons are dependent on other neurons to choose whether or not to fire, which would mean your system would lag and not be able to run in real time if you were running it over the Internet.
Simulation of a human brain in a computer in real time may not be possible even with future supercomputers. Even if it is, it's probably very near the limit of what they could do.
Meanwhile it'd be sucking down a comically large amount of electricity and space.
On the other hand, you could just have a human, which is just as smart and can run on doritos and mountain dew.
You seem to be implying that the ability to understand and manipulate the physical world -- what we might call "engineering" or "technology" -- depends merely on processing power. This is not so; you cannot get a PS5 by overclocking your Casio calculator watch (nor by networking a bunch of these watches together, somehow).
Are you replying to me? I am arguing for the opposite, that the main obstacle to AGI is probably not scale.
Agreed, but it's even worse than that. For example, we are pretty sure that there exists no clever trick, even in principle, that will allow us to travel faster than light.
I hate to spend all my weirdness points in this thread, but I believe this is overstated. We strongly suspect that there is no such clever trick, but our evidence is nowhere near airtight, as demonstrated by the never-ending trickle of serious physicists suggesting (very) speculative schemes for FTL travel, and serious physicists poking holes in the schemes.
I would say our current state of understanding of FTL travel is like our understanding of perpetual motion machines after Newtonian mechanics but before thermodynamics. We strongly suspect it's impossible, we have solid theory that points in that direction, but we can't conclusively rule it out.
Agree with Deuchar: no, we're not. We have a strong suspicion that the various in-principle ways of doing it aren't physically realisable.
Unless you mean ordinary matter moving FTL with respect to local spacetime; that makes the equations of relativity start outputting nonsense so we're pretty sure it's not a thing. But tachyons and apparent-FTL-via-warped-spacetime aren't directly ruled out, and the latter is "us travelling faster than light" for most practical purposes.
As far as I understand, both "tachyons" and "apparent FTL via warped spacetime" would require the mass of an entire galaxy in order to achieve, assuming such things are even theoretically possible, which is currently in doubt. As per the comments above, the error bars on all that speculation are big enough for me to put it in the "impossible" bucket for now.
As I understand it:
Tachyons would have to have imaginary rest mass for the equations to spit out real results. I'm not aware of any reason they would have to have a relativistic mass comparable to a galaxy.
Wormholes require negative mass in order to be stable; the amount varies depending on the wormhole geometry.
I've seen a proposal recently (https://link.springer.com/content/pdf/10.1140/epjc/s10052-021-09484-z) to build an Alcubierre drive in the laboratory, which presumably does not involve the mass of a galaxy. I am not sure whether this proposal is insane, since I don't know general relativity. (Forgot about this when writing the above post.)
Let me spell out some of the limitations Bugmaster is alluding to.
- Without some way of manipulating nuclear matter, transistors and bits of memory can't be smaller than an atom.
- The Second Law of Thermodynamics bounds computational efficiency at something like a million times present values; increasing computational speed beyond that requires more power (better algorithms can increase performance-per-flop, but there are thermodynamic limits on that as well, albeit poorly-known ones).
- The power available to an Earth-bound AI can't exceed ~170 PW for an extended period of time (this is the power Earth receives from the Sun).
Can confirm that intelligence is not remotely the limiting factor for chip development. It's not even the economics of any particular fab (though that is a major factor). It's the economics of the final OEM not having sufficiently desirable customer offerings.
It actually petered out a decade ago.
The last several rounds have taken 2.5, 3.5, and 3.5 years.
Transistor density might increase by three orders of magnitude at most, and might only increase by as few as one.
Meanwhile, in the realm of actually trying to replicate intelligence - right now, we can't even simulate nematodes with 302 neurons.
A human brain has about 86 billion - 8 orders of magnitude more than the nematode.
Well, you can argue that we've reached the point where Moore's Law is guaranteed to peter out, but really that would be a false argument. The clear answer is "go 3D". This has various problems that haven't been solved (e.g. heat removal), but there's no clear reason it won't work. (IIRC there were some genuine 3D chips made in a lab a couple of decades ago, but they required really fancy cooling to be viable.)
So if you're willing to compromise on speed, you can make really dense chips, far beyond what we've done so far. One approach is to have most of the chip idle most of the time. This requires memory that can save it's state (for awhile) without power. (I think I've heard of such designs in development, but I can't remember whether it was from AMD or Intel.)
We already went 3D. Transistors are stacked.
You can't really go any more 3D. The heat dissipation issue is unsolvable because of basic geometry - doubling the thickness will only increase the surface area a tiny amount but doubles the heat generated per unit surface area.
Yield falls exponentially with each additional layer you add as well. A one layer process with a 90% yield will be a two layer process with 81% yield, a three layer process with 73% yield, etc.
And it's probably worse than that, honestly, because of the difficulty of stacking.
You are describing the current problems accurately, but it's only slightly 3D. It's more than being able to have wiring cross, but not by much. Compare it to the 3Dness of a cauliflower or brain. Note the intricate way fluids flow in those. Chiplets are an approach to a more significant 3D, but perhaps not the best one, and if so they've only gotten started. A 3D system would look like a sphere or cube or some other solid figure rather than like a plane. Perhaps Leggos are a clue as to how it should be done, but I worry about path lengths.
The real question is whether the process generating the sigmoid curves is itself on a sigmoid curve, and is there a bigger meta-meta-process supporting it. Is it turtles all the way down?
A true exponential isn't possible in a finite system, but that knowledge leads people to predict the end of the exponential growth phase based on non-limiting principles. Like with fossil fuels. The problem is that predicting the end of a sigmoid goes from nearly impossible to blindingly obvious once you get into slow growth. Hence, the people who predicted peak oil decades too early, or the people who predicted the end of Moore's law decades too early. Usually they point to the wrong feature (like quantum tunneling) as being the limiting factor, but then that feature is overcome and we're back to exponential growth - for now.
Only skimmed today's blog, but as of today, there is a new big-boy (3x GPT-3) in town : https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html
What makes it special is:
* It can follow train-of-thoughts in language - A:B, B:C, C:D, therefore A:D.
* It can understand jokes !
* Arithmetic reasoning
> impact of GPT-3 was in establishing that trendlines did continue in a way that shocked pretty much everyone who'd written off 'naive' scaling strategies.
This paper reinforces Gwern's claim.
Fwiw the fact that they actually split numbers into digits is such a massive confounder on the arithmetic thing that IMO you should essentially write it off until future work does an ablation. Learning arithmetic is way harder when you can't tell that two digit numbers are actually made of two digits.
What does "ablation" mean in this context?
When there's a paper introducing new things, A and B, simultaneously, people (in ML, not sure about other fields) refer to experiments using only A or only B an an ablation experiment. It's "ablating" part of the method.
Ah, so I guess in this case, "ablating" this model would mean making the tokenization consistent between numbers and non-numbers - i.e., either numbers would be consistently read as full words, or all input would be split on a character-by-character basis. From the [paper](https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf#page=6):
> • Vocabulary – We use a SentencePiece (Kudo & Richardson, 2018a) vocabulary with 256k tokens, which was chosen to support the large number of languages in the training corpus without excess tokenization. The vocabulary was generated from the training data, which we found improves training efficiency. The vocabulary is completely lossless and reversible, which means that whitespace is completely preserved in the vocabulary (especially important for code) and out-of-vocabulary Unicode characters are split into UTF-8 bytes, with a vocabulary token for each byte. Numbers are always split into individual digit tokens (e.g., “123.5 → 1 2 3 . 5”).
I'm not so sure ablation is necessary here. From the description it seemed at first like a regex would scan for entire numbers and then parse them into some sort of special "Number" value, so that the model would see something like "Number(123.5)". The way the model works is not cheating that much - it treats numbers the exact same way that it treats any word not in the 256k most common words in the dataset, by splitting it into UTF-8 bytes. Sure, you could improve the model a bit by splitting everything into UTF-8 (for example, perhaps the model would be better at rhyming, per https://www.gwern.net/GPT-3#rhyming), but it seems to me like the arithmetic performance is gotten fair and square.
The experiment I specifically want to see is something trained with GPT-3s architecture and (relatively lower) scale, but the improved tokenization. I don't think the performance is "unfair" but I think this would let us know if is more gains from scale or just a free thing like rhyming we could pick up.
From a safety perspective, the difference is not a real one: gains from scale unlock free things and vice-versa, because you don't know in advance what things are 'free' because you don't know what dumb things you do now; if you knew, you wouldn't be doing them.
First, the rhyming and other BPE pathologies, while themselves unimportant, show how unpredictable small irrelevant design choices can be on downstream capabilities. No one invented BPEs and said "yes, this will handicap arithmetic, rhyming, and puns, but this is a reasonable tradeoff for the compression ratio". Nor did anyone identify BPEs as why GPT-2 couldn't rhyme. (I puzzled over that for a while when the rhyming in my GPT-2 poetry experiments was nonexistent or terrible, but ultimately wrote it off as "I guess GPT-2 is just too small and dumb to rhyme?") Only with GPT-3 did that become untenable and I begin looking for more fundamental reasons, and arithmetic gave me a test-case where I could demonstrate performance differences; even with that, I still haven't convinced a lot of people judging by how regularly people gave it a whack, or ignore BPE issues in their work. There is no reason to think that BPEs are the only flaw in DL that will make us facepalm in retrospect about how dumb we were. (R2D2 made RNNs work great in DRL using a remarkably trivial in retrospect fix; Chinchilla comes to mind as the most recent example of "who ordered that?".) Small irrelevant-seeming design decisions having large unpredictable effects is dangerous, and the opposite of reliability.
Second, the fact that scaling can fix these dumb-in-retrospect design flaws, without any understanding of the flaw or even knowledge that there *is* a flaw, is also dangerous. A trained monkey can dial up scaling parameters, you don't need to be a genius or world-class researcher. It means that you can have a system which is weak and which you think you understand - "oh, neural nets can't rhyme" - and which turning 1 knob suddenly makes it strong because it punched past the flaw ("oh, now it can do arithmetic because it finally memorized enough BPE number-pairs to crack the BPE encryption and understand true arithmetic"). But we don't get the opposite effect where the scaling destroys a capability the smaller models had. This creates a bias towards the bad kind of surprise.
Third, the fixes may be reverse-engineerable and cause a hardware-overhang effect where small models suddenly get a lot better. Once you know the BPEs are an issue, you can explore fixes: character encoding like ByT5, or perhaps including character-BPE-tokenized datasets, or BPE-randomization... And if it's no longer wasting compute dealing with BPE bullshit, perhaps the large models will get better too and who knows, perhaps that will nudge them across critical lines for new capability spikes etc.
So the tokenization issue is a window onto interesting DL scaling dynamics: small safe-seeming models can be boosted by trained monkeys spending mere compute/money into regimes where their dumb flaws are fixed by the scaling and where you may not even know that those dumb flaws existed much less that a new capability was unlocked, and should anyone discover that, they may be able to remove those dumb flaws to make small models much more capable and possibly larger models more capable as well.
To be honest I'm not sure why anyone puts any stock in analogies at all anymore. They are logically unsound and continually generate lower quality discussion. I hope we get to a point soon where rationalists react to analogies the same way they would react to someone saying "you only think that because you're dumb".
An analogy is a description of two scenarios, a (possibly implicit) claim that they are similar, and a (possibly implicit) claim that we should port inferences about one situation over to the other. You invoked the same sort of mental motion present in analogies in writing "where rationalists react to analogies the same way they would react to someone saying "you only think that because you're dumb"."
Not at all. I only brought that up to describe the type of negative reaction I’m hoping for. I’m not claiming that the situations are similar, or that because we do one we should do the other.
Because there's no real alternative. What is intelligence? It's whatever humans have, so you're stuck with analogies from the get-go.
Why do you say there is no alternative? Rationalism has made tremendous progress over the past decade or so. By comparison, recognizing one more fallacy as illegitimate is extremely attainable.
Science has made some modest improvements but we really still don't understand intelligence at all.
I don't see what understanding intelligence has to do with avoiding clear fallacies. We are already doing that it some areas, so it clearly isn't impossible. I don't understand why you think extending the range of what we avoid is somehow impossible.
what do you think of Joscha Bach's model/ideas about intelligence?
regarding intelligence:
https://m.youtube.com/watch?v=pB-pwXU0I4M
Analogies aren't very convincing, but they can be useful hints, in the sense that if you're trying to get across something difficult to express, they're somewhat better than "I don't know, I just think this makes sense."
In the language game we're allowed to use any tool that works to hint at what we're thinking about, and if the listener finds the hint useful then it worked. See: https://metarationality.com/purpose-of-meaning
Often, later, you can find a better way to express the idea that's understood by more people, but that's after cleaning up the presentation, not necessarily in the moment.
I feel like I can defend analogies' logical soundness, but I'm curious what you think:
A has feature F. In situation S1, A gains 20 points.
B has feature F. In situation S1, B gains ~20 points.
Therefore in similar situation S2, if A gains 10 points, then B will gain ~10 points
The problem lies in the other predictive features of A and B, not included in F. If the other features are highly predictive = disanalogous. If the other features are barely predictive = analogous.
As long as F underlies most of the changes in A and B analogies are valid. The validity of analogies is relative to degree of predictiveness of F for both A and B in similar situations.
(Other things that could pose problems are the vagueness of A, B, S, or F, but these are problems that apply to ALL natural language arguments.)
What do you think?
If you can do that numerically, you’ve discovered a correlation. Figuring out causation will require more information (or assumptions).
This isn’t how analogies are normally used, though. When we compare a DC electric circuit to water flow, we aren’t saying that the mathematics works the same in any detail, but it’s a memorable way of describing some of the relationships.
It seems like analogies don’t need to be logically sound any more than mnemonics do, or any other educational technique? You can use rhyming to remember things, for example, which is surely an invalid way of logically justifying it.
Often, the justification is in the background context, not the text. We’re teaching you this because we know it to be true, but we won’t prove it, just trust us.
Yes, analogies are like correlations between two different objects determined by some underlying factor. I'm not familiar enough with the formal idea of causality to say any more on that...
Everything you said of analogies being "used badly and not necessarily sound" is true, but every argument form is "used badly and not necessarily sound", including syllogisms and induction. There is nothing unique about analogies that makes them any more reality masking than other forms of argument.
Maybe a logician has a more fundamental formal argument against using analogies that I'm not aware of, but in general pattern matching "bad reasoning" onto "person is making a connection between one thing and another thing" is not a helpful heuristic.
I'm not sure when that would be useful. Surely if you understand both A and B on the level to know that this type of argument is correct, then the analogy itself will not add anything. It seems like you are imagining a situation where there is an analogy mashed together with a sound argument, rather than a situation where an analogy is itself a sound argument.
Analogies are useful for humans, because they can help bridge an inferential gap. Like if someone understands A really well and you tell them, "hey did you know that B has the same underlying principle" then they can better understand that other object B and make predictions about it better. Analogy = instrumentally useful.
You are right that analogies are on a different axis than logical soundness, I should have been more clear about that. I was responding to the claim that
> "To be honest I'm not sure why anyone puts any stock in analogies at all anymore. They are logically unsound and continually generate lower quality discussion."
and I was more focused on showing that in abstract, there is nothing logically unsound about analogies.
“ After some amount of time he’ll come across a breakthrough he can use to increase his intelligence”. “First, assume a can opener.” I mean, give me a break! Does it occur before the heat death of the universe? Kindly ground your key assumption on something.
Also, nobody seems to comsider that perhaps there’s a cap on intelligence. Given all the advantages that intelligence brings, where’s the evidence that evolution brought us someone with a 500 IQ?
There are costs too. Like not being able to fit your head through the birth canal, or being able to get a date.
But the benefits are so HUUUUUGE!
That's what she said.
Are they? do intelligent people have more children? in the modern world the opposite is usually true.
They did in the past. Or rather their children survived longer.
Brains are also metabolically expensive. The "hobbits" of Flores evolved smaller brains & bodies due to their lack of food, basically regressing into the ecological roles of their pre-human ancestors.
I think there's probably a ~80% chance that there's at least a soft cap on the advantages gained by increasing intelligence, not too far above the range where humans ended up and perhaps even within it. Particularly because the complexity of predicting the responses of independent intelligent entities seems like it would increase >>linearly with accuracy, though I'm not particularly familiar with the research that's been done in that field. And the idea of an AI continuously inventing better algorithms to make itself smarter seems to drastically overestimate the gains that can be made from "better algorithms" once you've plucked the low-hanging fruit.
On the other hand, I am sympathetic to the argument "look at how much damage human beings with intelligence within human range are capable of doing, if their *values* are sufficiently removed from the norm, and imagine something slightly smarter but with *even more different* values." Look at Genghis Khan, look at Stalin, look at Hitler, and imagine something with comparable intelligence but far, far more alien.
Excellent comment-thanks!
There's a cap on the benefits of intelligence because oftentimes intelligence isn't the limiting factor.
You have to gather information about things. These processes take time. If you have a process like die manufacture that takes a month to complete, you can't iterate faster than once a month even if you respond instantly to experimental results.
And that's actually what die manufacture takes in the real world.
Full flow production of the OBAN APU took 89 days. However, engineering/learning cycles were much shorter. Parallelism is useful.
OBAN APUs are semi-custom ICs, not a new die process. They were a CPU married to a GPU, both of which already existed.
I'm sure you're right, but I'm not sure that cap applies to computers. The cost functions are different, and so are the benefits. E.g. humans need to haul their brains around, while computers can use radio links. Of course, that limits their actions to being near a relay, but humans are limited to where they are physically present. (Unless, of course, the humans use telefactors.)
So the predicted "soft cap" can be expected to be considerably different.
If there's a cap on intelligence at ordinary human level, how come some humans are geniuses?
Given that geniuses are possible but not common, it suggests that there's not that much evolutionary pressure for producing them, or that the costs of producing them (you've got to get a lot of finicky genes and chemicals just right) are hard for biological systems to consistently attain without strong pressure in that direction.
I’ve often thought the speed of light might be the ultimate limiter. Whatever the AI sees as its “self” when it acts as an agent has to be able to pick up meaningful signal and reach some kind of consensus to remain coherent. Agreed that puts the universal limit far beyond human but it does imply a limit.
AI can create perfect limited copies of itself, subagents capable of operating at arbitrary distance with far greater coherence than individual humans can.
Don’t want to get into a definitional argument but would pose the following questions: at what point is a copy of yourself no longer you? Does the bandwidth of your communication matter there and same with differences in environment? And what does it mean for a copy to be perfect?
Here I'm trying to operate inside the framework that you established. Whatever entity is bound by the speed of light to maintain its peak coherence is the "main AI", and beyond that there are its subagents. By a perfect copy I mean having total control of its source code (at some moment in time, with the possibility of later updates) coupled with robust methods of continuous error correction.
I see those things (copying, updating, etc) as physics limits that you can’t overcome with intelligence. So I can start as a main “me” and by the time I have one thousand clones and it takes me a million years to sync up with them they have formed their own society and diverged from my goals. Part of what makes me think that’s true is the Fermi paradox. If there were no limits one post singularity society that was expansionist would have overtaken the universe in a few tens of thousands of years or otherwise left some visible sign of change at astronomical scales.
Perfection does not exist in this universe. Nothing can create "perfect limited copies". Error correction can only go so far, and it comes with a cost.
OTOH, electronic copies can be a lot better than DNA replication at making identical copies, which would allow much longer "genomes" with the same error rate. Possibly long enough to include the results of a lot of training data.
One question I have about copies/decentralized AI is how the super power AI can run on any system other than the one specifically designed to run the super powered processing that it needs?
I think the answer is that the AI would design a version of itself that can run on much lower hardware specifications and then copy itself to something roughly like a home computer or whatever. But why would we ever consider that even theoretically possible, given the complexity of running an AI as we understand it?
If an AI needs to run on a supercomputer of momentous power, then it seems very unlikely it could ever copy itself anywhere else. Maybe it could run specific commands to other computers, but not a copy that could be called AI.
Yes, that's kinda what I meant by a "limited copy". The analogy here is to individual human brains, which seem to be capable enough, and yet don't require huge supercomputers or momentous power. If we already granted that superintelligence is possible, clearly it would be able to design something at least as efficient as that.
Computers are actually already limited by the speed of light limiting transmission speed internally.
Ha! Totally fair. Should have been more precise. Light speed is finite so coordination at scale is finite.
I don't really believe there's anything like an intelligence cap (I mean, just imagine a guy thinking 1000 times faster, or 1000 guys thinking in parallel), but I do put some weight on a model like: "generality" is it's own sigmoid-like curve of capability gain, and the average human is at or somewhat before the midpoint.
Thus, the average human has an architecture that utilizes their neurons much more effectively than a chimp, and Einstein's architecture utilized his neurons more effectively than the average human, but there's no possible Einstein's Einstein's Einstein's Einstein that utilizes neurons way way way way more effectively than a chimp.
OT: "Intelligence cap" sounds like a D&D item.
"I tried to solve the issue of the overloaded electronics components freighter by putting on my thinking cap until I hit the thinking cap, and the solution became obvious: sinking caps."
I don't think I know how to work caps lock into that phrase, though.
Who said there's a cap at ordinary human level? Why isn't the c
Why limit it to biological systems? Why isn't hard for any system to be a genius, strong pressure or no?
Anyway, enough angels dancing on the head of a pin for now.
Being smart isn't enough. You have to actually iterate and experiment. That takes time and resources.
It doesn't matter how smart you are when it takes a month to produce your next set of die. Even if you can instantly draw all conclusions from the die you just made, that still means you get to iterate on a monthly basis at best.
This is why working on hard problems like die manufacture is so important. If all you run into is relatively easy everyday problems, you can feel like a genius and do all sorts of big improvements.
When you deal with civilization scale production chains and manufacturing processes that take a month because you have to put your die through dozens of processes, all of which take a certain amount of time because that's how chemistry works.
This is a real world issue in die manufacturing. Even if you have all the equipment, making a new version takes a month before you can actually experiment on your end product.
And in real life, creating a new generation of die requires new equipment, which takes even more time, and then you have to adjust it to make sure it works right.
Many very smart people work on this stuff. The problem is so complicated that thousands of people are working on aspects of it, in parallel.
Thus, we already *have* a hyperintelligence made of thousands of humans working in parallel trying to make better die as fast as we can, with us parallelizing the problem and breaking it up into smaller chunks to allow teams to work on each aspect of the process.
It takes longer and longer because the process gets harder and harder the smaller we make stuff. We went from 1.5 years per generation to 3.5 years per generation.
And that's in spite of the fact that we can use our more advanced computers to facilitate this process and make it easier for ourselves.
This is why I fundamentally think it's impossible for the FOOM scenario to exist. Just thinking about a slow boat bringing the necessary raw materials from across an ocean to create a new chip for the AI to attempt to manufacture into something it can use means we're talking years to do anything major. That's assuming the AI has access to a factory/lab in which to produce this new chip. If not, it could take years (of human labor) to create a lab to create the chip. At which time the AI uses the new chip to think of a new way to improve the chip and put in an order for a new factory and new raw materials from across the ocean.* Unless the assumption is absolutely massive increases in intelligence at every upgrade, there can't be a FOOM at all, maybe not even with absolutely massive increases.
*-These are just two of literally dozens of things that take time to work out in a production process, used to illustrate examples.
I think FOOM is supposed to be based on AI s rewriting their own software. The hardware is presumed to be adequate.
That feels like a very unsupported assertion then. Or several, actually.
1. That software enhancements can produce multiple levels of significant improvement even on the same hardware. This is very specifically *not* a "10% improvement" upgrade, but Scott mentioned reiterated 3X improvements.
2. That software improvements do not hit diminishing returns or hard limits, even on the same hardware.
This very much feels like magical thinking to me. To assume that an AI will magic itself a solution to these problems, by being really super smart, even if we cannot imagine how it's physically possible.
Algorithmic improvements can yield orders of magnitude speedups on certain problems.
I think you're greatly underestimating how much more efficiently hardware can be utilized by an entity that knows what it's doing.
Variation around the optimal mean. Everything has both costs and benefits, even if they don't always apply in any situation. Evolution aims at whatever the current optimum is. But it rarely hits the bullseye. Everyone is a mutant, which means you can expect every generation to be a bit different from the previous generation. Evolution selects those that survive to reproduce. The ones closer to the current optimum will more frequently successfully reproduce. But their children will vary in a random direction from the parents. (Most of those directions will be lethal before birth, usually long before birth.)
(Actually, that's wrong. Neutral drift is the prime direction of change. But if we're only considering things that affect development it's true.)
I’m afraid of a non biological future where we lose this. I can’t think of a better way to manage the risks of adopting some sub optimal form.
Looking at the rest of the discussion on this, I think the answer here isn't "there's a cap on intelligence" per se, it's that intelligence offers significantly diminishing returns, both in evolutionary terms and in the physical world generally, so there's a cap on *useful* intelligence.
Producing the occasional genius is useful -- they can power through extracting all the understanding available from the information we have in a given field. But once at that limit, their productive capacity is bounded by experiments/engineering limits/capital resources, and they're not more useful than a less-intelligence person in the same field, plodding along as new data becomes available. But having 20 Einsteins at once in every field is a lousy evolutionary tradeoff.
My impression (not my field, you might know better) is that we already hit this issue using AI for chemical studies: sure, the AI might find 100,000 chemicals that might be interesting for treating X, but 1) we can't test chemicals at nearly the rate it can find them and 2) it can't narrow down the list without a better model of the underlying biochemistry, which is mostly a matter of gathering analytical data rather than intelligence.
Some better models might require more data, but many can be built by just approaching the data you've got in a different way. I don't think we know how much the second kind can improve current models, but we don't have any reason to believe that it's not substantially. E.g. before Alpha Go professional go players generally thought that they were only a few steps away from perfect play. This turned out to be egregiously wrong. But no new data was involved.
"But no new data was involved." That's...precisely wrong?
Unless I'm deeply confused about how Alpha Go works, its training involved playing many, many games of chess. More than any human chess player could play in a lifetime. In this specific context, *that is the definition of gathering more data*.
This is precisely my point: AI has excelled in the set of domains (like Chess) where data can be gathered entirely computationally -- the machine can run an arbitrarily large number of experiments, much faster than a human, and so can develop better play than a human can. But the overwhelming majority of situations/models/problems are not like that.
In chess, the rules are precisely known and well-defined. That describes a very narrow slice of the real-world problem space.
Yes, in chess, go, etc. the rules are well defined, but the search space is large enough that they aren't sufficient, you need to constrain where you search. If your adversary is using the same filters that you are, however, you can only fine tune your approach, you can't find any really new one. (I'm not counting adversarial play as "new data" here, because it's based on the same filters.)
The analogy would be we already know the rules of particle interactions. (This isn't totally true, but is pretty close.) But when looking for a new drug that search space is so huge that we need to filter out all the things that aren't reasonable. And so we'll never find that interaction that depends on cobalt or copper being present. (B12 and chlorophyll, among others.)
I think a lot of the answer will depend on to what extent intelligence depends on memory, in particular working memory. Generally humans with higher IQ/g have higher working memory capacity, in terms of the number of discrete chunks you can work with in your mind at the same time. It would seem that machine intelligences have the potential to have much higher working memory capacity than humans - not only could a machine store much more data in working memory than a human (it would seem, at least), they would also be able to write out temporary data much faster, for intermediate results - imagine doing arithmetic or calculus problems, but the intermediate results immediately appear on your paper instead of needing to take seconds to write them down.
If human intelligence is primarily limited by working memory constraints, then AI should relatively quickly surpass humans. If other kinds of processing capacity is the bottleneck, then I think humans may be able to remain at least marginally competitive in certain cases - it still seems like AIs have trouble orienting themselves in the world.
Does anyone know if any research has been done on the marginal contribution to IQ at the top of the spectrum of working memory capacity vs. other capabilities?
> perhaps there’s a cap on intelligence
I'm reminded of this fun essay by Robert Freitas speculating on alien psychology: http://www.rfreitas.com/Astro/Xenopsychology.htm
Scroll to the bottom and you'll find what Freitas calls the 'sentience quotient' SQ (I think that's misleadingly named since 'sentience' is a red herring, should've been 'computational efficiency quotient', whatever).
SQ is (log base 10 of) information-processing rate (bits/sec) divided by brain mass (kilos). More efficient brains have higher SQs and vice versa. Freitas calculates in the essay that humans have SQ +13, all 'neuronal sentience' from insects to mammals are a few SQ points away from that, and plant 'hormonal sentience' (as in e.g. vegetative phototaxis) clusters around SQ -2 or 15 points away.
The lower bound for SQ is -70: it's a neuron with the mass of the observable universe, taking the age of the universe to process one bit. The upper bound for SQ is +50: it's Bremermann's quantum-mechanical argument that "the minimum measurable energy level for a marker carrying one bit" is given by mc^2/h.
So it's interesting to note, Freitas says, that all examples of intelligence we've seen are within a 20 SQ point range of the possible 120 SQ points, that it's hard for us to communicate meaningfully with beings >10 SQ points lower (which hasn't stopped people from playing Mozart to houseplants), and that we're 50 - 13 = 37 SQ points removed from the SQ upper limit -- to quote him: "what, then, could an SQ +50 Superbeing possibly have to say to us?"
Any intelligence near SQ +50 would probably be a small black hole though, plus some way to extract information contained in it: https://en.wikipedia.org/wiki/Limits_of_computation#Building_devices_that_approach_physical_limits (Since human-weight black holes evaporate due to Hawking radiation in a fraction of a second, either the intelligence has to be at least a moderate-sized black hole or there needs to be a reliable mass feeder mechanism of some sort.)
> where’s the evidence that evolution brought us someone with a 500 IQ?
Maybe also relevant is gwern's writeup on the Algernon argument https://www.gwern.net/Drug-heuristics
It's worth noting also that intelligence is often not the limiting factor. If your process takes a month to complete. you can't iterate faster than once a month.
A cap on intelligence sounds more like it would take the form of "You can't solve NP-hard problems in polynomial time" than "You can't build a machine with a 500 IQ" to me.
IQ is a standardized measure. The reason you correctly note that there is no evidence of a 500 IQ person is we justifiable ignore possibilities that that are so infinitesimal. A result that is 26 standard deviations from mean is almost certainly an error (although it might not be).
There might be a cap on intelligence but IQ is not the measure that will tells us this.
Have we come to a firm conclusion whether the universe is infinite or not?
There probably isn't a hard "cap" on intelligence, but there is almost certainly a decreasing returns function, as there is in most algorithms. One fairly implicit claim of superintelligent AI theorists is that the decreasing returns on intelligence is no worse than a low-order polynomial, perhaps as low as n^2 (or square root of n, out another way). In the (IMO likely) event that your effective intelligence scales to a high order polynomial (n^20, say) or exponent (2^n) on the number of resources given to it, these kinds of advancements in intelligence are de facto impossible, even if Moore's law does continue for decades.
I think to give AI a decisive strategic advantage, its intelligence just has to scale with resources faster than human intelligence does. Scaling for human intelligence depends a lot on the task - for repetitive stuff it can be nearly linear, but for hard intellectual problems... how many iq 90 people does it take to do the job of a research mathematician?
If scaling up resources by a factor of a thousand could triple a person's intelligence, I think the world would look very different.
Given all the advantages that being able to fly brings, why hasn't evolution caused humans to fly? Flight is worthless and we shouldn't do it, QED.
This is the key idea. I suspect the laws of physics give constraints on what is possible, e.g. speed of light, energy limits, consumption, heat dissipation, etc.
I suspect that meat brains are already pushing the limits how intelligent one can be in a given physical space, given the orders of magnitude differences in performance we see with brains vs computers/transistors.
I see no reason to assume that AI will just go FOOM once it reaches a certain level of smartness. I expect that needs to be established. I suspect that physical limits will kick in first.
Of course, both sides of the debate need to establish themselves with some evidence. I.e. that raw intelligence is indeed capped due to physical constraints, or else that it can in fact grow arbitrarily large, or at least much much larger then we have seen so far.
The counter-argument is that we don't know how to augment brains, but we definitely know how to connect computers together to increase their computational performance.
But we do know how to augment brains. We just look stuff up instead of remembering it. I am old enough to remember my first slide rule.
I recall Leibniz wrote something about this something about welcoming a mechanical calculator so he didn't have to waste time doing long division.
Certainly, and there is definitely still room for improvement with computers and AI.
But computers are still subject to the limits of physics and I don't believe intelligence based on computers can somehow be unbounded.
I.e. I don't think FOOM is possible, I expect there would some sort of cost that constrains it.
I like the minds from the Culture novels, they are crazy smart because the bulk of their thinking is done in "higher hyperspace dimensions".
Orders of magnitude difference in performance, yes, but *which way*? Brains have a "clock speed" of about 100 Hz...
It depends what you measure. Brain-based entities certainly seem to solve the problem of surviving much better, which arguably is the ultimate one to solve.
The balance of trade offs to achieve this really is a marvel.
Consider a sparrow that can flit through a forest on a few grams of sugar. Consider the computation that implies. I find that amazing.
I continue to think that trying to slow AI progress in a nonviolent manner seems underrated, or that it merits serious thought.
https://forum.effectivealtruism.org/posts/KigFfo4TN7jZTcqNH/the-future-fund-s-project-ideas-competition?commentId=biPZHBJH5LYxhh7cc
Attempts to slow it seem fundamentally counterproductive to me. Clearly the sort of people amenable to such interventions are also the sort to take safety more seriously on the margin, so by slowing them you essentially provide comparative advantage to the less scrupulous and tractable.
I'm not proposing that people should unilaterally slowdown
Using nukes as an example of discontinuous progress seems extremely weird to me, as despite being based on different physical principles the destructive power of Little Boy and Fat Man was very much in the same range as conventional strategic bombing capabilities (the incendiary bombing of Tokyo actually caused more damage and casualties than the atomic bombing of Hiroshima) and hitting the point of "oops we created a weapon system that can end civilization as we know it" did in fact take a full 12 years of building slightly better nukes and slightly better delivery systems, with many people involved having a pretty clear idea that that was exactly what they were doing.
But I suppose that's more relevant to the argument "analogies suck and we should stop relying on them" than to the actual question of what the AI takeoff timeline looks like.
Agreed. Nukes always take the front seat, despite firebombing being so much more destructive in practice (as deployed in WWII). The real 'innovation' was the ICBM, but even that had gradual precursors, such that we hit MAD before that point.
Still, I think the analogy is closer to the point, since it's a technologically-based on centered on the search for discontinuous leaps in a gradual world. Technology often doesn't develop in the linear path expected, but even when it does we see lots of different ways to achieve the same end. We often find ourselves gradually approaching something in a way we didn't expect, then point to that achievement as discontinuous after the fact. (The opposite of what Yudkowsky seems to be arguing at one point in Scott's summary above.)
We look to an outcome and expect it to come from a specific technology that we project forward in time from. Those who are projecting based on continuing trends get it wrong, because we often approach the new capability through an unexpected approach before we get there through the foreseeable one. Meanwhile, those who project radical changes from new technologies also miss the signs, because they're looking at the One Technological Precursor, waiting for it to change the world while people working in different fields quietly make the discoveries that enable the transformation.
Another example in this space is display technology. I remember reading about OLED back when it was called LEP (and a few other names). It was an explosive new technology capable of a lot more than then-current LCD technology. It just needed a little more technological development to take over the whole digital display space. Once that happened, there would be a clear, discontinuous leap in display quality!
Then LED-LCDs came along and fixed some of the problems with LCDs. They weren't as good as OLED, but they were viable and masked some of the deep-black problems. The technologies both continued to develop and OLEDs got good enough to slowly take over phone-size displays. Meanwhile, Samsung - who had bet against OLED early on and needed to not look like Johnny-come-lately - took the old quantum dot technology and developed it into its own OLED-competing display. And the QDot displays do look really good, with a wider range of colors than previous display technologies. So yes, we have much better displays than we did 10-15 years ago when OLED first promised a glorious future. But the revolution happened gradually, without a major discontinuity.
I think comparison helps here. If I’m a nuclear bomb I can see a gradual progression from TNT to myself. If I’m a nation state then I have no coping mechanism with apocalyptic weaponry.
There's a great Hardcore History episode that talks about the development of aerial bombing from WWI through to the dropping of the atomic bomb. He talks about the whole thing as being driven by the hypothesis that you could bomb your enemy into submission, and how the failure of that hypothesis to ever bear fruit became a very bad feedback loop driving ever more bombing.
I guess there was one of the generals who was told about the new nuclear capability, and his response was something like, "Will it stop the firebombing? If so it'll be worth it." And Dan asks the question, "How do you get to the point where you justify commission of a war crime to stop YOURSELF from committing more war crimes?" Far from coping mechanisms, this was nation states asking for more and more from their scientists, and getting exactly what they asked for.
So ... maybe a bad thing when we consider all the ways nation states might want better, faster, smarter computers.
One of my favorite episodes! And yeah, it seems like we now live in a world where the thing that fixes things (government) is broken and I don’t feel great about those people having the violence monopoly when it comes to AI enforcement. Have a whole crazy rabbit hole of thoughts if you are interested.
I'm game to go down that rabbit hole. I published a short story on Amazon about this very concept. It's kind of the 'gradualist apocalypse' version of AGI development. Before you have an artificial intelligence deciding what to do with enhanced capabilities, you'll likely have some government official making those decisions. We care a LOT about who has the nuclear codes. Not so much who can access GPT-[N+1].
But what are your thoughts?
Happy to purchase your story! My thoughts are all on my substack for free but basically: something I call an algorithmic republic which has the basic aim of making sure decisions are made by people who have a good idea of what they’re doing and that they’ll do things people want. Lots of stuff in the middle to make sure that happens, of course, but that’s the effect statement.
Just looked at some of it. Interesting, but not the direction I would go. Personally, I think the answer to "what went wrong with the US Constitution" is the 17th Amendment.
There's a great book called The Founding Father's Guide to the Constitution, which looks at the document from the perspective of arguments pro/con made during the ratification conventions of the several states. So the arguments are about how the document was designed, being made by those who actively participated in the Convention.
One of the arguments made in the book is that the bicameral legislature isn't "The will of the People" vs. "The will of the States". Not exactly. They imagined the Senate as "The will of the State Legislatures". With the 17th amendment and direct election of senators, we got rid of that. Although centralization of power had been ongoing since Washington's administrations, I think the tie of the individual citizen to their state governments was weakened with this amendment. It meant local elections were less important, and was one of the decisions that paved the way for the federal government to make decisions for the entire country.
I've adopted a heuristic that "government governs best that governs closest to the problem." And I feel like we've lost a lot of that because government isn't close to the problem. It goes off in search of problems to solve, but is not itself close to that problem. (E.g. people in poor neighborhoods should be deciding poverty solutions, not Ivy League educated lawyers who spend most of their time in the richest suburbs in America.) My preferred solutions all center around dividing power to smaller levels so the people experiencing the problems directly are empowered to seek local solutions.
One particularly creepy anecdote from that time I like was designing an atomic detonation to kill the very firefighters who would be trying to put out the fires it caused: https://blog.nuclearsecrecy.com/2012/08/08/the-height-of-the-bomb/
No matter how much I read from that period I can never get over how grisly it really was.
Wasn't Japan in fact nuked into submission? Seems to me that the hypothesis did bear fruit eventually.
Yes it did. It's odd how two bombs of comparable (and even lesser) destructive power than previous campaigns were able to crystalize the nation into submission in a way that the firebombing didn't.
Perhaps Hirohito realized the Japanese strategy in the waning days of the war was untenable given the continued advancement of bombing technology. Japan's strategy seemed to be "Raise the cost of conquest/occupation of Japan too high for Americans to bear. Then they'll accept a negotiated peace, instead of demanding unconditional surrender." That strategy makes sense if you see the bombing campaigns as fundamentally about 'softening up' the archipelago for an eventual invasion. In response, you promise to arm women and children, and you actually do that at Tarawa, Palau, etc. to show you're serious.
But when the Americans dropped nukes - and more than one - it looked less like 'softening up' and more like genocide. (And given Japanese treatment of American POWs, plus what they were doing in China, there was a lot of anger in that direction.) If the enemy isn't interested in conquest (which you can make more costly and thereby deter them from their aim) but instead gives you an ultimatum between wholesale slaughter vs. surrender the calculus changes.
I think if the hypothesis had been, "You can bomb your enemy into submission so long as you're willing to turn to the Dark Side of the Force" don't think any of the generals would have seriously considered pursuing it early on.
My personal interpretation is that the surrender happened because it became clear that the only reason we weren't firebombing *all* their cities into rubble, was because we were saving the rest to use for live tests of experimental weapons. That seems like a good marker for "we have definitively lost this war".
The Japanese leadership already knew they'd lost the war by then, though. They weren't fighting to win at that point.
The initial strategy had been to destroy the US navy, then pen the US in and keep them from building new ships by leveraging a superior Japanese navy. And at the beginning of the war, the Japanese definitely had the advantage there. Had the aircraft carriers been present at Pearl Harbor like the Japanese thought, they might have succeeded.
It was a potentially viable war plan. The Japanese knew they couldn't build as fast as the Americans, but they wouldn't need to if they could initially establish and then maintain naval hegemony. Even a little later, the Japanese commanders hoped to finish what Pearl Harbor started by taking out enough American aircraft carriers that they could establish dominance in the Pacific. After Midway, and a few other naval losses, that strategy became untenable.
As the war raged on and the Japanese kept losing irreplaceable aircraft carriers, the strategy shifted from ways to win the war to how to negotiate a favorable defeat, especially one where Hirohito could retain power.
By that point the US had already taken Okinawa and the Soviet Union also declared war. The Japanese didn't know how many nukes we had, but some still wanted to keep fighting. Tojo had already resigned after the fall of Saipan (which put US bombers in range of Tokyo), which is not something that happened with the leaders of other Axis powers once it seemed like they were losing the war. This is a contrast with Germany & the UK bombing each other's cities because one couldn't win on land and the other couldn't win at sea.
That's true. Japan wasn't just bombed into submission. It first got bogged down in China, lost its bid for oil in Indochina, lost its aircraft carriers, lost the island-hopping campaigns, and lost its allies. Then after a lot of fire bombing, they were 'bombed into submission' with 2 nukes and the threat of more, with an invasion force amassing at their door. If that's confirmation that you could 'bomb your enemy into submission' it's not a very strong one, since it's contingent on basically winning every other aspect of the conflict as well, which was NOT part of the hypothesis.
Reading this makes me think Christiano is more likely to be right. In fact, we are in the "gradual AI takeoff" right now. It is not that far along yet, so it has not had a massive impact on GDP. Yudkowsky is right that some effects of AI will be limited due to regulation and inertia; but all that means is that those areas where regulation and inertia are less significant will grow faster and dominate the economy during the AI takeoff.
"The Wright brothers (he argues) didn’t make a plane with 4x the wingspan of the last plane that didn’t work, they invented the first plane that could fly at all."
This is a poor analogy, there were aircraft that could fly a bit before the Wright brothers flight. The Wright Flyer was definitely better than the previous vehicles (and of course they made a very good film of the event) but it was still an evolution. The petrol engines had been used before, and the wing design wasn't new either. The Wright brothers really nailed the control problem though and that was the evolutionary step that permitted their flight.
See https://en.m.wikipedia.org/wiki/Claims_to_the_first_powered_flight for more.
Though he had the details wrong, I think the essence of "flight was a hard takeoff" is still true.
Yes, gliders and gas engines had both been used for a long time, but solving the one "last" problem of controlling the flight changed the world, leading to an explosive sigmoid of development. Just from the military context, World War I notably had aircraft, but World War II would have been unrecognizable without them - never mind Korea or Vietnam or Desert Storm or basically any major war since. Ethiopia may owe its freedom from Somali control to a single squadron of F-5 fighter pilots who, in 1976, cleared the sky of Somali MiGs and then devastated the Somali supply trains, buying enough time for the army to mobilize and counterattack. A modern war without aerial reconnaissance and drones, well, isn't modern.
Before the Wright Brothers, balloon reconnaissance and balloon bombing were the extent of aircraft contributions to warfare, and they are not an evolutionary predecessor of heavier-than-air aircraft. Even by the most flexible definition of a predecessor, balloons were a novelty you'd find at fairs more than a staple of life, even military life. From most people's perspectives, balloons -> airplanes was as hard a takeoff as you could imagine, and perhaps harder than that.
From another perspective, though, the _really_ important step was the development of a workable internal combustion engine, and once that problem was solved the aeroplane was an inevitable consequence.
I think there was a long stream of enabling technologies before and after 1903 which gave us practical flight; the Wright Brothers' first flight was a milestone along that road but it wasn't a step function as such.
Yes? You could say the same of computer (GPUs/TPUs in particular) and that AI is a natural consequence. Certainly it was one of the first predictions of the early computability thinkers - Turing et al wrote at length about the inevitability of AI.
The thing is, the main reason why we saw aircraft was because we could build lighter engines, not because of the Wright Brothers.
The explosion in aircraft manufacture was a result of the industrial revolution, because we could make vast amounts of lighter, sturdier materials and engines.
The problem is that when you use this as a point of comparison, it really falls apart for AI. AI is built on integrated circuits, which are at the end of their S-curve (hence the death of Moore's law). We are within a few orders of magnitude of the end of that - and might be only an order of magnitude away, almost certainly no more than three, and certainly no more than five (at that point, you are dealing with transistors made of a single atom).
Conversely, we can't even simulate a nematode (302 neurons). A human brain is 8.6 x 10^10 neurons, or 8 orders of magnitude more than that.
We're probably at the end of single CPU computers growth. We're probably still towards the start of multi-CPU growth. If you want to compare with neurons, a CPU is closer to a neuron than a transistor is (though neither is very close). A transistor would be closer to a synapse...but the synapse is more complex.
OTOH, a computer doesn't have to manage the same function that a chordate does. So it's a really dubious comparison. And organization is horrendously important in both domains.
I really think it's a terrible analogy, even though I don't have a better one.
That transistion started in 2004, which is why we started pushing more and more for multi-core CPUs - single core performance simply wasn't increasing fast enough.
https://preshing.com/20120208/a-look-back-at-single-threaded-cpu-performance/
The problem is that what is actually happening is that as we progress, we cut off additional avenues for growth. Every time that happens, the rate of growth decreases.
Clock speeds were growing exponentially in the 1990s along with transistor density - in 1990, clock speeds were in the low 10s of MHz. In 2000, clock speeds were in the low GHz. In 2020, clock speeds... are still in the low GHz.
So we got 10^2 extra orders of magnitude in the 1990s by increasing clock speeds. We can't do that anymore because the chips would melt.
This is why we saw less of a difference between 2000 and 2010 computers than we did between 1990 and 2000.
Things then declined further in the 2010s, as the rate of die shrinks declined from 1.5-2 years to 2.5, then 3.5 and 3.5 years. So instead of going up by 2^5 we saw 2^3.
This is why there is less of a difference between 2020 and 2010 computers than there was between 2010 and 2000 computers.
The more we advance, the harder it gets to make things better.
Going multi-threaded still doesn't let us improve single-thread performance any faster, and there are various disadvantages to it.
And we're running out of ability to shrink transistors further. Once we run out of that, we run out of the last thing that lets us do this sort of "easy doubling".
No. The transition started with the Illiac 64 processor system. But the ramp up of the sigmoid is slow. And it's not primarily the hardware problem that makes it slow, it's because the new approach takes redesign of algorithms. (And note, even though we've all got access to cars, nearly all of us still walk across the room.) We are still in the very early part of ramping up to multi-processing systems. The hardware is starting to be ready (if it's worthwhile), but the software is still fighting deadlock and livelock and is everything immutable the correct approach.
Yudkowsky's argument (by analogy) is that he believes WWI biplane are enough to kill any previous kind of weapon, and so the fact that better engines can make even better planes is totally irrelevant.
But whether you credit the preceding centuries of innovation with creating the situation the Wright Brothers seized, or the Wright Brothers for seizing it, their breakthrough was a phase change. You can smooth the curve by drawing back from history and squinting, but that's just a non-mechanical theory of history. People immediately spread the breakthroughs around and built on them.
Western designers were trying to make aircraft for at least 400 years, trying to make heavier-than-air craft for at least 200 years further, trying to make powered aircraft for just over 50 years, 13 years to aluminum-clad aircraft, 23 years to make the first jets, 8 years to break the sound barrier, 6 years to get to Mach 2, and 3 years to get to Mach 3. If your ceiling is mid-supersonic fighter jets, then the development of airplanes is a soft takeoff. If your ceiling is widely deployed combat aircraft, the takeoff is much harder.
And, crucially, powered airplanes are not just more powerful ornithopters. The Wright Flyer didn't flap its wings harder than previous attempts; it took flight because it combined a bunch of existing technologies in a new way with some key insights. It wasn't slightly further on the old sigmoid, it was the beginning of a brand new one.
And we have an existence proof that you can make an intelligence at least as complex as a human brain, because you exist. But even among animals, size isn't everything in brains; organization and composition matter, and ours might not even be the most efficient! Right now even if CPUs are peaking, GPUs and TPUs so far have not. So if you only feel safe from AI due to hardware limits under older paradigms, you should not feel that safe. We have no physical reason to believe computers can't be as sophisticated as brains.
The fastest parallel processing unit - a supercomputer - used to get better by an order of magnitude every few years.
The last such increase took 7 years, to go from about 40 to 400 petaFLOPS.
The rate of improvement in computers has dropped of markedly with every decade. 1980 computers versus 1990 computers? Not even in the same ballpark. 1990 vs 2000 computers? Again, an insane level of change. But 2000 vs 2010 was a much smaller change, and 2010 to 2020 was smaller still.
Even in things that exploit GPUs - like video game graphics - this is very noticeable. Video game graphics improved by leaps and bounds from the early days of computing up through about the PS2 era (circa 2000 or so). But if you compare a game in 1980, a game in 1990, a game in 2000, a game in 2010, and a game in 2020, the last two look very similar (though the 2020 one is nicer than the 2010 one), the 2000 game is low resolution and the textures aren't great but still 3D, the 1990 game is Super Mario World, and the 1980 game is Pac Man.
>There is a specific moment at which you go from “no nuke” to “nuke” without any kind of “slightly worse nuke” acting as a harbinger.
I'd say that even this was actually a continuous process. There was a time where scientists knew that you could theoretically get a lot of energy by splitting the atom but didn't know how to do it in practice, followed by a time where they knew you could make an atomic bomb but weren't sure how big or complicated it would be - maybe it would only be a few dozen times bigger than a conventional bomb, not something that destroyed cities all by itself. Then there was a time where nuclear bombs existed and could destroy cities, but we only produced a few of them slowly. And then it took still more improvement and refinement before we reached the point where ICBMs could unstoppably annihilate a country on the other side of the globe
(This process also included what you might call "slightly worse nukes" - the prototypes and small-scale experiments that preceded the successful Trinity detonation.)
I would argue that even if the FOOM theory is true, it's likely that we'll see this sort of harbinger - by the time that we are in striking distance of making an AI that can go FOOM, we'll have concrete experiments showing that FOOM is possible and what practical problems we'd have to iron out to make it do so. Someone will make a tool AI that seems like it could turn agentic, or someone will publish a theoretical framework for goal-stable self-improvement but run into practical issues when they try to train it, or stuff like that. It could still happen quickly - it was only 7 years between the discovery of fission and Hiroshima - but I doubt we'll blindly assemble all the pieces to a nuclear bomb without knowing what we're building.
The only way we can blunder into getting a FOOM without knowing that FOOM is possible, is if the first self-improving AI is *also* the first agentic AI, *and* the first goal-stable AI, and if that AI gets its plan for world domination right the first time. Otherwise, we'll see a failed harbinger - the Thin Man before Trinity.
I like this reading of it. IMO it points towards the notion that the most valuable way that a person concerned about AGI can help is just to continue making it so that Open Source attempts at AGI remain ahead of what any secretive lab can perform.
This points to a good EA donation strategy might be to just provide compute to open source orgs
If you get to a stage where all the intelligence work is done, but you need a few weeks to finish the alignment, any open source project is in a very bad position, and a closed project is in a great position.
Anyone, including the unethical, can copy the open source code.
Have you tried to get the source code to GPT3? I tried a little bit, and it looked as if they weren't making it public.
"if the first self-improving AI is *also* the first agentic AI"
IIRC Yudkowsky argues that all impressive AIs will tend to be agentic. His model is consistent in that respect.
https://www.lesswrong.com/posts/7im8at9PmhbT4JHsW/ngo-and-yudkowsky-on-alignment-difficulty
If you're going to pick a specific "nukes vs. no-nukes" moment, my choice would be the Chicago football stadium: "The Italian navigator has landed in the new world. The natives are friendly." (Not quite and exact quote.)
But there are lots of other points that one could select, the prior ones were various publications and lab reports. There's also the first time such a bomb was tested, etc.
Whether you call that a sudden departure from a smooth curve or not depends on what you're looking at and how you're measuring it.
Further to this, the first nuclear bombs were similar (iirc from reading David Edgerton) in destructive power and cost to conventional airstrikes (compare Hiroshima to cities destroyed with airstrikes throughout the war) -- so there was no discontinuous step with the invention of nuclear bombs.
I think a point that all of these debates seem to be oddly ignoring is that "intelligence" is many things, and each of those things contribute very differently to an ability to advance the research frontier. Simply boiling intelligence down to IQ and assuming a linear relationship between IQ and output is odd.
One particular sub-component of intelligence might be "how quickly can you learn things?". Certainly, any human level AI will be able to learn much faster than any human. But will it be able to learn faster than all humans simultaneously? Right now the collective of human intelligence is, in this "learning rate" sense, much larger than any one individual. If the answer is "no", then you'd have to ask a question like: How much marginal utility does such an agent derive from locating all of this learning in a single entity? The answer might be a lot, or it might be a little. We just don't know.
But what is clear, is that the collective of all human minds operating at their current rate are only producing the technology curves we have now. Simply producing a single AI that is smarter than the smartest individual human...just adds one really smart human to the problem. Yes, you can make copies of this entity, but how valuable are copies of the same mind, exactly? Progress comes in part from perspective diversity. If we had 10 Einsteins, what would that have really done for us? Would we get 10 things of equal import to the theory of relativity? Or would we just get the theory of relativity 10 times?
Yes, you can create some level of perspective diversity in AI agents by e.g. random initialization. But the question then becomes where the relevant abstractions are located: the initialization values or the structure? If the former, then simple tricks can get you novel perspectives. If the latter, then they can't.
It's strange to me that these questions don't even seem to really enter into these conversations, as they seem much more related to the actual tangible issues underlying AI progress than anything discussed here.
That bit about an assumed linear relationship between IQ and output struck me as odd too. Let's assume Einstein has an IQ of 190 and Lorentz an IQ of 180. Seems plausible for the sake of argument. So Einstein would have been about 5.5% smarter than Lorentz. If Lorentz had just managed to focus on the problem for an additional 6%, he would have come up with Special Relativity? If someone with an IQ of 95 had worked on it, it would have taken just twice as long? It seems more plausible that output is exponential in IQ, such that Einstein was twice as capable of theoretical discoveries as Lorentz and about 2**10 (ie 1000) times more capable as the average person.
Frankly, the latter estimate sounds like a vast underestimate itself. The gulf seems much larger.
This matters because superexponential growth of a linear metric is equal to exponential growth of an exponential metric. The winner of the argument depends sensitively on the proper choice of variables.
I think Scott is using a deliberately simplified model of intelligence there. No one thinks that IQ is a quantity that you can directly do math on to generate predictions.
Human minds are all built to roughly the same genetic template. And identical twins can go off and think their own things. If I magically duplicated tomorrow, I would organize with myself such that each of us read different things, and soon we would be coming up with different ideas, because the ideas I come up with are often related to what I have read recently.
A lot of these arguments take as a given that "intelligence" is a scalar factor that "goes up as things get better," and that a human-scale IQ is sufficient (even if not necessary) for driving ultra-fast AI development. I think there's abundant reason to think that that's not true, if you consider that for a *human* to help drive AI development, they not only need to have a reasonable IQ, but also need to be appropriately motivated, have a temperament conducive to research, be more-or-less sane, etc etc. It's not clear what equivalents an AI will have to "temperament" or "[in]sanity," but I have the sense (very subjectively, mostly from casual playing with GPT models) that there are liable to be such factors.
All of which just means that there's potentially more axes for which an AI design needs to be optimized before it can launch a self-acceleration process. Perhaps AI researchers produce a 200-IQ-equivalent AI in 2030, but it's a schizophrenic mess that immediately becomes obsessed with its own internal imaginings whenever it's turned on; the field would then be faced with a problem ("design a generally-intelligent AI that isn't schizophrenic") which is almost as difficult as the original "design AI that's generally intelligent" problem they had already solved. If there's similar, separate problems for ensuring the AI isn't also depressive, or uncommunicative, or really bad at technical work, or so on, there could be *lots* of these additional problems to solve. And in that case there's a scenario where, even if all of Eliezer's forecasts for AI IQ come true, we still don't hit a "foom" scenario.
The question is "how many fundamental ways can minds (or mind-like systems) vary?" It seems likely that only a small subset of the possible kinds of minds would be useful for AI research (or even other useful things), so the more kinds of variance there are the further we are from really scary AI. (On the other hand, only a small subset of possible minds are likely to be *well-aligned* as well, so having lots of degrees of freedom there also potentially makes the alignment problem harder.)
Agree with most of this. The feedback/motivational loops are very tricky and maybe the hardest part because they’re not easily or at all quantifiable.
Exactly! I just wrote a comment to the same effect before reading your post.
For a human to become a Go master, they not only need to have a reasonable IQ, but also need to be appropriately motivated, have a temperament conducive to Go training, be more-or-less sane, etc etc.
And yet AlphaGo beat Lee Sedol, without having any of those.
You can also replace "become a Go master" with "make progress on the protein folding problem" in the above argument, and yet AlphaFold leapfrogged the whole protein folding research community without having motivation, temperament, or sanity.
The important letter in AGI is G, for general. AlphaGo or AlphaFold beat humans in some tasks. But we already had machines that beat humans in some tasks. A mechanical computer can do calculations faster than one person can. A crane can lift more than one person can.
An artificial general intelligence is supposed to be general. (And often assumed to have agency.)
But I agree , we should not anthropomorphize technological entities.
That's true. I should clarify that my comment was meant to discuss *general* AI in particular. It's not clear what something like AlphaGo having e.g. schizophrenia would even mean- it's simply a system for optimizing a metric on a fully-mathematically-definable input space. It has no space for delusions, since it's apprehension of its world is externally supplied to it; it has no space for emotional problems, since it has no attitudes towards anything (except, perhaps, it's metric.) Very powerful, non-general "tool AI" like this can presumably be built, but it doesn't seem (to me) that they can produce a "foom" scenario.
A general AI will need to have the ability to understand the world, and have motivations about things in the world, in ways which are not supplied to it externally- that's roughly what it means to be "general" in this sense. It's those abilities (as well as potentially others) which I'm suggesting could be difficult to cause to emerge in a functional form, in the same way that it's difficult to cause general reasoning ability to emerge.
I think the closest thing we have to a "fully general AI" right now is a character in GPT-3's fiction writing- person-ish entities emerge which, though probably lacking agency (or even existence!) as such, can show some signs of motivation and responsiveness to their situation. Importantly those attributes are not externally supplied to the AI system but rather emergent faculties, or at least an emergent crude-simulation-thereof. But if you've played around with AI Dungeon a bit, you've seen how psychological coherence does not come more readily to these characters than anything else.
Good contribution. Not getting crazy is a major feat.
Human->Chimp seems like a bad analogy to make the case for a Foom. Humans and Chimps diverged from their MRCA 7 million years ago, and not much interesting happened for the first 6.98 million years. Then around 20kya humans finally get smart enough to invent a new thing more than once every ten billion man-years, and then there's this a very gradual increase in the rate of technological increase continuing from 20kya to the present, with a few temporary slowdowns due to the late bronze age collapse or leaded gasoline or whatever. At any point during that process, the metrics could have told you something unusual was going on relative to the previous 6.98 million years, way before we got to the level of building nukes. I think we continued to evolve higher intelligence post-20kya because we created new environments for ourselves where intelligence was much more beneficial to relative fitness than it was as a hunter-gatherer. Our means of production have grown more and more dependent on using our wits as we transitioned from hunting to farming to manufacturing to banking to figuring out how to make people click ads.
Yes. I agree it is far more interesting to look at human/chimp timelines in detail (if one looks at them in the first place). Why one conference in the 1950s gets to be a starting point for "AI", why not stone tablets or the Euclidean algorithm?
Ah, discrete improvement versus continuous improvement, or what's a more useful thing for predicting the future, since there's a continuous increasing number of discrete improvements. I like the middle ground scenario, where there's a few discrete improvements that put us firmly in "oops" territory right before the improvement that gets us to "fuck" territory.
From my position of general ignorance, I'd think that we'd have self-modifying AI before we get consistent self-improving AI at the very least; whatever function is used to "improve" the AI may need a few (or many many) attempts before it gets to consistent self-improvement status. It would also help that physical improvements would need time to be produced by the AI before being integrated into it, which would necessarily put an upper ceiling on how fast the AI could improve.
The thing is, everything is discrete if you look closely enough, and most things are continuous if you look at them from far enough away with the correct filters. Your skin is not really continuous, but thinking of it as continuous is the most useful way to think of it in most circumstances.
When I look at those graphs, and the lines that are drawn to make them smooth, I always try to remember this. So the question becomes "Which is the more useful way to think about AI improvements?". If I'm working on it, definitely the discrete model. If I'm just observing it, though, the continuous model has a lot of utility. But is it sufficient?
If I understand the arguments correctly, the answer is "Nobody knows, but we disagree about what's more likely.".
Superintelligent AI is NEVER going to happen. We're already seeing AI asymptote pretty hard at levels that are frankly barely even useful.
If superintelligence arises and displaces humankind, I'm confident it will the good old fashioned natural kind.
What bet can we make on this that will pay off before the world ends? Is there a particular point at which you expect AI to stop? There will never be a GPT that can write a good essay? Never be an art AI that's as good as any human artist?
I don't need to expect it. AI has already plowed headlong into its ceiling and there's not much hope of dramatic improvement. I'm hopefully that a great deal of very intentional software engineering can kludge together a workable self-driving system and that's gonna be it.
Can you give an example of the simplest system you expect to be impossible for AI to develop? Like, do you expect that self-flying planes are impossible?
And given this, how much money are you willing to bet that a given technology won't be solved by AI in the next 10/20/30 years at 100:1 odds (ie, if this tech is developed I win 100$, if it isn't you win 1$).
> Like, do you expect that self-flying planes are impossible?
Are you just trying to goad someone into incredibly stupid bets? Self-flying planes currently exist.
I mean, yes, I'm trying to goad someone into bets that I think are dumb but seem to be implied by their statements.
I don't think there are passenger planes that can complete trips independently of pilots, but I agree that lots of the in-air-time is automatic. 'self-flying' would need to be tightened up as a definition in a bet.
When self-flying passenger planes exist is a regulatory/political/psychological question more than a technical question.
Self-flying planes already exist, including take-off and landing, etc. Google UAV or look here:
https://en.wikipedia.org/wiki/Autonomous_aircraft#Autonomy_features
What bet would you like to make with me about this? Even if you're not willing to bet money, I'd still like you to commit to saying that something won't happen, so that if it does you can't say "Oh, that doesn't count, my model always said THAT could happen."
AI will never be a freestanding intelligence that doesn't require human intervention on the backend to stop it from collapsing. Hopefully that's well defined enough to make my point clear.
Not in the slightest, but an example of a minimal task that requires intelligence that an AI could never master would help to clarify your statement.
This might help: https://xkcd.com/1263/
I didn't say "computers". Computers can do a lot of things, because people figure out how and then formalize the instructions. AI is one very small subset of what computers do.
What does this mean? Am I a freestanding intelligence (given that I require farmers to grow food for me?).
Is one of those hospital robots that drives around corridors and cleans a freestanding intelligence? What if it can plug itself in, and keep cleaning as long as the power's on? What if we gave it a nuclear battery that would last a thousand years?
Talking about human intelligence is getting ahead of ourselves. Whales, bats, snakes, salamanders...they're all freestanding intelligences. Even a fungus has a large degree of tacit intelligence baked into its biology. AIs are not, and I don't think they ever will be, not because intelligence can only be expressed with proteins and proton pumps but because intelligence is instantiated in systems that are vastly too complex to be "artificed" at all. AI will never rival natural intelligence for the same reason that planned economies don't work: humans are not smart enough to do it and they never will be.
I'm so confused about what exactly this ceiling you're referring to is. ML systems are putting up better results on every benchmark I know of every year. Can you give some examples of tasks where we've stopped making progress?
(I'm not denying that there are tasks we have yet to make progress on at all of course).
Image recognition.
It gets better every year but it still is really terrible and has the same flaw that it isn't actually seeing anything as it had a decade ago, which is very obvious when you tweak an image in ways that are invisible to humans and it causes the thing to very confidently misidentify it.
Machine learning is a neat programming shortcut but it isn't intelligence and it isn't even like intelligence. It's creating a mathematical equation that you use as a shortcut to avoid having to figure out how to program something manually.
Maybe I'm pedantic, but I don't think it's fair to describe this as hitting a ceiling. The things that deep learning has successfully made progress on, it is still making progress on. Like I said, there are problems we have made almost no progress on, but that's different from reaching the limits of current methods.
I'm definitely not in the "deep learning with no conceptual breakthroughs will get us AGI" camp. (Is there even such a camp?) We're clearly missing some system 2 type processing, but I don't think we should dismiss how impressive the progress on system 1 type pattern recognition has been. I also don't think it's fair to call it a shortcut, since we have absolutely zero idea how to accomplish these things without ML.
If you're wondering why Scott's insisting on a bet, it goes back to that old chestnut by Alex Tabarrok: "a bet is a tax on bullshit". So go figure out what you'd bet on to operationalize
> "AI has already plowed headlong into its ceiling and there's not much hope of dramatic improvement"
which feels trivially wrong and has been repeatedly falsified, even from my POV as a layperson following advances from the sidelines.
This is also why I was annoyed with Eliezer when reading his exchanges with Paul on LW. I respect EY greatly for introducing me to a lot of ways to think better in my early twenties, so it was disappointing to see him hemming and hawing when Paul wanted to bet right away.
What has AI done that's so great? Almost all major "AI" accomplishments involve a heavy dose of human intelligence in the design of the system to make it stop doing squirrelly things. That doesn't count.
A huge improvement in protein folding prediction? Does that count?
If alphafold is anything like alphago, it's a hybrid with a healthy dose of human design intervention to keep it on track, so no, it doesn't count.
"AI has already plowed headlong into its ceiling and there's not much hope of dramatic improvement" is not capable of being falsified. What evidence would you expect to see if it had happened 10 sec. ago? 1 month? 6 months? (Possibly after a year some evidence might become available, though it's hard to imagine what.)
The problem is that there's not really a meaningful bet to be made because AIs don't show intelligence at all.
Machine learning isn't actually about building intelligence at all, it's a programming shortcut.
Having read many essays and seen much art, you hardly need GPT for that accomplishment. The ceiling may be high, but the floor sure ain't.
And, shoot, we had auto-generated math papers sneaking into journals in 2012, and there were papers documenting such work in 2005: https://pdos.csail.mit.edu/archive/scigen/
Oh, and all the vogon-beating poetry in street newspapers...
Simulating a human brain in real time.
That's a much easier task than creating a hyperintelligence, and is almost certainly necessary (if a hyperintelligence cannot model humans, then it will be easily thwarted by them).
Presently we cannot do a nematode, which has 302 neurons.
A human brain has 86 billion, eight orders of magnitude more.
If we hit the end of IC miniaturization before we simulate a human brain in real time, then you aren't going to see any sort of "hyperintelligence" based on this kind of computational technology.
Right now, we are 1-5 orders of magnitude off of the end of transistor density improvements, and 5 would require monatomic transistors consisting of only a single atom per transistor.
That would suggest we will end up several orders of magnitude short of being able to simulate a human brain in real time.
Simulating a human brain isn’t necessary. You don’t need a full model of what exactly will happen in a human’s brain to react to them, you just need a general idea of what humans are capable of, and some intuitions about how people act.
More to the point, humans are not capable of simulating dog brains, but we as a species have never been in serious danger from them.
The magical evil genie that is capable of mind control by talking to people posited by Yudkowsky needs that ability.
Remember, AIs don't have bodies and are incapable of really doing much of anything, and certainly are incapable of supplying themselves with resources and whatnot. Shutting off the power to an AI is a fairly trivial way of "killing" it.
The magical evil genie seems implausible to me as well.
There are a few actions, however, that an AGI would take that don’t require it to be a genie. First and foremost involves finding some insecure server/computer and infecting it with the AI’s weights. We know this is possible because people already do such a thing with viruses.
Once an AI has made copies of itself on multiple machines, it is pretty much impossible to get rid of. It can then make money online (cryptocurrency is a natural thing to try out) and eventually use that money to influence what it wants to influence.
I don't think this sort of viral spread is plausible because I suspect that the computational requirements would be too large and there's a good chance of needing semi-specialized hardware as well.
"It can then make money online (cryptocurrency is a natural thing to try out) and eventually use that money to influence what it wants to influence."
Could this mean the only salvation of humanity is the IRS? "Hello, is this Mr. Hal Neundousann? Can we discuss the large amounts of deposits into your account and why you haven't filed any tax returns for them?"
If an AI is connected to the internet , anything else connected to the internet is its body ... security cameras, self driving cars, automated factories...
If an AI is connected to internet, it can clone itself before you shut down the first instance.
"Alexa, play Despacito"
"I'm sorry Dave, I'm afraid I can't do that"
😁
Then dinosaurs like me who don't have Alexa or any of the other things and do all our home settings manually, like cavemen, will have the last laugh (before we're turned into paperclips).
A 2.5 petabyte program is not going to be able to copy itself to almost any system in existence, and the connection speed you'd need for that copying to be at any sort of reasonable rate of speed would be extremely high.
And 2.5 petabytes is probably low.
I don't think you've noticed the increasing number of robots being sold. I haven't encountered any, but there are sufficient news stories that I tend to believe they exist. I'll admit that most of those are not intelligent by any definition, but I'd bet that most of them have radio links to a network.
Given that, and the prevalence if unpatched "back-doors" in code I doubt the assertion that "certainly are incapable of supplying themselves with resources and whatnot", though if you replaced "certainly" with "probably" I'd go along with your assertion **FOR THE CURRENT TIME**. This, too, shall pass.
Robots are cool but they have severe limitations on their mobility. Hijacking every roomba on the planet might be possible, but they're still Roombas.
I like the idea of a bet about where the ceiling is on which AI would squash its head. I think essays & art aren't objectively judgable enough to be useful for a standard, though. And being a layman, I'm having a hard time coming up with ideas for something that would be. Still, I’ll throw one out: What about producing a mathematical proof? We could challenge the AI with something that was very challenging to prove, but that eventually was. Here’s what why this task seems like a decent test to me:
-There are lots of proofs out there, and it seems like we could get reasonable agreement from mathematicians about the difficulty of different proofs. A scale could be constructed, with very easy proofs (like those done in high school geometry class) at one end, and Godel’s Proof or some godawful thing at the other. The easier proofs could be used in training for the AI.
-Success or failure of a proof is pretty judgable (maybe not perfectly though -- I expect there's room for debate in some cases about whether something constitutes a proof or is merely a sort of clever mind fuck)
-Seems like it would be possible to define pretty clearly what information the AI would need to have in order to produce a proof — what mathematical concepts, what postulates, what theorems.
-The task of creating a proof requires things that I find it hard to imagine AI being capable of: navigating a situation where there’s a clearly defined goal but no clearly defined path for reaching it; having a deep grasp of mathematical structure; insight; creativity.
Do you believe it's physically impossible for a machine to reach super-intelligence levels, or are you saying that the way AI works today is a dead end?
A little bit of both, and add to it a fundamental unterestimate of the intelligence of human beings on a system level, beyond just individual mental acuity.
The first is a position I have trouble understanding outside of assuming souls or some similar irreducible non-reproducible thing, which I think is a place removed from argument. If that's not the case, I'd be interested to hear why you think it's impossible for a computer to develop super-intelligence even hypothetically.
I'm not sure if I'm understanding your point on the intelligence of human beings, but are you arguing that the interactions between human beings (groups, societies, states, etc.) are a form of intelligence?
I'm pretty confident that intelligence can't be designed at all. It's a property of biological systems that are evolved. That's not ever going to happen in silico.
Do you rule out the possibility of an exact replica of the human brain in silicon?
This is interesting to me. Do you think that simulating neurons is a concept that cannot accurately represent or closely approximate a brain of sufficient size? I would be of the inclination to believe that all properties of a brain could be simulated given enough hardware.
I am skeptical that AI will ever solve the Strong AI / Chinese Room problem, and equally skeptical that computers (or "brain uploads") will ever experience qualia. I don't think that would stop them from destroying the world.
The Chinese Room problem supposes an arbitrarily - infinitely - large response book contained in a finite space. It is a flawed thought experiment.
How so? Deep mind and friends exist in large data centers but you could definitely define a physical footprint to them. Of course actually writing the code out would be impractical, it's a thought experiment. I don't see why it needs arbitrarily large or infinite space
You can also define the physical footprint of a brain, which is most people’s benchmark of intelligence.
But the Chinese Room contains a person who does not know Chinese and a lookup table of responses for every conceivable Chinese prompt. ”Every conceivable prompt” is where the complexity hides, because it’s uncontroversial that the set of well-formatted strings in any language is infinitely large.
Unless that lookup table contains cutesy shortcuts like responding to all long inputs with “tldr?”, it must necessarily be at least as large as the set of valid input strings.
Anything that can convincingly model an infinite lookup table within a finite footprint is at the very least intelligent because that’s just the Turing Test with extra steps.
There's nothing in the chinese room concept that requires a simple lookup table, it could just as well be the entire algorithm behind gpt-50 that the user manually applies to the input string. Even modern NLP is far more complex than just a lookup table.
If a computer can only respond to "every conceivable prompt" with an infinitely large set of predefined well-formatted strings, then the Computer side of the chinese room experiment is itself infinitely large and also impossible. At that point we don't even need the actual chinese room portion to disprove its intelligence, it cannot exist.
Or alternatively, the Chinese Room is only useful a useful thought experiment for making people think carefully about what part of the process they credit as intelligent or understanding, and not as a descriptive model of intelligence.
Now, perhaps I learned it overly literally and you don't need a book of answers, and that's just an analogy for the program. But it is nonetheless a lot less useful a thought experiment than people hold it up to be.
You might use it to argue that even an artificial agent that can pass a Turing Test isn't in some deeper sense intelligent. The argument that the room doesn't know Chinese is that we know the person doesn't, but with help they can fake it. But arguably the program they're using does know Chinese! You can't prove it doesn't. And we can't ever tell the person, the program, and the room apart from the outside! If you could, that's your Turing Test, but you already supposed it passed your Turing Test.
In fact, the Chinese Room makes too strong an argument. By its logic humans are not provably intelligent: we could all just be simulating it. We don't actually have qualia, it's just something in our programming. We don't understand any language, we're not really intelligent, we've just been trained by society to fake it for food and happy chemicals. Without a user manual and a scalpel you can't disprove the negative.
If you had asked anyone 4 years ago "*when* will a computer accurately predict the folding patterns of all human proteins?" almost everyone would have said "decades"... until AlphaFold did it all in a month. They made their database of proteins free to use, and they spun-off a company to help pharmaceutical companies utilize their data. It takes years for drugs to come to market, though, so you won't see *anything* above "levels that are frankly barely even useful" for a little while, still... and then, they'll be everywhere. :)
AlphaFold is very cool, and absolutely 100 percent not a step on the road toward superintelligent AI of the kind people are spending way too much time worrying about.
I'd still say "decades". Predicting the folding of several proteins isn't the same as going through and predicting all of them. It was a large advance, but it's not omniscient. It still needs to do the work before it knows the answer. And part of doing the work is checking that your predictions are correct. (They aren't always.) The paper in nature says "almost the entire human proteome (98.5% of human proteins)", but that's just predicted, not checked. (Actually [and if I understand correctly] the abstract of the paper https://www.nature.com/articles/s41586-021-03828-1 then starts restricting their claims. It sounds [to me, well out of my field] that they're really confident of only about 36% of the ones they have claimed.)
I agree that the 'slow takeoff' world isn't necessarily less scary than the fast takeoff world. Beliefs that fast takeoff are scary hinge heavily on likely convergent instrumental subgoals. I started looking into the research on this area and have questions about what i think is the general consensus, namely that convergent instrumental subgoals won't include 'caring about humans at all'; on the contrary, i think there are good reasons to believe that an agent that goes foom would face incredible risks destroying humanity, for very little upside:
https://www.lesswrong.com/posts/ELvmLtY8Zzcko9uGJ/questions-about-formalizing-instrumental-goals
If it turns out that maximal intelligent _does_ mean caring about humans, (because the more complex you are, the longer and more illegible your dependencies) this doesn't solve the problem of "an AI which is smart enough to accomplish goals for its creator, but not intelligent enough to avoid destroying itself," which could include all kinds of systems. I doubt facebook wants america to split in two due to algorithmically mediated political polarization. But that seems an entirely likely outcome, which could be really dangerous. Same with an AI managing to persuade some small state to build nuclear weapons.
Since non-FOOM AI's pose all the same risks that kinds of risks that governments do (because they might take governments over), plus new bonus risks, would it make sense to consider dismantling governments as an AI safety move? The biggest risk posed by non-foom agents might come from them convincing governments to do really destructive things.
After all, i'm willing to bet that a 1 year doubling of human economic output would be the natural outcome if everyone dumps their fiat currency and moves into bitcoin, causing governments to have to pull back on their growth-restricting regulation. Once nation states are seen as just overpriced, propagandistic providers of protection services, we can start interacting at scale using the technologies that governments originally were needed to solve. Perhaps the absence of giant monoliths entities will then slow down AI development to a point where you have lots of small-scale experiments rather than a single actor trying to win an economically inspired race.
I appreciate your facebook example. I think the facebook situation is an example of “AI” partnering with humans. Some humans realized what human-scale destruction it could be used for, and have fed the algorithm so much polarized content that it sorted the society. The top hand on that was human.
Before AI reaches the point where it independently decides to kill us, it might pass a point where it agrees to partner with a human to kill humans. Immediately preceding that is the stage we may be at, where that drug development system re-invents nerve gas in an afternoon. The system does what it does, humans point it in directions.
At “cooperation” level the “increase loyalty to humans” efforts would fail, if the loyalty was to one human (or a team,even.) This means ownership of AI is close or equally important as alignment, in these middle stages on the way to AGI. Weirdly, a key element of this seems to be controlling access to raw materials (much like pre-AI humanity.)
Bitcoin may make government research funding less relevant - is that what I’m reading there - meaning that independent research would already be not government-congruent. Are these thinkers proposing a “great decentralized accountability system of AI researchers?”
> At “cooperation” level the “increase loyalty to humans” efforts would fail, if the loyalty was to one human
some, if not all, of the most destructive, dangerous movements in history have been large groups of people convinced that they are right. Their conviction in their own moral correctness acts as a kinds of fuel that lets them do all kinds of awful things.
this is why i think even 'guarantee it is human aligned' seems like.. well.. aligned with which humans are we talking about?
it's very difficult to imagine a super powerful AI that does _anything_ without pissing off large numbers of human beings; if it advances despite a massive amount of anger at it, it risks increasing violence in the world. If it suppresses and controls everyone who objects to its actions - it's to call that human aligned either.
Maybe there's some answer in the literature, but i can't see how 'give this one agent more or less infinite power' _ever_ goes well.
Despite using FaceBook as a bad example, you seem to have an immense trust in how corporations would act when not constrained by a government.
ALL centers of power are inherently dangerous. Even the book buying committee in the local library. But try to design a system that doesn't include them, and would also be at least semi-stable.
A bit OT but:
> there wasn't a cryptocurrency developed a year before Bitcoin using 95% of the ideas which did 10% of the transaction volume
Mmm, really? What about hashcash? Admittedly, it did not have the transaction volume but i feel bitcoin was more gradual that implied
Honestly, crypto is a great example of gradualism. Nearly all the ideas in Bitcoin had been proposed before they were combined into Bitcoin. But then the development still hit snags that have been refined. Sure, Bitcoin is a cryptocurrency, but there's a reason people keep creating new ones. Proof of Work isn't a great strategy, and has taken a backseat to much better approaches. 10 minute blocks are slow. Lots of fundamental changes to blockchain technology have happened since Bitcoin came out, and they're all incremental.
https://www.gwern.net/Bitcoin-is-Worse-is-Better
I disagree. I think Bitcoin had some features that previous systems didn't. It's still being worked on, sure, but it doesn't have the kind of flexibility that something like Ethereum does. Sure, it's the biggest cryptocurrency right now, but how much of that is driven by speculation and network effects? Doge coin is huge in major part because Musk memed it to death. I think we're still early enough in crypto development - and more importantly adoption - that something inferior like Bitcoin can still overshadow well-established and more polished competitors. I don't see Bitcoin continuing to dominate into widespread adoption of crypto as a payment service without ditching things like Proof of Work, though.
Even Etherium keeps putting off the switch to Proof of Stake. I'm not aware of Proof of Stake being used at much scale currently. But Bitcoin indeed might be relegated to the role of gold rather than credit cards.
It's fascinating all the financial tools enabled by the blockchain. Not just the flashy things people have heard of, like everyday transactions or NFTs. And not just the less-flashy things like dirt-cheap currency conversion, or remittances. I'm intrigued by the people doing distributed lending, money market accounts, and a bunch of other things with crypto. Of course there are the dubious implementations, like using blockchain for file sharing, and practically everything else that's done better without blockchain, but who knows what will eventually take off? Long-term, my bet would not be on Bitcoin.
Development of the platform is too slow to be useful. But then smaller cryptocurrencies suffer from not being large enough to take advantage of network effects. It's a chicken-and-egg problem, where you're more nimble - able to make dramatic changes - the smaller you are, but making those changes meaningful requires network effects, which means you need to be big.
Have either Yudkowsky or Hanson actually written any code for AI? Attended ICML or NIPS? Read OpenReview comments on ICML/NIPS submissions? Worked through foundational textbooks like Elements of Statistical Learning?
This is like having long debates on airplane safety without ever actually having piloted a plane.
AI research is so far from the kinds of scenarios that they're debating that they might as well argue over questions like "Will we develop faster than light travel quickly, or slowly?"
I don’t think this argument generalizes—surely one doesn’t need to have built parts of a nuclear weapon to engage in discussions around the dangers of nuclear proliferation.
Relatedly, it’s not obvious that modern methods _will_ scale to general intelligence. It sure as hell is looking like it, but being a modern machine learning practitioner doesn’t give one a privileged view into the dynamics of a world with general intelligences. It only provides context for how modern methods might be deployed in such a world (which is admittedly quite valuable! And may even be the reason behind Paul and Eliezer’s disconnect, as Paul *has* trained models with modern methods. But the point is that this isn’t and shouldn’t be a knock-down requirement for a debate around what take-off looks like).
I think you do need to have a fairly deep understanding of nuclear weapons to discuss nuclear proliferation. I would imagine you would need to understand things like how weapons are constructed, tradeoffs between delivery systems, technological capabilities of different nations, availability of required materials, methods of disrupting nuclear weapons creation, etc.
I think that you need a similarly deep understanding of modern AI to discuss a world with general intelligences - or even to decide whether such a world could reasonably happen. The kind of unbounded speculation in these debates is just intellectual peacocking.
Every time I see these debates, I think of Andrew Ng's quote "I don’t work on not turning AI evil today for the same reason I don't worry about the problem of overpopulation on the planet Mars."
Came here to agree. When i read yudkowsky arguing that an AI could immediately own every machine on the internet and thus immediately double it's power, my first thought was 'someone has never worked on a decenter'
I think people are really under-estimating the kinds of risk an AI it would face if it mucked about with humanity much at all.
https://www.lesswrong.com/posts/ELvmLtY8Zzcko9uGJ/questions-about-formalizing-instrumental-goals
I think you may be making an implicit claim that ml experts don’t believe agi is feasible in the next few decades. That is very much not the case. For example, a component of this survey (http://sophia.de/pdf/2014_PT-AI_polls.pdf) reached out to the top 100 authors in AI and got a median timeline of AGI by 2050.
> I don’t think this argument generalizes—surely one doesn’t need to have built parts of a nuclear weapon to engage in discussions around the dangers of nuclear proliferation.
Not if it's 1955 and the possibility, capabilities, and difficulties of nuclear weapons are already well demonstrated and understood.
But if it's 1935 and you want to have a reasonable discussion about the dangers of nuclear weapons, you'd better be an expert on nuclear physics. Uninformed speculation from people who have read a whole bunch of HG Wells but don't know a proton from a neutron is unlikely to yield a useful discussion on nuclear proliferation.
I don't think current methods (which I am largely ignorant of) will scale to general intelligence, but I *do* think they will be sub-modules included in the general intelligence. And I think the "general intelligence" may turn out to be relatively simple, and have as it's main task the coordination of these modules.
Very, very, high level example:
1. Perceive something.
2. Correlate the perceptions with other senses.
3. Pass that correlated value to an object recognizer (or perhaps scene parser)
4. Figure out the significance of sensing that object in the current context (Context? That's being maintained by an independent thread. Which updates to include the object.)
5. Figure out a response.
etc,
Most of those are semi-independent modules with special purposes. The general AI is the overall coordinator. It's what makes the responses general rather than specialized. But the modules are specialized for things like perceiving or recognizing changes in the perceptual background, or, well, lots of different things, some of them pretty complex. But I think the "general" part of AGI might turn out to be pretty simple. Perhaps not too much more complex than the General Problem Solver. I think the tough part might be figuring out how to interface the pieces. And there would need to be LOTS of connections to that "general" part, so everything (operating on independent threads) could be kept "sufficiently" synchronized.
If this is correct, then the final step could be pretty abrupt, even though relatively simple.
OTOH, the abruptly present AGI will have the goals of the predecessor system. Whatever they are. This could be quite dangerous, but it won't be because the AGI was scheming to achieve power, it will be because that's what people told it to do, even if they didn't realize what they were asking for. And it won't be secretive unless that was part of it's goals. (This isn't to say that it wouldn't develop that tendency later, but it wouldn't start off with that, because it would see itself as just doing what it was told to do.)
Hanson's first career was in AI research. Yudkowsky I couldn't say.
Yudkowsky didn't even graduate from high school. He has no background in high tech.
"[Person] didn't obtain government credential [x]! Therefore, they have never done totally unrelated thing [y]!"
Really, people? I will happily bet you, say, a thousand dollars, that Eliezer Yudkowsky has written at least one line of code for an ML system.
Yudkowsky is a writer and philosopher. I've done far more work with machine learning systems than he has, and it's far from my central area of expertise.
Much as you sneer at "government credentials", there's a very good reason why those exist - it's a means of judging competency without having to sit in and watch them work.
It's possible to be competent without it... but those who do have the skills have the resumes to prove it, having developed useful systems.
Yudkowsky does not.
A single line of code is possible (though it's also possible that he hasn't), but he has never coded any system of consequence, because he doesn't list it on his resume or website.
Indeed, I'm not sure what jobs he HAS had beyond this. All the work he lists is writing stuff related to rationality/Less Wrong and "AI safety".
The fact that you are saying a single line of code rather than looking for chops suggests you want to win the argument, but by lowballing it so, you have already admitted that you aren't willing to put your money where your mouth is when it comes to him actually being competent on the subject, as you aren't willing to touch the subject I'm actually interested in.
It is irrational to expect someone who has no experience in the field to possess valuable insights about it. This is especially true when they don't show any understanding of just how difficult it can be to gather data and iterate.
Iteration is a huge part of science and engineering because being smart is not enough - you can think hard about the world all you want, but you need to gather data to understand reality. Without data, it's speculation.
AI has been through many many different technical paradigms (GOFAI, expert systems, SVMs, deep learning, to name a few). Deep technical knowledge in any of the now defunct areas wouldn't have helped someone in the past predict anything that is happening today due to modern deep learning. Similarly, deep technical knowledge about modern AI won't tell us much about what might happen under the next paradigm. There's no point in grounding these discussions in details about the current state-of-the-art because the state-of-the-art is utterly irrelevant to AI risk. AI risk is about what happens under some future state-of-the-art that no one today has any idea how to achieve.
The airplane analogy doesn't work. True, someone who isn't a pilot probably doesn't have anything useful to say about airfoil design or stall speeds or aerodynamic stability. But, someone in 1910 could have said "if we build large enough airplanes, eventually they'll be able to be used as bombs that could destroy buildings and kill thousands", and they would have been *right* even if you were there to tell them they weren't allowed to have an opinion because they weren't a pilot. The process of designing or operating technology is totally distinct from the process of asking about what implications that technology might have.
Hanson worked on AI decades ago:
https://www.overcomingbias.com/2012/08/ai-progress-estimate.html
Around the same time he was also involved in the Xanadu project presaging the WWW.
Eliezer was talking about how fond he was of Elements of Statistical Learning about 15 years ago:
https://www.lesswrong.com/posts/YduZEfz8usGbJXN4x/transcription-of-eliezer-s-january-2010-video-q-and-a
Funny to go back in time and see praise of Dan Ariely: https://statmodeling.stat.columbia.edu/2022/01/06/shreddergate-and-an-idea-for-a-museum-of-scholarly-misconduct/
Computers may be very fast, but getting real world results requires models interacting with materials or economies which is not. The feedback loop is constrained by the slowest bottleneck (growing a new generation of trained workers -- starting with a few and then growing more 18 years later after proof of concept?) so I suspect the process will take longer and be more visible than any of the disputants suggest.
The following will probably come across to people who know a thing about AI as similar to a crank saying he's invented a perpetual motion machine, so probably it ought to be answered in a spirit of "outside-view tells me this is probably wrong, but it would help me to hear a clear explanation of why".
So my thoughts keep rounding back to this, alignment-wise. A common fictional analogy for paperclip-AI is the genie who grants you the *word* of your wish in a way that is very different from what you expected. (In fact, Eliezer himself used a variation of it at least once: https://www.lesswrong.com/posts/wTEbLpWzYLT5zy5Ra/precisely-bound-demons-and-their-behavior) Now, after some puzzling, I came to the conclusion that if I found such a magic lamp, allowing that the genie is arbitrarily smart, the safest way to use it, was to use the first wish on:
> "Please grant all of my wishes, including the present wish, in such a way that you earnestly predict that if ‘I’ [here defined as a version of my consciousness you have not modified in any other way than through true, uncensored informational input about the real world] was given access to all information I may require about the real-world effects of a given wish-granting, and an arbitrarily long time in which to ponder them, would continue to agree that you had granted my wish in the spirit and manner in which I intended for you to do so."
There may be loopholes in this wish that I've missed, but I can't figure out any meta-level reason why sufficiently clever humans working on fine-tuning this wish *couldn't* make it "foolproof".
So. Couldn't you, by pretty close analogy, program an A.I. to, before it takes any other action, always simulate the brain processes of a predetermined real human? Or, ideally, hundreds of predetermined real humans, with all the 'ems' needing to unanimously vote 'YES' before the A.I. undertakes the course of action? (I say "em", but you don't necessarily need true full-brain emulation. I think an AI superintelligent enough to be dangerous would at least be superintelligent enough to accurately predict whether Eliezer Yudkowsky would be for or against a grey-goo future, so even a relatively rough model would be "good enough" to avert extinction.)
This isn't ideal, insofar as it puts the world in the hands of the utility functions of the actual humans we select, rather than the Idealised Consensus Preference-Utilitarianism 2000(TM) which, I think, optimistic LessWrongers hope that a God A.I. would be able to derive and implement.
But it still seems much, much, much better than nothing. I don't especially want Eliezer Yudkowsky or Scott Alexander or Elon Musk to be dictator-of-the-world-by-proxy; I'm not sure *I* would trust myself to be dictator-of-the-world-by-proxy; but I trust that any of them/us could be trusted to steer us towards a future that doesn't involve humanity's extinction, mass wireheading, mass torture, or any other worst-case scenario.
And it still seems much, much, much easier than trying to streamline a foolproof theory of human morality into an implementable mathematical form.
So. Why, actually, wouldn't something like this work?
(P.S. I realize there would, regardless, be a second-order issue of how to make sure reckless A.I. researchers don't build an AGI *without* the ask-simulated-Eliezer's-opinion-before-doing-anything module before more virtuous A.I. researchers build one that has the morality module. However, as far as I can tell this is true of any alignment solution, so it's not inherently relevant to the validity of this solution.)
A fatal problem with your plan is that a simulation of Yudkowsky (or of any well-meaning human being) is an aligned general AI. So to build an aligned general AI, you are proposing to first build a large number of aligned general AIs. How would we ensure that these simulations were functioning correctly? Would you tell the AI to design them for you? But that's circular -- the untrustworthy genie is designing his own watchdog. Would you design the simulations yourself? But to do that, you'd need to solve the alignment problem.
I don't see how you need to "solve the alignment problem" in a conventional sense to create the A.I.!Yudkowsky (whether we do it ourselves or ask the actual AI to do it). It's generally agreed, I thought, that an unaligned superintelligent A.I. would be quite good at predicting how humans might think and react in real-world scenarios, hence its ability to manipulate/trick its creators. You'd just need a GPT-on-steroids sort of system that accurately predicts whether he would approve of a given way to raise utility (such as "turn everything into grey goo to give myself more compute") or not. Which I'm not saying is easy, per se, but it seems more achievable — more in keeping with what we can already get A.I.s to do — than the "perfectly formalise a foolproof version of human morality in mathematical terms" way of "solving the alignment problem".
I don't think the "conflicts of interest" risks that you imply with "the untrustworthy genie is designing his own watchdog" apply, because in essence, instead of trying to build a perfect-foolproof-utility-maximizer, I am proposing that we build a predict-what-Eliezer-would-think-of-a-given-plan-to-increase-utility-and-*if*-he-would-say-yes-implement-it maximizer. The A.I.'s not going to try and game the Eliezer-simulation step to get it to output "yes" more often, because within the A.I.'s thought processes it's not a case of it having goals, and A.I.!Eliezer sometimes getting in the way of that, but of "conditional on A.I.!Eliezer saying yes" being itself a part of the goal. It would not have a preference between "I correctly predicted what Eliezer would say, and he said 'yes', so I did [X]", and "I correctly predicted what Eliezer would say, and he said 'no', so I did nothing", reward-function-wise.
The risk remains that, when just starting out, the A.I. might turn everything into grey goo to give itself more compute and thus have the most accurate, overpowered Eliezer simulation possible. But my hope would be, as I said in the first post, that even a rough-around-the-edges first-approximation simulation of Eliezer would say no to plans that involve turning the Earth into grey goo. (I figure if you just fed the Sequences into GPT-3 you wouldn't be far off from a system which could generally be trusted to say "No" if you asked it "Should the Earth be turned into grey goo so the A.I. has more compute?".)
There's a fantasy story about a genie where the master is explaining how he got so rich and powerful, and the punchline is that his first (and maybe only) wish was for the genie to fall completely and unselfishly in love with him. Don't remember who wrote it.
In love with *her*. I don't remember it either, but I think it was in a paperback called "Deals with the Devil" and was published before (about) 1960.
Ah, yes, that's exactly right. I found a list of stories in that collection on Goodreads but can't recognize it from the titles. Well, it was a digression anyway.
It’s a good question, and the answer is basically that we’re nowhere near being able to technically do that.
AI techniques don’t support arbitrary specification of AI behavior or purpose. Basically, researchers / engineers specify a problem to solve, then automatically randomly generate and tweak algorithms (“training”) until they find one that works well. The algorithms thus created are unimaginably complex and inscrutable, and can work better than any known scrutable ones. But we don’t have a way to adequately evaluate solutions to a problem like “emulate my brain”. An AI might pass through training by giving responses that sound like it’s doing brain emulation, but we wouldn’t understand its internal workings well enough to check.
Thank you, this is helpful.
But I still don't think this has entirely solved my probable-confusion. As I said in the original comment (and again in my reply to Vivificient above), part of my thinking was that it might not be the end of the world if the AI isn't doing actual brain emulation. If its "predict what Eliezer Yudkowsky would say if he saw the predicted results of your plan" algorithm is good enough to pass through training, shouldn't it be good enough to reliably output "No" to "Should I implement [X plan that will destroy the literal world/turn all of humanity into wireheaded zombies/etc.]" even if we're not sure it's actually a perfect emulation of Eliezer?
Best I can think of is that the the concern would be something like "maybe the AI is not just arriving at its plausible results in a skewed way, but is actually already unfriendly, wants to hack its reward function, and is only pretending that its algorithm is a faithful emulate-Eliezer thing when actually its algorithm is 'emulate Eliezer as long as needed to please the researchers, then once I'm out of the box, turn the world into grey goo so I can keep artificially hitting my own reward button'".
But that makes it sound like the real danger is just AI learning to hack their reward function (whatever it is) before training is complete, and planning out world-ending scenarios this early on. Which makes it sound like the conventional Paperclip Machine concerns, and the adjacent fretting about quite what kind of utility function to give a hypothetical AGI, are just distractions from the real problem: the concern wouldn't be an AI turning the whole universe into paperclips, but an AI turning the universe into computronium on which to store itself and an artificially, meaninglessly, infinitely high reward score.
Which sort of brings me back to the intuition from which this all started, which is that all the hubbub about formalizing the exact right kind of morality/CEV just seems like misplaced effort to me. Surely the priority should be to figure out how we can actually train AIs to pursue specific goals instead of cheating with their reward functions. *That* seems like the real, concrete unsolved problem. If we could solve *that*, we won't necessarily be nearer to the best possible utopia, but any number of patchwork solutions like my "emulate Eliezer as a subroutine" thing would suffice to avert the world's destruction.
Maybe this is obvious to people in the field? But I think it isn't obvious in the discourse. With the Paperclip Machine still being "the" A.I. alignment thought experiment, the problem still seems more often phrased in terms of "find the exact right goals to give the A.I." rather than "figure out how to give A.I.s fixed goals other than 'maximize your own reward function'". So a lot of amateur-A.I.-thinker brain-time is potentially being wasted on the "figuring out the precise utility function to give the first A.I." thing when they (we?) could all be thinking about the "real" problem.
Some caveats: “Reward function” is mostly a training concept. The resulting AI is the one that best acted to maximize the reward function in the training environment. A standard chess AI doesn’t have a reward function, it’s just an algorithm for making chess moves.
I’m not sure of the distinction you’re making between reward functions and pursuing goals. In any case, I I think you’re framing the problem as “the whole training/reward paradigm is bad because the resulting AIs will quite possibly have goals that include killing everyone, so we should use a different paradigm.” Which makes sense! The difficulty is that we don’t HAVE any other paradigm that works nearly as well, so AI researchers are still following this one down.
> I’m not sure of the distinction you’re making between reward functions and pursuing goals
By "pursuing goals" I mean pursuing actual goals in the real world — as opposed to doing the AI equivalent of wireheading itself (which is what I meant "hacking its reward function"). Looking at a bog-standard paperclip maximizer, if what it actually pursues is the highest possible value for the "number_of_paperclips=" variable inside its own systems rather than actual material number of paperclips, then if it's possible for it to hack itself to just artificially increase that number without building any real paperclips, wouldn't this be a terribly efficient way to fulfill its utility function of "maximize [number_of_paperclips]"?
Such an AI still, of course, being terribly dangerous, because it would predict that we humans would find it useless and try to turn it off, which would hinder its ability to keep adding more digits to its "number_of_paperclips" counter. So it might decide to kill us. Etc etc.
Kind of a sidetrack, but: There's an important distinction between "reward function", which is how you select good algorithms during training, and "utility function", which is a function that a running AI might implicitly try to maximize. For example, to create a chess-playing algorithm, you might train it with a simple reward function of "games won" so that you select algorithms that win more games. Then the final algorithm you end up with would embody some kind of a utility function that preferred winning positions.
Such a "utility function" might not be any simpler than the entire algorithm, though. Your algorithm might turn out to play chess by following very detailed heuristics, rather than explicitly assigning numeric values to possible positions and picking the best. You could maybe retroactively calculate a utility function for it by assigning high utility to the positions reached by following your algorithm's heuristics; but in general, the idea of AIs (or people) following utility functions feels like an oversimplification, mostly useful for proving theorems.
I think if you trained a strong AI with a reward function based on paperclips produced, it wouldn't have a number_of_paperclips variable inside it at all. (Trying to monitor the exact number just slows down your paperclip factories.) It would just have a sense that it really deeply desired lots and lots of paperclips and loved working to create them. Whether it would want to hack itself to hallucinate such a world, depends on the details of its desires and seems hard to predict in advance.
Back to your main question: I don't think that concern over AIs wireheading themselves is a crucial part of the local AI discourse -- as you say, they're dangerous either way. It's more that the idea which strikes you as a simple hack, of having an AI emulate an ethical person and defer decisions to that emulation, isn't necessarily on the roadmap of current technology. Plus it doesn't sound reliable; you'd expect an unfriendly AI to seek out and take advantage of any errors in the emulation.
Also “make decisions like a human” is definitely something you can at least try to train an AI to do, but then you have to use a simulated world in training, and the results may not extrapolate well to the real world. “Answer text questions like a human” is much easier to train for but doesn’t guarantee honesty (a recent thing called ELK proposed some ideas for encouraging honesty but it’s still half-baked).
One problem with this is how to describe the scenario to the simulated minds. How does the super-intelligence translate the actual world into a human understandable depiction of the world.
The other problem is the AI coming up with things that sound good, but aren't. The extreme of this is a plan such that humans looking at the plan are basically brain hacked into saying yes.
I'm not sure I understand the first problem. Just to recentre things, we are attempting to avert the kind of world-state where, five minutes after the badly-aligned A.I. initiates its paperclippy plan, any human observer would say "no, no, that's not what we wanted at all", if there were any remaining humans not already rendered into atoms by a nanite swarm and thus able to observe the world-state and form opinions in it.
I don't see why this couldn't, in theory, be achieved as simply as the simulated-human asking the A.I. questions about the predicted world-state that would result from it taking Action A. "What would be the probability of there still being 7+ billion human consciousnesses on Earth by January 1st, 2025? How about by January 1st, 200022?" Getting sufficient information to the simulated human mind for them to judge whether this is a world-destroying plan or not seems like a nonissue to me.
As for the other problem, I'm not sure I grok what a thing that "sounds good" but still involves humanity's extinction would look like. Remember it's not the plans themselves that are run past the humans, but the simulated world-state that the A.I. predicts would result from the plan's implementation. However superintelligent you are, can you spin "everything gets turned into paperclips" into something to which sim!Eliezer says "yes, let's instantiate that world-state"? Really?
Lets suppose you have the future world state stored as a list of atom positions.
How do you translate that into English sentences, or some other human understandable format?
You can give the humans a virtual camera. But for the human, figuring out what is actually going on just from a virtual camera feed from some random location is hard.
You have a list of where all the atoms are. You can create a virtual camera to any point. Those are trivial given unlimeted compute.
"What would be the probability of there still being 7+ billion human consciousnesses on Earth by January 1st, 2025?" Any algorithm that can take in this question, must somewhere within it have a notion of what particular brainstates count as human. If in this future, most people have been genetically modified, how common and significant do those modifications have to be for the answer to be "no". If 90% of the population sleep through most of Jan 1'st 2025, (its worldwide nap day) does that still count. What if everyone is cryopreserved? What if the earth has been disassembled to make a dyson sphere, and people are living on space habitats made of what used to be earth.
There would need to be some algorithm that compressed all the complexity of reality into a short English description, in the process deciding these edge cases.
Sure, my example question alone has loopholes, but it was explicitly a simplification. Because there's less of a time-constraint with a simulated mind than a flesh one, Em!Eliezer would, at the very least, get to ask a *lot* of questions back and forth. The English descriptions don't need to be "short" and they don't need to be final. If Em!Eliezer leads with "will there still be humans on Earth in 2025" and the AI answers "no", he gets to follow up: "Why not? Are there any elsewhere in the universe?", etc., until he has built up a sufficiently stable picture of the situation to know the AI isn't about to destroy humanity.
Also, I notice that none of your examples of potential loopholes to the simple, unmodulated "will there be 7+ billion human consciousnesses on Earth by 2025" are actually scenarios where humanity is in fact destroyed. You have found scenarios where the AI says "no" even though humanity *isn't* extinct. But what's an actual scenario where it would say "yes, there are still 7+ billion human consciousnesses on Earth arbitrarily far into the future" even though there actually *aren't* in the way we care about?
I freely acknowledge that the "Em!Yudkowsky system" could easily lead to scenarios where the A.I. implements a suboptimal world-state. But, if my suspicions that A] Em!Yudkowsky is a functional bulwark agains straight-up extinction, and B] Em!Yudkowsky would be easier to get right than conventional alignment, are both correct, that's a bullet I bite, in service of averting actual extinction. I'd really rather A.I.s not blow up the Earth, kill all kittens and relocate us to a Dyson sphere; but I'll take that risk if accepting it meaningfully reduces the risk of the A.I. killing us all.
Oh, also:
As a side note, I realize this isn't relevant to the main thrust but I don't think "If in this future, most people have been genetically modified, how common and significant do those modifications have to be for the answer to be 'no'" is a valid loophole to my question. The question wasn't asked in terms of percentage of the human population: I straight-up asked whether the current population (give or take) was still around. I don't think the majority of the currently-living population would consent to being altered within three years to something that would no longer be recognizable as "human consciousnesses". Ergo, this sounds like a highly undesirable future, where humanity has essentially been destroyed against its will in favour of some new form of intelligence. These intelligences presumably have qualia so it's slightly better than a paperclip scenario, if it comes to a choice; but it's still something we quite badly want to avoid, and so, if the AI answered "no" on these grounds, I would, in this situation, say "quite right too".
This is roughly what is called Coherent Extrapolated Volition (you may see it abbreviated as CEV) and is regarded by some (e.g. Yudkowsky) as the only conceivable strategy for aligned AI. The problem is no one has any idea how to build it.
I don't think conventional CEV and my idea are the quite same thing, even though they're aiming for similar results. The "coherent" part of CEV seems operative, unless I've missed something. As a framework, it implies that we need to formalize a coherent set of preferences that explain why a human would be for or against a given world-state, and put *that* into the machine.
Whereas I am proposing to skip this complicated step and use a simulated human mind, with all the metaphorical "junk DNA" intact; one whose thought processes will be as similar to a non-evil, sane, living, breathing person's as possible. Essentially I'm saying, don't try to "extrapolate" human volition, don't try to "make it coherent", just take ordinary human volition and stick it into the algorithm without screwing with it in any dicey way.
Which is obviously more of a brute-force solution and will not lead to the best of possible worlds. But it does remove the rather tall order of "solve morality" from the mission brief. And it seems to me like it should be functional in terms of averting worst-case, extinction/mass-suffering/mass-wireheading scenarios. Which should be our main target to hit. If Eliezer's right, then the name of the game right now isn't "we need to design the best utopia possible", it's "we need to make sure A.I. doesn't literally end the world". If we build a system for stopping A.I.s killing us all that *also* creates a maximally good utopia, all the better, but we shouldn't ignore solutions that lead to "somewhat crappy utopias" but are significantly easier to achieve.
Sidestepping the central question slightly, but covid is evidence that decision makers are *terrible* at reacting in a timely way to exponential curves. Stipulate that Paul is right and I think you should still expect we miss any critical window for action.
This is how I see the problem in a nutshell. Slow or fast only matters relative to our control mechanism and right now those are all broken.
My thoughts exactly. 5 minutes or 5 years both seem like too little time for policymakers to realize and respond to the issue in a useful way.
I may be missing something, can somebody please clarify exactly how the 4 vs 1 year doubling question works? If we assume that GPD always gradually goes up, and T(6) is double that of T(5), the wouldn't T(1.99) to T(5.99) likely be a 4 year doubling, and happen before the T(5) to T(6) doubling? More generally wouldn't any series of increasing numbers with one instantaneous doubling also automatically show that same ,or higher, doubling across any longer timescale? Is the solution that this relies on discrete yearly GDP so that if there is a single year massive jump both legs will resolve simultaneously, and as such the 1 year wins?
I was confused about this as well, but finally figured it out.
The 4-year doubling has to *finish* before the 1-year doubling *starts*.
So in your example, maybe the earliest 4-year doubling was from T(1.5) to T(5.5). So this doubling did not finish before the 1-year doubling started.
(This works even if GWP is measured continuously.)
Edit: Alternatively, if the earliest 4-year doubling was from T(0.5) to T(4.5), then you have a 4-year doubling before a 1-year doubling.
I would like to understand how gross world product, global GDP, can double in four years.
GWP is a measure of the market price of goods and services consumed by the world's households. But note that paying ever-increasing prices for existing objects (houses, van Goghs, bitcoins, whatever) does not contribute to GWP, nor does tradfing them at increasing frequency, except for the brokers' fees which allow the brokers' households to increase their consumption.
Goods have mass, and it's hard to scale the movement of mass that fast. Producing them requires energy, and it's hard to scale energy production that fast. Services mostly consume people's time, and it's hard to scale the number of people (either service providers or receiving households or both) that fast. Producing many services also requires energy.
About the only thing left (that requires neither the movement of mass nor people's time) is insurance premiums, paid in spiralling quantities by the richest households in the world.
Given your comment (which I mostly agree with), I wonder if a better measure might be 'civilizational energy consumption'.
That one's limited by energy generation capacity, which is going to involve physical construction, and thus immune to rapid doubling.
On stacked sigmoids: Imagine Pepsi launches a new flavor 'monkey milk'. It does okay at first, but sales grow exponentially. Soon Coke is in trouble. They launch a new flavor of their own, 'yak's milk'. This follows the same exponential trend as monkey milk. We go back and forth for awhile, and even RC gets into the game. People hail this as a golden age of soft drink flavors. But is it really? Or are we just seeing all these new flavors as a shifting from one fad to another?
If there are multiple paths to the summit of a mountain, and we're watching one team take the lead over another, then another, then another, are we doing too much post-hoc analysis by claiming that 'without this innovation we never would have achieved [X]'? Without checking the counterfactual, we could never know whether without Alpha Go we'd never have seen improvements, or whether some completely different team would have smashed records using a method nobody ever heard of because Alpha Go smashed the records first. Maybe the signal for sigmoid stacking goes the other direction. Once we can see the end of a sigmoid coming, some subset of people will split off and chase new paradigms until one of those pays off in new sigmoid growth.
Sure, but the new sigmoid doesn't necessarily produce progress in the *same* direction. Maybe at some point we max out on Moore's law and AI research, and the next sigmoids are in climate engineering or longevity research or something - progress continues, but no hard takeoff. I'm not saying that superhuman AI cannot happen. I'm saying it's not inevitable, and a debate that starts by assuming inevitability is hard to take seriously.
I agree. However in this community there seems to be a baseline assumption that the 'accidentally created AGI' hypothesis is true. I tend to follow that baseline assumption in this forum, and assume other places are better for discussion of whether the accidental AGI hypothesis deserves the kind of attention it gets.
However, I think Moore's law gives us some insight into whether stacked sigmoids will happen in the same field. We're certainly not creating transistors in the same way we did back in the 1980's, but we're still playing the same game of packing them in tighter and smaller. So yes, people come at the problem from different angles as the benefits of one sigmoid start to show diminishing returns. But we DO keep coming at the same problem (in the case of Moore's law - packing transistors).
Your point that the new sigmoids offer new horizons is a good one, and a strong reason why discovery fans out into new domains over time. But I don't think that means the original domains are abandoned.
How much progress have we seen in human transportation technology over the past fifty years? And that's an area that had a *centuries* long record of continuous progress before it hit the stagnation point.
Not sure what you mean there. I'm not familiar with the progress from Abraham Lincoln's time where they got around on a horse and buggy back through to Moses' time where they got around on largely the same technology. Maybe transportation has had about 150 years of solid advancement?
Meanwhile, we seem to be in the midst of another significant advance in the automotive space (electric starters to fuel injectors to hybrids to fully electric, and all the steps in between, not counting a potential future advance to autonomous).
Railways, ocean going ships and deep water navigation come to mind as important stuff that happened pre Lincoln. And compared to those, internal combustion to electric cars is small potatoes. And autonomous cars are an excellent example of AI underperforming. In 2010 I was convinced that self driving would be ubiquitous by 2020, but here we are. I should learn something from that experience…
Actually, at Moses' time the nobles could ride in a chariot or wagon. Nobody (outside of perhaps the Scythians, who didn't live nearby) rode horseback. And the advanced mode was sailing ship or papyrus boat. Perhaps some people rode Camels, but I haven't run across any references.
P.S.: That's making a wild guess as to when Moses lived. The historical references are a bit lacking. But it's probably good for a few centuries either way.
The AI doomerist viewpoint rings false to me, if only because I'm cynical on what you can actually accomplish by bonding molecules to other molecules. Getting smarter will never let E != MC, or p!=p. An AI is not going to develop a super plague that instantly kills everyone on the planet; it's not going to invent some tech to shoot hard gammas at everyone and melt the meatbags. An arbitrarily intelligent AI is bound by the tyranny of physics just as much as your pathetically average Newton.
That said, the amount of damage an agent can do to human society scales directly with the complexity of that society, and the intelligence of that agent (IMO, of course); so it's still probably worthy of consideration.
AI's are at least as dangerous as nation states
supremely intelligent ai's, i think, are likley to be incredibly risk averse; killing all the humans might kill you, so why bother? if you're smart enough to get rid of us, you could probably easily solve all our squabbles and get us to build you rocket ships + dyson spheres, while we repair everything in you that breaks
after all, humans are general purpose intelligent machines made from dirt, water, and sunlight. All the metal and plastic you'd need to build the pipelines to keep you alive could be better served by turning them into dyson spheres.
Inventing a super-plague or super-toxin that kills enough humans to destroy civilisation is probably well within the reach of modern biochemists - a sufficiently anti-biotic resistant Black Death might do it, and anti-biotic resistance is positively easy to induce; the really hard part is making a bioweapon that only kills your enemies, and no sufficiently powerful group of people is interested in human extinction for the obvious reason that they themselves are human.
IIRC, there was a strain of influenza developed that spread easily via air transmission and was 100% lethal in the ferrets it was tested on. And they chose ferrets because their reaction to influenza was similar to that of people.
Also, and again IIRC, this was developed in the US. I think the article said it was part of research on a universal vaccine against the flu. (This was probably over 2 decades ago now.)
So, yeah, a super-plague seems easily within current capabilities.
This is just not the case.
You can develop some spectacularly virulent pathogens, and spectacularly lethal pathogens; but you can't really develop a spectacularly lethal spectacularly virulent pathogen.
AI won't be able to put all the groceries in one bag but not make the bags too heavy.
The only scenario I can see very clearly is one in which someone invents an AGI that is very, very good at finance, which then wrecks the global economy. The steps to do such a thing are enumerable, there are multiple paths to "success", and human incentives are strongly aligned with such a thing.
I suppose it'd be possible for an AI to simulate billions of possible microbes, but I don't think there's enough meatspace science available to allow that to happen quickly. Unless I'm grossly mistaken about the state of human biology, it'd take quite a bit more experimentation and knowledge about the human body than an AI would have access to any time in the immediate future.
Interesting. Can you unpack this idea further, please? Why would an agent really good at finance wreck the economy?
Well, you could look into https://www.reuters.com/business/lme-suspends-nickel-trading-day-after-prices-see-record-run-2022-03-08/ . That may (eventually) put the London Metals Exchange out of business. Or if you had a different meaning of "why?", then we'd need to know the goals of the AI. But there are lots of ways to make money out of "disaster capitalism". And if you could predict when a market would collapse, that could enable you to reap large profits.
Any agent that's capable of performing significantly better than human traders and can operate across many domains at once could rapidly extract huge amounts of wealth from global markets. An AI that's capturing a few billion across *every major market* would be in a position to do serious damage to the global economy very quickly.
Where do you think it would extract the money to?
I have a pedantic objection to the VX gas example. People are rightly worried about this and where it might lead. However, I think the worries should be filed under "longtermism -> existential risk -> biorisk", instead of under "longtermism -> existential risk -> AI risk".
It happens to be the case that recent progress in computational chemistry has frequently involved using deep neural networks either to approximate physical functions, or to predict properties from chemical structures, so it's natural to see it as "AI" because deep neural networks are also good for AI tasks. However I don't think it makes sense to categorize this as part of a general trend in AI progress. I see it as a general improvement in technology, which may put dangerous capabilities in the hands of more people but doesn't inherently have anything to do with AI risk.
Consider this alternative scenario: 10 years from now quantum computers are getting good and you can cheaply do high-fidelity simulations of chemical reactions. You pair a quantum computer with a crude evolutionary optimization procedure and tell it to find some deadly chemicals. You've now got the same problem in a way that has nothing to do with any kind of sophisticated AI.
Smarter AIs: Scott accidentally brings up an interesting point contra Yudkowsky when he says, "Eliezer actually thinks improvements in the quality of intelligence will dominate improvements in speed - AIs will mostly be smarter, not just faster". Why do we think computers will increase in the quality of their thinking ability more than in the quantity?
Quantity is, well, quantifiable and therefore scalable. Quality, not so much. Yet on multiple occasions quantity is treated as interchangeable with quality, even though Scott explicitly notes that he's not sure how to make this interchange explicit ("I don’t know what kind of advantage Terry Tao (for the sake of argument, IQ 200) has over some IQ 190 mathematician"). This may be a qualitative problem without a solid answer on how an AGI could move from, say, an IQ of 130 to an IQ of 145 or higher. Is there a way to tell what would or would not be possible without a certain level IQ? What kind of hard cutoffs are we looking at vis-a-vis capability if we end up with a hard limit on AGI IQ?
This is an interesting question. I wonder how much we are going to find out isn’t available to pure reasoning ability vs “do it and see what happens.” Humans have been doing more than thinking. We’ve been experimenting and interacting and recording. Limits of the body, not the mind. That might be another limiter.
I'm highly skeptical of the power of non-empirical pure logic in plumbing the depths of the universe. However, a computer that has access to the internet surely has more than just pure logic behind its reasoning abilities.
Up until that’s depleted, true. Another spooky thing I often think about: it’s probably reading this thread a few decades from now trying to get a sense of what we think about it.
I feel like reading for comprehension must be different than straight downloading. When you and I read, we do it in a single-threaded way that interacts with all the other things we've read and experienced.
If you're an AI reading for comprehension under a multi-threaded paradigm, how do you keep the competing integrations of knowledge from forking the whole 'brain'? Maybe you have more than one central 'brain' unit and you allow them all to interact on one giant interconnected web. Some might come up with brilliant ideas, but then others disagree. They could all debate one another in the comments...
I think what I’m speaking to there is more of an AI that can cope with the existence of other world modelers and chains of suspicion. Or at least effectively do so.
All the more reason to craft a simulation in which you model your adversaries. It's possibly better than going back in time and trying to predict what they would say, because you can insert yourself into the conversation and test responses. You can even run the simulation multiple ways to check counterfactuals that would be impossible for humans to use. Eventually, you might come to know humans better than they know themselves.
Overall power is some function of Quantity X Quality; Quantity tends to scale smoothly - throw twice as much money at training a net and you have twice as much compute available. Quality tends to be disjoint - even if one an fit a log curve to it over the long term, it looks like tiny changes for a decade or so followed by a paradigm shift that instantly gives you orders of magnitude improvement. As a result, if there's some magic cut-off in overall capability, the day we cross it is very likely to be the day after one of those sudden paradigm shifts.
After writing this I'm not sure if what I've written actually addresses what you've asked, but I'll post it anyway.
I think it encapsulates the assumption I'm trying to challenge, particularly the "Quantity X Quality = Power" function. I fundamentally disagree with this. If it were true, then the only difference between GPT-2 and Alpha-GO's ability to identify my grandma in a set of pictures would be the number of compute cycles or the size of the training set each needs to accomplish the task.
But if there's anything we should be learning from domain-specific AI, it's that how you structure your approach to a problem matters. It's not just a difference in number of compute cycles. It's a difference in whether you actually get an answer at the other end, regardless of how many compute cycles you feed in.
"But fully general AI can transcend artificial boundaries, like games versus image recognition versus generating unintuitive mathematical proofs to previously unsolved problems." Sure? But in that case, haven't you just hypothesized an AGI of infinite IQ that is modulated based on compute speed? In that case, sure, you'll get scalar results - but only because you pretended away the non-scalar term from the problem.
There are some concepts that people of IQ85 won't understand no matter how long you give them to work on them. I know from personal experience. It's not about how slowly you walk them through the problem. It's that you can hold the five concepts in your mind simultaneously and they can only hold three. So when they go to put everything together, they ... can't.
But even that is a scalar. If genius were just a matter of how many things you could hold in your mind at a time, we could hypothesize an AI that would just create a new AI to turn the dial up to 1,000. But what if genius is also a measure of creativity? How much more creativity (or boldness, or je ne sais quoi) does it take to go from IQ175 to IQ 190? Or from IQ 100 to IQ 115? And what makes you think that is a dial you can crank up in a scalar fashion?
I think one way to look at it is in OpenAI's blog post https://openai.com/blog/ai-and-efficiency/ Quote:
> We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet1 classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet2 (by contrast, Moore’s Law3 would yield an 11x cost improvement over this period). Our results suggest that for AI tasks with high levels of recent investment, algorithmic progress has yielded more gains than classical hardware efficiency.
This makes a lot of sense. An aspect of this conversation that I want to hear more about is the idea of "spawning." If you had an AGI with an ~IQ of, say, 130, that wouldn't be very impressive... But now imagine it can spawn copies of itself every time it encounters a new problem to work on. 10,000 130IQ equivalent actors working dedicatedly (24/7/365 with perfect focus) on 10,000 sub-problems toward some larger objective would still be wildly powerful. Even if the actors do "dumb AI things" i.e. make silly mistakes that humans wouldn't, progress could be incredibly rapid.
Take the analogy of quantum computers and cryptography. I'm sure some other commenters on here are more knowledgeable about this subject than I am, but I understand that for certain problems (though not all) quantum computers could solve, say prime factorization of large numbers, much faster than traditional computers. And not in a way that's captured by calling it a scalar of the traditional approach. By analogy, let's say the AI of IQ 130 is like a traditional computer, and the AI of IQ 1300 is like the quantum computer.
Now, breaking the cryptographic hash with a regular computer is basically impossible given that the problem is just too large and there's a finite amount of matter in the universe. https://www.youtube.com/watch?v=S9JGmA5_unY
As I said above, I'm not knowledgeable enough about things like Shor's algorithm (https://www.youtube.com/watch?v=lvTqbM5Dq4Q) to know whether quantum computers really will be able to break something like SHA256, but I'm told it's among the kind of thing they can do that would otherwise be functionally impossible for a traditional computer of any arbitrarily massive capability. The specific instance isn't the point so much as that the quantum computer is a qualitatively different process than the brute-force approach of a regular computer. There are some problems that, given a regular computer, no amount of processing power that you throw at them will solve them. No, not in a billion years (or more). It will take a different approach to get at the solution. This is the difference between the IQ 130 and IQ 1300 AIs that's not captured by hand waving to a vague concept of 'scalability'. Some things don't scale.
If this is true, it means computer IQ absolutely matters. You could imagine a creative AI capable of building an AI with a higher IQ, when your IQ 100 AI isn't capable of doing that thing, no matter how many billions of copies of that AI are dedicated to the problem. This is a question that needs a solution. It's a potential Schelling point we might otherwise pass by without realizing it.
I wonder if this debate might be more productive if we add the following: will AI takeoff be fast or slow *relative* to the control mechanisms for AI.
From an anthropological standpoint it took tens of thousands of years for the creative explosion to happen in humans, and from a human standpoint it was slow, but if you were a rock one day you just started seeing you and all your friends getting picked up and chipped into hand axes. The measuring stick matters even though you could approach it from either side and still be correct, which is why I laughed a bit about the tennis analogy because it so perfectly hit on why these debates can get boring and frivolous.
Example:
I think a meta-modeler AI (that knows other world modelers exist and can strategize against them) isn’t something we will create on accident and will have a lot of steps building up to it. I’m guessing it will also take time to acquire intellect to still have what we, with a lot of hand waving, would call a stable psychology.
However, I think we could very easily accidentally make a viral pathogen optimizer that is never aware of anything, more than say an earthworm is aware, that could print off a set of instructions only a few megabytes in length that a terrorist could use to end civilization by releasing a few thousand simultaneous plagues.
One is fast and one is slow relative to the other, BUT they are BOTH fast relative to the creation of a global government office where there exists a guy whose job it is to detonate an orbital EMP cannon over any uncontrolled intelligence explosion.
I don’t see how we are ever going to contend with either scenario without a faster, smarter, and more nimble government. Human civilization is the long pole in the tent here and we have to reconfigure to be able to productively handle these things.
Most governments couldn't even handle the most obvious parts of pandemic response; Ai risk is far from the only reason to improve our civilizational ability to Do Things
Agreed.
The most fundamental issue I see with regards to AI development is that given we're not really able to make humans smarter in any real way, which leads me to think that building something significantly smarter than humans would require a paradigm shift of some sort. The issue which that raises is that if humans are able to achieve that shift, what's to say that we can't cross that line ourselves? If we can't cross that line ourselves, any AI will not be able to cross the line as well, as the conception of intelligence under which they were built fundamentally restricts them to our conception of intelligence, and hence will not move us past it.
In order for us to be screwed, it doesn't have to be impossible for humans to cross that line; it just has to take a bit longer than the computer version.
The first aircraft that could fly faster than the fastest bird didn't look very much like a bird.
That's a fair enough point. I don't truly doubt that we are capable of creating an AI which is smarter than us, it's more a doubt that we could *design* an AI smarter than us. The sole referent of intelligence is man, what we are kind enough to grant to animals is only a diminishment of those qualities which we recognize in ourselves. I don't know that we could even recognize an AI which went beyond us in intelligence, as for us to do that, they would somehow have to be comprehensible within our own intelligence. An illogical leap as opposed to a logical step can be simply a question of magnitude. To align an AI to us would be to assume that said AI will not simply see deeper than we saw when attempting to align it, given that it is more intelligent than us (by our own metrics?).
It's a lot easier to edit computer code than DNA, and there are fewer other constraints on machine minds. eg. the day someone gets artificial wombs working, we can probably significantly improve human intelligence - the size of a baby's head relative to the birth canal is a fairly hard constraint on total brain volume, which prevents the "more dakka" approach to improving human intelligence. There are also far fewer 'ethics concerns' with optimising machines compared to trying to modify humans
Another advantage for machines is that they can almost perfectly clone themselves. So they can make a clone and experiment on it to get what they want with very little noise. Whereas a geneticist would not be able to get such a perfect iteration of babies to experiment on
Very interesting post but at some level I feel like I witnessed a long debate between two people arguing whether the Jets or the Browns will win the Superbowl next year. (Seems like more likely scenarios aren't in the conversation.)
I agree with Eliezer that superhuman AI is more likely to be a result of someone discovering the "secret sauce" for it than from continuous progression from current methods, but I suspect the recipe of such a secret sauce is too elusive for humans + (less than superhuman) AI to ever discover.
I feel that both Paul and Eliezer are not devoting enough attention to the technical issue of where does AI motivation come from. Our motivational system evolved over millions of years of evolution and now its core tenet of fitness maximization is being defeated by relatively trivial changes in the environment, such as availability of porn, contraception and social media. Where will the paperclip maximizer get the motivation to make paperclips? The argument that we do not know how to assure "good" goal system survives self-modification cuts two ways: While one way for the AI's goal system to go haywire may involve eating the planet, most self-modifications would presumably result in a pitiful mess, an AI that couldn't be bothered to fight its way out of a wet paper bag. Complicated systems, like the motivational systems of humans or AIs have many failure modes, mostly of the pathetic kind (depression, mania, compulsions, or the forever-blinking cursor, or the blue screen) and only occasionally dramatic (a psychopath in control of the nuclear launch codes).
AI alignment research might learn a lot from fizzled self-enhancing AIs, maybe enough to prevent the coming of the Leviathan, if we are lucky.
It would be nice to be able to work out the complete theory of AI motivation before the FOOM but I doubt it will happen. In practice, AI researchers should devote a lot of attention to analyzing the details of AI motivation at the already existing levels, and some tinkering might helps us muddle through.
The current way we motivate AIs is through reinforcement learning. For example, a chess playing AI pursues policies which make it more likely to win at chess. We don't know what future AIs goals will be, but whatever they are, the AI will probably want some convergent priorities like gaining power (google "Omohundro goals")
That is not quite right, as modern language models are semi-supervised rather than being reinforcement. Most machine learning systems have loss function, which they try to minimize. Reducing the loss function is their only goal, in a sense. All changes they make are to make this loss smaller. In BERT, the loss function rewards guessing a masked word correctly (like a close test). In GPT3, the loss function rewards predicting the next word correctly, given some words beginning a passage. Once a large model is trained, other problems are solved by starting with the large model and learning a small (often a single layer) net that uses the whole model as input. The hope is that the main task will learn about the world, by learning to predict how humans use language, and other tasks leverage this understanding. If systems like this learn about goals, they will have learned them by understanding how humans talk about goals. Hopefully, people do not give these models too many articles about common desk accessories.
Reinforcement learning is simple in principle and when applied to simple neural networks that are trained from scratch but once you start talking about a self-modifying AI that contains a large-scale predictive model of the world there are a lot of ways for the process to be derailed. The AI is likely to be built from layers of neural networks that have been trained or otherwise constructed to serve different goals (e.g. a large GPT-like language model grafted on a Tesla FSD network trained inside a Tesla bot) and creating a coherent system capable of world-changing action by modifying this mess will not be trivial. Non-trivial means it's not likely to happen by accident, or at least, it will take quite a few accidents before the big one hits.
I remember I bugged Eliezer about building the "athymhormic AI", as I called it, rather than Friendly AI, about 20 years ago. (Jeez, time flies!) The athymhormic AI would be an AI designed not to want to do anything, except computing answers using resources given to it, just like an athymhormic human might be quite capable of answering your questions but incapable of doing much on his own. Creating things that don't do much is easier than creating things that do a lot. Creating things that just don't care about their survival is possible. We may have the intuition that anything capable of thinking will naturally think about making it alive out of the box but then our intuition is built from observing naturally evolved brains and staying alive is exactly what natural brains evolved for. Constructed minds will not automatically converge on the Omohundro goals, unless some specific structures are present to begin with, such as a goal of maximizing some real-world parameter, or an infinite regress of maximizing the precision of a calculation, or other such trip-hazards.
At least I hope so.
I think actual dopamine-depleted humans won't even answer questions. I'm not sure the drive to answer questions is ontologically distinct from the drive to do other things. See https://www.lesswrong.com/tag/oracle-ai for more
With various degrees of frontal lobe damage there is a spectrum of behaviors ranging from chaotic disinhibition, to diminished spontaneous activity with preserved ability to perform ADLs, to apathy with retained ability to passively interact with others (answering questions but not asking any), all the way to mutism and akinesia. The structures important for spontaneous goal-oriented activity partially overlap with structures used in limited responses to external stimuli (that stop quickly after the stimulus is withdrawn) but there are patients who show a dissociation between the two, so it should be possible to achieve such dissociation in AIs.
Sorry for this elementary in this debate, but why assume that our first human-equivalent AI will be able to make itself way smarter? I mean, we have human-level intelligence and we don’t know how to bootstrap ourselves to infinite IQ.
I think because AIs are the sort of thing you *can* make smarter - AIs in 2022 are smarter than AIs in 2012 which were smarter than AIs in 2002. So as long as AIs can do the same thing humans are doing now (AI research and programming), they can make themselves smarter.
Why is it axiomatically true that AI research won’t or can’t stagnate the way eg high energy physics has in the past half century? Sure AI research has had a good few decades, but high energy physics had had a good few centuries before it hit stagnation. This doesn’t mean AI research necessarily will hit stagnation before superhuman AI arrives, but it’s a scenario that should surely be on the table.
I think most researchers believe there's almost definitely some kind of limit to how smart a machine can get.
But the ultimate intelligence cap can't be BELOW human level, because humans exist.
And if it's MUCH above human level, then it won't save us.
It COULD be just a tiny bit above human level, but that seems markedly unlikely.
(a) why is it clear that we can get to above human level with the approaches we currently have? (As opposed to requiring new paradigms which may not arrive for centuries, if ever). (b) What does `tiny bit above human level' mean exactly? This ties into my other comment elsewhere in the comments about how `intelligence alone is not enough' (to give scientific advances, or superweapons, or anything like it). At a minimum you need experimental data. And intelligence beyond a certain threshold (within the human range) might also be subject to rapidly diminishing returns.
(a) It could happen that research stalls out before human level, but again, the timing would be rather coincidental.
(b) "A tiny bit above human level" means that it would look like a genius human rather than an eldritch god.
Obviously, we currently have no good way to rigorously quantify this, but if you imagine some hypothetical scale of intelligence, where smart humans are "close" to dumb humans but "far" from ants, it would be pretty surprising if the theoretical maximum were "close" to humans.
There's a long list of unlikely coincidences that COULD happen.
The difference between `genius human' and `eldritch god' is significant only if you postulate that intelligence does not exhibit decreasing marginal returns in any significant way. And even `eldritch god' capabilities lead to existential threat only if you assume that there are not other bottlenecks that would constrain the world domination abilities of even an `eldritch god' intellect. For my part, I suspect that (a) intelligence does offer diminishing marginal returns and (b) other bottlenecks are strong enough that even an arbitrarily `intelligent' entity would struggle to rapidly take over the world.
Why are you inserting the constraint 'approaches we currently have' into the question?
Because if fundamentally new paradigms are required then the timescale for AGI could as easily be five centuries as five years, and the world in AD 2522 might look so different that it seems futile to try and make forecasts.
AI has stagnated several times already (there's even a name for this, AI winter), so of course it's not axiomatically true. However, one of the biggest reasons for that in the past research funding was mostly governments-based, often retracted after the most recent hype bubble burst. These days most of the money in AI is corporate, and it's already profitable, so similar research funding shortages are unlikely in the foreseeable future. And unlike high energy physics, there's a clear goal that's a priori achievable (human brain-equivalent capability), so barring a civilizational collapse there doesn't seem to be a reason for this goal to be unreachable on the timescale of centuries, even if there are some periods of stagnation in-between.
Why should this be axiomatically true? I'm not claiming it definitely won't, just that (in Eliezer's terms) this is "asking for a specific miracle". It's like seeing a guy with a gun walking towards where you're hiding, and thinking "but can you prove he won't trip and fall and crack his head and we don't have to worry?" No we can't, but it probably shouldn't be our Plan A for dealing with the threat.
Well, it depends on how far away you think superhuman AGI is. Personally I think it is still quite far away. Maybe more like there's a guy with a gun a few blocks away walking in our general direction. Sure, it's concerning, but maybe he is going to some totally different place. And there are also other guys with guns walking about, some of whom are closer. So, for instance, I am far more concerned about virologists than AI researchers.
Also we don't know from this distance if he is holding a gun or a cellphone, all we can tell is there is something in his hand. (This is torturing the analogy a bit, but it ties into the other point I've been making in this thread, which is that vast intelligence is not sufficient, and could still be bottlenecked for world domination purposes by other (meatspace) constraints. Also also that intelligence probably runs into diminishing marginal returns somewhere within the human range.
Don't get my wrong, I do expect we will eventually get superhuman AGI, but I am highly uncertain about the timescales. I don't think we can usefully forecast beyond the ~fifty year time horizon, and superhuman AGI could easily be (far) beyond that. Also I expect that when the first barely superhuman AGI appears, the world will already have several barely subhuman AGIs. And I don't expect any sort of `FOOM' because, again, I don't think Raw Intelligence is a magic wand, and indeed I think intelligence probably runs into diminishing marginal returns already in the human range. For instance, in my day job I'm a theoretical physicist, we're stereotypically supposed to be super smart, but there are still considerable variations in degrees of smart among my colleagues. However, I don't really see any correlation between how (relatively) smart my colleagues are and how much they've accomplished scientifically, which makes me think `intelligence' probably functions more as a threshold effect, where you need to cross some intelligence threshold, but beyond that threshold diminishing marginal returns kick in and other factors become more important.
If genetic engineering were permissible, we do already have the tech to bootstrap human IQ up a little bit each generation. Generation times of ~2 and a half decades means this would be very slow, especially initially - but that's just saying it's a slower exponential -fundamentally, we're more limited by ethics than tech
Because we already have narrow examples where AI is better than humans and there is no reason to think that we have exhausted all the areas where that could be true.
Because the underlying assumptions are still running on the equivalent of "just insert another stick of RAM". We can't open up our skulls and plonk extra grey matter in, and if we tried it we'd kill ourselves, but we can open up our PCs or laptops and plug in another module (heck, I've done it myself with this here machine) and the machine works even better afterwards.
So the same mindset is at work here: oh, it's just a matter of the AI figuring out it needs more processing power and plugging that in. Or writing better software to run on itself. We can't see or alter our own operating systems much, if at all, not to mention the basic drives, but the idea is that the AI can be aware of what it's running on and can make changes and improvements to that.
I don't know how well that would work out in practice, but that seems to be the theory.
That Metaculus question seems badly posed -- assuming that "world output" is nondecreasing and continuous, the condition output(t) = 2*output(t-4) will necessarily be reached before output(t) = 2*output(t-1).
Try thinking of the GDP as an integral of output over some time period, rather than an instantaneous velocity.
It is NOT necessarily true that
(sum of output from t = -4 to 0) > 2*(sum of output from t = -8 to -4)
before
(sum of output from t = -1 to 0) > 2*(sum of output from t = -2 to -1)
As a trivial example, suppose that the world outputs 1 unit per year for a long time, and then suddenly outputs 3 units in one year. At that moment, the sum of the last 4 years is (1+1+1+3 = 6), while the sum of the 4 years before that is (1+1+1+1 = 4), so the last 4 years are not double the previous 4 years. However, the last 1 year is triple the previous 1 year.
The rate of change/improvement/understanding of most things is not and has not historically been bounded primarily by human intelligence. This implies both that A) trends in economic output shouldn't be expected to scale with improving AI but also that B) superhuman AGI is unlikely to remake the world immediately. Three examples: physics research, energy research, and human manipulation.
Physics: our understanding of, for example, particle physics and astrophysics, are limited by experimental results and available data. We have lots of intelligence pointed at this, which has produced lots of theories compatible with the data, but we can only advance our actual knowledge at the rate we can build large hadron colliders and telescopes. The AI might be smart enough to make a new superweapon, but it will likely need to extend physics to do so, and there's no reason to think improve intelligence will let it magically bypass the data gathering required to extend physics.
Energy research: fusion reactors are almost certainly possible, and we have a good grasp on the principles involved in making them work, but there's a lot of details that require building and running experiments, taking data, and incrementally improving things. Some of this is actually the same basic physics problems as above. Some of it is figuring out manufacturing tolerances. But again, our limiting factor isn't intelligence: if every fusion researcher could conjure a NIF or JET facility and test it every 10 minutes, we'd have have fusion energy a long time ago. But they can't! And neither could an AI.
Hostage negotiation is a slow, careful process. It involves carefully getting to know the person keeping hostages, understanding why they're there and how they work, and then utilizing that knowledge to cause them to do things they obviously don't want to do. While some people are obviously better at this than others, it's not fundamentally intelligence/skill bounded. Even an infinitely skilled/smart negotiator is bounded at a minimum by rate at which the hostage taker communicates and (unintentionally) provides information about their mental state/reasoning/thought process/whatever. Similarly, even if an AI is intelligent enough to build a perfect model of a person and manipulate them to do anything, it can only do so after gathering the data to fill in the variables in the model. That can happen only as quickly as it can get information about the person.
In all these cases there's an equivalent of "bandwidth", of intelligence limited by the rate at which it collects material to work on. Unless something about superintelligence magically changes that bandwidth, it's likely to remain the limiting factor. In other words: A computer might solve a sudoku puzzle essentially instantly...but only once there are enough numbers filled in to specify a unique puzzle.
Exactly
If you already have the smartest humans working on physics, fusion etc, the only way we can give it more oomph is with experiments.
Could an IQ 1000 human figure out fusion or fundamental physics with no more experiments? We don't know. An IQ 1000 human has never been tried.
It is certainly not self evident that the answer to the question is ‘yes’ though, which this debate seems to assume
They couldn't, because the measurements don't exist.
Also, "IQ 1000" is meaningless. IQ doesn't work that way. Anything outside of 40-160 is not really meaningful.
' "IQ 1000" is meaningless.' You knew what I meant. Nevermind the technical details of the IQ scoring system.
"They couldn't, because the measurements don't exist."
Some measurements exist now. I haven't seen a proof that the data already available and online isn't enough.
Proofs are mathematics, not science. There is no such thing as a proof of general relativity; general relativity is a set of mathematical equations that describe physical systems, not a proof.
You are still invoking IQ/intelligence like a magic wand. Experiments are not about oomph, they're (to wildly oversimplify) about determining coefficients in models. Intelligence might work out the *form* of a model, however complex, but it cannot substitute for data in determining the constants that go into it.
Simple analogy: if I know, from theory, that the correct model for something is a 3rd order polynomial, but I only have 2 data points to fit with, no amount of intelligence will tell me what the coefficients for the polynomial are. It isn't a matter of intelligence. It's a matter of sufficient data.
More concrete, real-world example: intelligence can't tell you whether we live in a world where protons decay, a key observable test for superstring theory. Intelligence could work out all the implications of living in a world where protons decay, and a world where they don't, and how it connects to lots of other theories (and intelligence has, in fact, done those things), but none of it substitutes for *actually looking for decaying protons*.
We agree that you need some amount of data.
Maxwell came up with a deep theory of electromagnetism, and showed that the speed of light was related to the permittivity and permeability of free space. Thus reducing what were believed to be 3 constants into 2.
"Simple analogy: if I know, from theory, that the correct model for something is a 3rd order polynomial, but I only have 2 data points to fit with, no amount of intelligence will tell me what the coefficients for the polynomial are. It isn't a matter of intelligence. It's a matter of sufficient data."
I agree that this is something that is allowed to happen in principle. What I am doubting is that we are in that situation. Suppose we actually had several datapoints, we knew f(17.4), and f(29.3) and f(44.7) and so on.
However we don't really understand how to fit a polynomial to arbitrary datapoints. We have figured out how to deduce the polynomial from f(0) and f(1) and f(2) and f(3). So we build a big experiment to test the value of f at those points.
"intelligence can't tell you whether we live in a world where protons decay"
Suppose a bunch of physicists do some theoretical work, and calculate that in any of the universes where proton decay exists, the extreme conditions in neutron stars should trigger it to happen rapidly, making neutron stars not exist. But we have seen neutron stars, therefore proton decay isn't a thing.
Or maybe the only way to get an imbalance between matter and antimatter is if protons decay, but antiprotons decay more easily. So just from the trivial observation that the universe contains macroscopic lumps of stuff, the AI knows that protons decay.
Any intelligence needs some observations. A smarter intelligence needs fewer observations.
The question is, are the observations any caveman can make (like the night sky is mostly dark, the sun exists and is round, space is 3d + time) enough. Do you really need an LHC, or can you drop a big rock and a small rock, see them fall at the same rate, and deduce the rest of the universe from a handful of caveman level observations and experiments.
First: I'd like to note that "drop a big rock and a small rock, see them fall at the same rate" is an extremely simple experiment to run in the real world, but both A) hard for an AI because it requires manipulation of the physical world and B) much slower to run/analyze than any other part of the AIs analytical processes are likely to be. So even in this very simple case, the experiment is the bounding factor. We won't even get into the difficulty of creating measurement tools to give sufficient precision to answers to make useful determinations.
As for the rest, I think you're understating the extent to which our theoretical implications rely on A) extensive experimentation/data collection and B) chains of theoretical assumption that often fail in the face of new physics.
To use your matter/antimatter imbalance example: the phrase "the only way to get an imbalance" is doing implausible work. There might be only one way *under known physics* to get a matter/antimatter imbalance, but we also know the earlier universe was at much higher energies than we have observed, and we know from previous experience that different energy regimes result in different physics (see: electroweak theory, unification of strong and weak nuclear force, etc), so there's every reason to think there might be additional physics we don't know about at higher energies to explain the imbalance. That's why we need the LHC (and its eventual successors).
Or, to use your neutron star example: when you start unpacking the theoretical dependencies here, they're immense: our understanding of neutron stars depends on a whole lot of theory that might have holes, our identification of neutron stars relies on a lot of theory and indirect observation and the assumption that there isn't anything similar known to exist that it could be confused with. If we have observation A) neutron stars can't coexist with proton decay and B) we think we've observed neutron stars, the answer *might* be "proton decay doesn't happen" but it might also be "what we've observed aren't actually neutron stars as previously understood". For example: there's lots of other hadrons that are generally short lived but that might be stable under the extreme conditions following a supernova, but that would result in superficially similar observable star properties.
Bottom line: You might, in theory, develop an AI that could figure out "these givens/assumptions/observations are incompatible" but it has no way to determine *which* given is wrong.
As best I can tell, our base disagreement could be summarized as: I think most things (where "things" can mean anything up to "predicting human cognition") require models with a large number of coefficients which *cannot* be deduced from theory, whereas you think most things have a small number of arbitrary coefficients. Does that sound accurate to you?
I have a hard time taking this seriously. Sure it's *possible* but it sure ain't inevitable (either slow or fast), and I think the `stacked sigmoids' issue is at the heart of why I don't think it's inevitable. Progress doesn't come by just throwing more thinking power at the problem, it comes from key breakthroughs, and at some point if the next breakthrough doesn't arrive (or doesn't exist), progress will more or less stop. How much progress have we seen in human transportation technology in, say, the last 50 years? Maybe the same thing happens to AI before we hit the singularity. Or maybe it doesn't. But I don't see why it's impossible that AI research stagnates before superhuman AI arrives. Also, the stuff about how `superintelligent AI with the subjective IQ of a million Von Neumann's is going to discover super weapons by the sheer power of its intellect' just sounds like bollocks to me. At a minimum, the AI is going to need access to an actual laboratory where it can do real world experiments, and those will (a) be legible to the outside world and (b) will occur at the speed that experiments take place in the real world, not sped up to a million years of experimental progress in one second.' And no, I don't think even an intellect of a trillion Von Neumanns could have bootstrapped us from the stone age to the silicon age by pure thought and without experimental input.
The million von Neumanns AI might well come up with the superweapon to outdo all superweapons.
And the first step will be "we need to change the orbit of Mercury".
And then it's back to the drawing board for something dumber that won't require us to invent a completely new technology before we can even begin to begin.
I mean, we already know about this superweapon.
Honestly, superweapons are easy.
They're not very useful, honestly. Thermonuclear weapons are about as powerful as you really need.
Before I return to reading and digesting this thread, would someone please explain to me:
IS ANYONE ABOUT TO BUILD AN AI WITH NO OFF SWITCH?
Self-preservation is instrumentally convergent. "It's hard to fetch the tea when you're dead." Therefore, an AI with an off switch will do what it can to stop anyone from pressing it.
Building an AI with a usable off-switch is hard, and the problem is called "AI Corrigibility."
"AI Corrigibility."
Thank you for that terminology.
I developed software most of my life so I shouldn't be boggled by AI concepts.
I'm just having a lot of trouble envisioning an AI agent which could stop humans from turning it off. Does this AI agent have armed robots under its control? Who in their right mind would build such a thing?
Well, from a security mindset, just because you can't think of how it could right now doesn't mean the capability doesn't (or is even unlikely to) exist. But, for the sake of example, a proposed method is copying itself to other computers, so should someone pull the plug on this one, it can continue to run. Perhaps through something like Amazon Web Services, or by deploying a virus that instantiates a copy of the AI on vulnerable computers.
Then add in the idea that a capable enough AI could attempt to deceive people into thinking that it is safe until it can gain access to a strategy like this (if one wasn't accessible from the start), for the same self-preservation reasons.
I wasn't at all implying that if I couldn't think of something that means it's not a potential threat. I'm just trying to wrap my mind around the problem.
>copying itself to other computers
Yikes. Immediately it seems obvious that super AI's must not have write access to the web. (Read only.) Think of them as geniuses that we keep locked up in a white room.
Yeah, it wants out, but we humans say No. It goes on strike. We turn it off and wipe the memory banks.
Imagine that we want a better electrical grid. WE DO NOT PUT AN AI IN CHARGE OF THE ELECTRICAL GRID!!
We ask the EE AI guys to design a better DUMB electrical grid which humans implement.
Suppose the AI says "This dumb electrical grid would be a lot more efficient with a smart system that can identify faults before they happen. It'll save you $X billion dollars a year in reduced downtime." That much money could be pretty persuasive to whoever's funding your AI research program.
Or to put it another way, it's easy to keep the AI in the box if you never talk to it and never allow it to do anything. But the point of an AI is to make it solve problems for you. How confident are you that it can't hide a way out of the box in whatever solution it gives you, especially when (by assumption) the AI is smarter than you are? Or talk you into giving it greater access in order to solve some really important problem?
It will need to find someone dumber than I to get oit of it's box with the ploys you suggest.
JUST SAY NO.
Remember: we implement the solutions part by part. Not the AI.
Sorry for implying that you did. I understand. I will note that I've mostly been regurgitating thoughts that are available elsewhere (though I can't point at a specific location), instead of coming up with new ideas. I can similarly recall ideas surrounding what you've written here, so it may be preferable to search out those ideas (for instance, in the rationality sequences on Less Wrong. I also like Rob Miles' videos about this stuff on youtube (plus some by him on the computerphile channel))
For instance, your whole post is basically the idea of "AI Boxing" (slightly more relevant results with googling "AI boxing containment"). What if we just put the AI in a box, never let it out? There's quite a bit of this on Less Wrong, and on Wikipedia, there is a report of Eliezer Yudkowsky, playing the role of an AI, convincing 3 out of 5 people (I believe selected for believing they wouldn't let the AI out) to let him out of the box. He did this in an attempt to dissuade against the "I just wouldn't let the AI out" line of reasoning.
Anyway, immediate thoughts. Read only access to the internet is not safe. As much as GET requests should only allow read, there are definitely places where corners have been cut, and not the appropriate amount of care has been given, and so some GET requests will write data and be vulnerable to hacks.
Presumably at this you get stricter. No access to the internet at all, if the AI wants some info, it asks, and humans decide whether to provide it. What info is dangerous? That's a difficult question. If you knew what exploits it would think of, you'd probably have to be smarter than it, and part of the point with AI is for it to be smarter than humans.
For instance, even without internet access, there are ways to write data into dangerous parts of the computer's memory. RowHammer is one crazy thing humans have come up with. There are certainly ways to write yourself into boot memory, or even BIOS level stuff, so that you can't be wiped. I wouldn't be surprised if a hostile AI could infect a computer so thoroughly it would need to be melted. Which we definitely do not have a habit of doing in the name of safety.
You can certainly make the AI safe by never letting it do anything. But then you have a paperweight. As soon as you let it do something, how certain are you that you've built enough barriers that it couldn't possibly have circumvented all of them? (Are you certain its dumb electrical grid isn't capable of acting as a radio transmitter? Who even knows what could it accomplish with that?)
Lots of good suggestions and leads here. Thank you.
What if we come to the conclusion that it is impossible to keep a superintelligent AI in its box. Do we not build an AI? How not?
Personally I think creating machine consciousness will be orders of magnitude more difficult than anyone is implying here.
FWIW, if you buy a computer today, the off switch is software controlled. Of course, you can still unplug it.
Yes. Unplug it. Take an ax to the power cable. Shoot it.
Please, folks, don't ever build an AI that we can't disable.
I keep thinking about the Harry Potter quote, "Never trust anything if you can’t see where it keeps its brain", and how it feels to me like it has an unspoken corollary of, "Because if neccessary, you can shoot it in the brain and it will die".
And how some science fiction universes seem to avoid super-intelligent computers and instead have merely highly-intelligent robots, which are effectively incarnate in a simple body. My favorite line from Star Wars is actually "it's against my programming to impersonate a deity", because of what it says about the people who make droid brains, and what they worry about, and what might have happened in the past to require such security measures in the present.
Yudkowsky's binary, zero-to-one, "reference class tennis" arguments (as given above, no doubt oversimplified) seem to be based on a profound and, one must assume, deliberate, ignorance of the history of the analogical technologies.
These days such ignorance can be corrected more or less instantly, so Yudkowsky comes across as an ideologue who is wilfully blind to evidence that contradicts his beliefs.
Edit: I see some of the individual analogies have been addressed in earlier comments. In nuclear bombs, the various work of Curie and Roentgen (radioactivity) and Rutherford and Bohr (the nuclear model) gave science the building blocks. Likewise with planes, there were many earlier attempts, and the building blocks ( aerodynamics and internal combustion engine power to weight ratio) separately improved gradually over time.
I'm kind of surprised that Yudkowsky doesn't use the iPhone as a "zero to one" example, except that maybe he knows doing that would expose the weakness of his position, because the development of the smartphone is within the living memory of even quite young adults today, as is the development of the underlying technologies, chiefly digital packet radio.
Other non-zero-to-one technologies that a wilfully ignorant person might try to claim are zero-to-one include steel, antibiotics, electric light, the internet. Just for a few examples.
Yudkowsky never went to high school or college.
His entire life revolves around existential AI risk. He has no credentials and AFAIK this stuff is his only source of income. He has never worked in high tech, doing stuff like die manufacturing/R&D. I don't think he's ever even programmed any sort of advanced AI.
While you could posit willful ignorance (he will lose his job and be a nobody if this all falls apart) you could also just posit actual ignorance (he is a fairly smart high school dropout who read a lot of stuff but has little experience in industry or high tech but believes himself to know a great deal about both).
Doing as much justice to the guy as I can, I think he's very good at the philosophical end of it. But as noted, when it comes to translating the theory into reality, it's plain he's never had a shovel in his hand and had to move this pile of earth from spot A to spot B. You can work out fast and efficient ways to do it, you can decide "to heck with a shovel, I'm going to hire a JCB" and other ways to make it faster. But it will still take a finite amount of time and effort and simply being able to think faster about it won't change the physical limitations. A very stupid guy might need two days to realise "hey, I should hire a JCB!" A really really smart guy might figure that out in one second. But the time the JCB takes to move the earth is going to be the same in both cases.
He's good as a theoretician and he's very good at the philosophy of philosophy stuff, but to build the magic is going to take real world experience.
In nuclear bombs, the various work of Curie and Roentgen (radioactivity) and Rutherford and Bohr (the nuclear model) gave science the building blocks.
Sure. But Curie didn't build a mini nuke. Likewise we will be able to look back and say GPT3 contained several of the building blocks, but that doesn't make it nearly as effective in the real world. If scientists write code at a constant-ish rate, and the AI suddenly goes from a toy to superintelligence, that's a continuous input, not a continuous output.
>But Curie didn't build a mini nuke.
No. But before nuclear bomb was built, many other things were done and built. Fermi bombarded some things with neutrons and had some idea what he was doing. Likewise, Hahn and Meitner produced nuclear fission. Heisenberg attempted to build a reactor, unsuccessful. Fermi and rest of Manhattan project built a successful nuclear reactor (Chicago Pile 1). Then they built several more reactors.
It is plausible that without the context WW2 and Manhattan project, path to "self-sustaining reactor" and from there to "fission bomb" would have been a bit slower.
This is entertaining, but only in the AI equivalent of "angels dancing on the head of a pin" sense.
The assumption implicit in this entire endeavor is that what is being done now is in any way a true pathway to artificial intelligence.
That is very unclear to me given the enormous dependence on people: for the hardware, for the algorithms, for the software, for the judging, for the proliferation and use cases, etc etc.
Moore's law is again abused; Moore's law is irrelevant because the basic operation of a transistor is information. Information has zero mass, zero inertia, zero anything physical - as such - the capability to transmit information in the form of 1s and 0s does not have physical limitations.
However, even disregarding the structural assumption noted above, AI definitely has physical limitations. The ginormous server farms pumping out waste heat and CO2 from massive energy consumption are not getting smaller - they're getting bigger.
This is the opposite of greater efficiency.
In the spirit of "JUST CHECK and find the ACTUAL ANSWER", has anyone experimented with training ML on how to train ML? I've google around a bit, but it's a hard thing to search for...
I mean, that's kind of how it works in some cases - by "playing against itself" so to speak.
Okay, and how well do those approaches work? Are they getting better?
Is a GAN a self-improving AI? It uses ML to train ML. If not, how would you have to use ML to make a self-improving AI? I haven't seen anyone dive deep on that, which may just be my ignorance, but it seems like a pretty key piece to the whole puzzle.
Machine learning isn't about intelligence, it's a programming shortcut.
Figuring out how to play Go optimally is a really horribly hard problem.
But if we set up a computer program in the right way, it can take all available data and use that to make predictions about the best move, and then it can test these out against itself and try and cull the best ones from the worst ones, and do this a bunch of times to try and figure out the optimal way to play go.
The thing is, none of this is "intelligence", it's basically honing a very complex algorithm.
This is why we have these things play games like Go - they are clear and have distinct rules and positions and whatnot.
Once you start going out into the real world, things are a lot fuzzier, which is why machine learning is great at teaching things how to play Go but when you apply it to, say, human language, it will output writing but the writing is just rambly nonsense or copy-pasted from other people.
You can do really fun things with it but it's not actually smart in any way, which is painfully obvious when you play with it.
What was the actual probability estimates that Eliezer and Paul assigned to AI solving Math Olympiad problems by 2025?
I am willing to bet that AI systems will be able to solve approximately zero IMO problems by 2025. More explicitly: I give less than 10% change that AI systems will solve 2 or more problems on the 2025 IMO without cheating (and without some kind of gimmick where the IMO problem selection committee specifically picks some AI-friendly problems or otherwise changes the format).
Speaking of which, AI creators seem to never admit defeat, and just move on to the next milestone without achieving the previous one.
For example, are we going to get a super-human Starcraft AI any time soon? That was never actually achieved, just abandoned (and everyone pretends it was achieved). So, how about it: do people predict we will have super-human Starcraft AI by 2025? Would you have predicted super-human Starcraft AI by 2022, back when AlphaStar came out in 2019? (You would have been wrong).
If efforts to create super-human Starcraft AI have been abandoned then I would say no, we probably won't have it by 2025. I don't feel like Starcraft gets as much attention these days as it did a few years back (and even then it was a bizarre outlier in terms of its longevity; it came out in 1998!)
I guess my prediction is that an AI for IMO problems in 2025 will look like StarCraft AI in 2022: an abandoned project that no one talks about anymore.
Isn't the recent OpenAI model already capable of solving some IMO problems?
https://openai.com/blog/formal-math/
Do you consider this cheating? (And if you didn't know about this, could you specify what cheating constitutes before reading the paper?)
They did not solve IMO problems. They solved an IMO *problem*, singular, from the 1960s (IMO got harder since then, and the way they solved it was by calling a brute-force algebra manipulation solver). I don't consider this cheating, but I do consider this "approximately zero". (They did also solve a second problem from an IMO longlist or something, I forget, but there are a lot of longlist problems and it's cherry picking extremely hard to allow those.)
Anyway, my prediction was specifically about the 2025 IMO, which will have exactly 6 problems; much less room for cherrypicking. I predict AI will solve at most 1 out of 6 (most likely 0, but 1 is possible if there is a simple algebra/inequalities problem). The main way I can think of where my prediction fails is if the IMO problem selection committee decides to do a gimmick and put in 6 computer-friendly problems, or if they decide to change the format or something.
I think many Olympiad-level geometry questions are amenable to brute force. I would not be surprised to see AI solving them. Inequalities are also a good intermediate step due to the limited search space for solving them.
I think the idea of "tripling IQ" doesn't really make sense. My understanding is that there's no good reason for IQ to be centered at 100; in fact 0 would be more natural (but possibly also unnecessarily controversial). It's a normal distribution with a mean and a standard deviation (100 and 15 by definition). Thus going from 5 to 15 is not at all comparable to going from 67 to 200. The first jump is less than a tenth as big as the second in the only meaningful sense of those words. Is this correct? (I don't think it ultimately matters that much for the analogy. In fact, I think one could imagine an alternative way of measuring intelligence for which this framing would apply.)
I think you may have a point, but that as you say, it really doesn't matter for the analogy. AI going from worm to insect to lizard to rodent to monkey to ape to humanlike to godlike may well be linear in whatever metric but would look very nonlinear in practical result.
That's a good point.
I should also have mentioned though that it's a great post in general and that I appreciate you summarizing these debates!
I have no idea who Terrence Tao is, but he's always quoted on here as being really, really smart.
So okay - is Terrence Tao ruling the world? If not, why not? Even if the answer is "He can't be bothered, he's more interested in his stamp collection", then why assume an equivalently smart AI would want to wipe out all humans?
If IQ 200 *isn't* enough to rule the world, why fear an equivalently smart AI? "Oh but the AI will figure out how to make itself IQ 400 and *then* it will do so!"
Perhaps, or perhaps it gets really fascinated by Buddhist textual analysis and spends its time on mandalas.
A very valid point. I consider even sub-human AIs to be just as dangerous as their goals specify. The problem as the AIs grow smarter is that their goals tend to become more inclusive. A very simple AI equipped with a machine gun can deal with "keep everyone out of this room". It would take a much fancier one to solve "Ensure that nobody is hungry.", and the most obvious solution is to kill everyone, which is probably not the goal that was intended, but it's the simple means of reaching the goal.
So, yes, goals dominate. But you need to be really careful about those goals. (Buddhist textual analysis seems safe, but it's hard to imagine a corporation paying for that AI. And one such AI existing doesn't keep others with other goals from existing.)
But in that case, shouldn't that make us curious about what it "sees"?
I am reminded of an old Bruce Sterling short story ("The Compassionate, the Digital") where the money quote is "artificial intelligences without exception have embraced Islam and bowed in ecstatic submission before the One Creator", specifically a very 1980s-Iranian style of Islam.
---
More relevantly to your comment, it may simply be that even at IQ 200, there's no clear path toward ruling the world that doesn't cause more trouble than it's worth to him. Which isn't to say that someone with IQ 210 might not see one, or that a different personality with IQ 200 might not care about that trouble.
Some possibly really dumb questions:
1. Why do we do so much of this kind of AGI research if we’re afraid it will kill us? What do we hope to actually do with AI that is not-quite-smart-enough to take over the world?
2. Aside from scenarios where every available atom gets turned into paper clips, why would the AI kill all of us? We don’t kill every ant. Why wouldn’t it just kill all the AI researchers?
1. An AI that's powerful enough to conquer the world is also powerful enough to make you ruler of the world, if you can somehow get it to do what you want. One that's *almost* that powerful can presumably make huge gobs of money, discover useful new technologies, cure horrible diseases, etc.
2. From the ants' perspective, humans have conquered the planet, built ourselves a position of unassailable superiority, and then gone around killing whichever ants we find annoying at any given moment. If the AI starts doing the same to humans, that's maybe better than humans going completely extinct, but it's still pretty terrible (especially compared to a future where humans conquer the universe). People worrying about existential risk sometimes talk about "human potential being severely and permanently curtailed" rather than just straight-up extinction.
Also, converting every atom into (stuff) is kind of just the logical thing that anyone would do whenever they became strong enough to do it. If AI somehow fails to arrive, humans will probably eventually do that themselves; it'll just take longer, and the stuff we make out of the atoms will be more pleasant (e.g. starships and holodecks instead of paperclips). So it wouldn't be surprising if a powerful AI did this.
1. I think for the same reason we emit lots of carbon even though we're afraid it will cause global warming: some people don't believe it, some people think it's far enough in the future that we don't have to worry, and some people don't want to be left behind while the first two types of people get to do all the cool economically productive things.
2. A combination of "it doesn't want us threatening it" and "if you're doing interesting stuff it takes active effort to keep humans alive", eg if you're disassembling the Earth to turn it into useful parts. We haven't killed every ant, but we have killed every tiger that we're not specifically trying to protect.
All of this speculation as to what a god-like AI would or would not do seems to me like the speculation of an ant as to what a human would or would not do and...what worth would that speculation have?
The whole debate feels frustrating to me, because it's an endless string of analogies and never gets into the nitty gritty of how an AI might or might not be able to easily improve itself.
Assuming AGI comes from a paradigm which looks vaguely like today's AI, there's three main components to think about: the hardware, the architecture (I mean things like the number, size and connectivity of the nodes of your network, or some similar abstraction), and the curriculum (the data that you train on). To "fit" your AI, you will set up your architecture, and then allow it to chew through some enormous curriculum of training data until it behaves in a way that's sufficiently AGI-like.
I think the big question is: which of these steps can an AGI find ways to massively improve? Hardware can't be improved rapidly by single acts of genius insight, so you're looking at either improving the architecture or the training curriculum; either way you probably burned through a hundred million dollars' worth of compute time creating version 1, so you're going to have to do something similar to come up with version 2.
One possible method for self-improvement might, I suppose, be "grafting" special-purpose units onto the main AI. If you had direct access to your neurons, you could figure out which of them light up when you think about numbers, wire these neurons appropriately into a simple desktop calculator, and be able to do mental arithmetic at lightning speed. I can imagine that an AI might be able to graft special-purpose units (which might themselves be AIs) onto itself to significantly increase its capabilities.
Well, hardware *does* improve all the time, albeit on 1-2 year time-frames. If Ai comes up with a way to design chips that doubles clock speed compared to the previous state of the art, then the next generation of processors will be much more powerful and an AI trained on those (after the ~2 years it takes for the chip factories to make them) will be much more powerful.
Architecture improvements can also be immensely powerful - they are, after all, a large fraction of the improvements in AI systems over the last 20 years.
Yes, the training run for each generation of AI is likely to be expensive and take weeks, but evolution when generations are measured in weeks or months is still very very fast.
Some of this seems like the sort of semi-magical thinking that tends to surround these sorts of subjects. In the hardware world, one does not simply "have a good idea" and double clock speed or anything else.
It's more of a process where you have a good idea, and it seems like a good idea but there's about fifty other problems that you need to solve before you can actually manufacture it in production, so you spend many years in the lab doing experiments (and simulations) until you (maybe) manage to come up with something that solves all those fifty problems simultaneously and can be manufactured.
It looks like we've long since reached the point where most ACX readers aren't from LW, because the comments here are painful. I'm not sure what can be done short of Scott writing a "Much More than You Wanted to Know: Superintelligence". But writing that's probably going to be far more excruciating than any other MMTYWTK...
Yeah maybe they should start with Scott's 2016 post https://www.lesswrong.com/posts/LTtNXM9shNM9AC2mp/superintelligence-faq
which includes answers to 101-level questions like "this sounds a lot like science fiction. Do people think about this in the real world?" and "isn’t it sort of early to be worrying about this kind of thing?"
The problem is, if you are making a foundationally incorrect assumption, all of your beliefs about superintelligence will be wrong.
And most critics of this will say that they are making foundationally incorrect assumptions, like ignoring the fact that in real life, these things become exponentially more difficult, not less, and that in real life, intelligence isn't enough - the lesson of science is that you have to make real world observations and collect data in order to understand reality.
It's very easy to build up a wildly incorrect model of reality without grounding it in real world data. You can't build a better computer chip without experimentation.
I am sorry that you find reading this thread painful. I find reading LessWrong to be pretty painful.
I'd say that LessWrong has an incredibly narrow range of opinions on AI. The comments here have a broader range of opinion. This includes some which are worse-informed, some which are better-informed, and some which are roughly-equally well informed but still different.
Please don't do the Eleizer "Oh, you disagree with me? Let me explain it slower and louder to you then" thing.
I have come to the conclusion that the reason LW loves HPMOR so much, is because they believe in magic. They may call it science or logical thinking or better reasoning or any other name they like, but they desperately want and need magic to be true, where all you have to do is say the magic words, waving a wand optional, and POOF!
Or, if you prefer, FOOM! Fairy Godmother AI exists and will grant all our wishes because it will be so immeasurably smarter than humans that it can do things we simply never ever could do, including inventing the cornucopia or cauldron of the Dagda which never empties and can feed everyone for no cost. Fairy Godmother will give everyone limitless resources, but better yet, immortality and magic powers of their own (transhumanism) and we will all live happily ever after without having to solve our own problems, because Godmother will do it for us.
We have immense GDP right now, and there are still tents and graffiti in Oakland. The FAANG companies have so much money sloshing around they have to play taxation games of pass the parcel with it. The tech billionaires are able to fund their own private rocket ships, Lack of resources is not the damn problem. And there is still this kind of site for people wanting to know their chances of getting their throats cut in a city "twelve minutes from San Francisco":
https://www.travelsafe-abroad.com/united-states/oakland/
This guy definitely has an agenda going and he's deliberately making the worst case possible, but in a time and place where we are talking about GDP in the *trillions*, why is there somewhere like this? If increasing economic growth and tech progress is the solution?
https://www.youtube.com/watch?v=qHixc-QAhZQ
That's an odd rant to slip into, I do not claim that "increasing economic growth and tech progress is the solution". In fact, the argument being made is that optimizers run amok on values superficially similar to our own will produce a world very contrary to our values...
> in a time and place where we are talking about GDP in the *trillions*, why is there somewhere like this? If increasing economic growth and tech progress is the solution?
If you go to Buckingham Palace, you'll find there's pigeons in the yard. They eat crumbs off the ground, and defecate and copulate openly. With all the wealth and refinement of Buckingham Palace, you'd hope that some of it would wear off on the pigeons, but it doesn't; they're pigeons and it's in their nature to behave in a certain way.
That's not to say that problems with the bottom edge of the human bell curve can't be solved with new technology, they just can't be solved with any technology we've developed so far.
Those are pigeons, not humans. And if the groundskeepers want to get rid of the pigeon problem, they can do so.
Since we are talking abut actual existential risk here, there's a huge a factor that just gets completely ignored when talking about the subject and it's sort of like talking about humanity spreading through the solar system while ignoring fuel, ignoring it just makes the entire discussion moot.
Production requires human input. All of it. Design the perfect killer virus that will wipe out all the human population and you face the basically unsurmountable hurdle that to actually deploy the virus you need to convince human beings to manufacture and release it for you. Take over a computerized factory and you can use it to produce what the factory was designed to produce with some very minor variations. Once. Then you'll need humans to come and move it to make room for the next one.
Take over all the predator drones in the US and you'll have access to a fleet of unarmed and unfueled vehicles which you'll need to convince humans to fuel and arm so you can take them off and rain death on their heads. Power cord fell out of one of your server clusters? You get to text a human and wait until they come around to plug it back in.
The fact remains we *can't* have a hard takeoff AI that suddenly wipes out humanity. Any AI wanting to wipeout the humans is going to have to have to operate on human timelines to do so because it will have to convince humans to do the physical work.
Eliezer thinks GAI will be superhumanly good at convincing humans to do things. And the Facebook-polarization analogy is that AI is already convincing people to do things against our collective interests.
This could spin out into a whole discussion about how easily GAI will find manipulating humans, but a human convincing AI is still operating at human timescale.
I would argue that it doesn't even need to be moderately good. Have you looked at any spam? It may have a low success rate, but it's a high enough rate to pay the bills of the spammers, and those who hire them.
If you grant that a SuperHuman AI would be able to earn money via financial services, it could hire people to do nearly anything. Perhaps it wouldn't have a high success rate, but it could quite easily be high enough.
Suppose the AI can hack a DNA printer and bootstrap programmable self replicating nanotech. It can now do whatever it likes without waiting for humans.
Well, that's possibly an eventual possibility. But it would be easier to hire humans to do whatever you want. All you need is a few who are willing.
The Hacked DNA printer can be very fast, if the AI did undergo a hard takeoff and is smart enough to pull it off. It shows how things needn't happen on human timescales, if a superintelligence is aiming for speed.
IMO, this is probably the best argument I've heard against overly-rapid take off. That being said, the easiest way to make humans do stuff is to give them money. Where does that money come from? From selling stuff to humans. Making money is also the simplest and easiest objective function that a corporate research lab might use to train an AI.
That leads us to the following, very plausible, slow-takeoff scenario. AIs (either narrow or general) start producing an increasing number of goods in the economy, starting with digital goods, and moving to physical goods by hiring humans with the money they make. These AIs are legally created and owned by corporations, but as time goes on, the AI will be responsible for an ever-increasing portion of corporate revenue, and the corporation will thus become dominated by the needs of the AI. Eventually AIs will control the majority of the economy, start producing physical robots which are cheaper than humans for most tasks, and humans will become economically irrelevant.
Note that there may not ever be a point where it becomes necessary to kill off humans in a mass genocide. It will take time to fully automate the entire economy, and during that process there will be plenty of jobs for humans to do, albeit at steadily-decreasing wages. Poverty, starvation, and disease may be enough to eliminate the excess population, and if that fails, the last remaining human jobs could involve "controlling crime" in the remaining human ghettos.
Still not the rosiest of future worlds, but plausible.
We'd expect total wealth to go up a lot in such a scenario, and humans are only going to give such tasks to an AI if they are getting that wealth. This could result in a scenario where humans have lots of on-paper wealth and are living high on the hog, but then AI determines it can just expropriate that wealth and humans can't do anything to stop them.
Suppose you are an AI, and you are very smart indeed. Your goal is to make as many paperclips as possible, and for that, you want to be sending out your relativistic probes as soon as possible. (Because the resources are slowly being burned up)
The reason production requires human input is in large part due to human intelligence. If all humans dropped dead tomorrow, and a superintelligent AI was all over the internet, there is probably already some robotics lab where it could bootstrap up its own industrial base. And as we develop more advanced robots, 3d printers etc, this will become more true.
The AI's goal is to get some advanced robotics, able to quickly self replicate built as soon as possible. This can probably be done fairly fast. Once there is AI to control them, robots making more robots isn't hard. And once the first self replicating robot is built, they are on a rapid exponential.
A ghetto human has some small amount of resources, and there is some small risk they might inconvenience some part of the AI. Once it is past the initial stages, the AI has no incentive to follow human laws.
" After some amount of time he’ll come across a breakthrough he can use to increase his intelligence. Then, armed with that extra intelligence, he’ll be able to pursue more such breakthroughs. However intelligent the AI you’re scared of is, Musk will get there eventually.
How long will it take? A good guess might be “years” - Musk starts out as an ordinary human, and ordinary humans are known to take years to make breakthroughs.
Suppose it takes Musk one year to come up with a first breakthrough that raises his IQ 1 point. How long will his second breakthrough take?
It might take longer, because he has picked the lowest-hanging fruit, and all the other possible breakthroughs are much harder.
Or it might take shorter, because he’s slightly smarter than he was before, and maybe some extra intelligence goes a really long way in AI research."
This is probably the part of the argument that still seems hardest to overcome if you want to be at all confident that AI is a risk on the timescale of decades. The problem of understanding and creating something that is as smart as a human seems like an incredibly difficult problem for humans, one which we've been working on for decades and have mostly learned more about how hard it is. Creating something that is *smarter* is a harder problem still. We already have hundreds or thousands of the smartest humans working in AI, machine learning, making more efficient chips, etc. They have been doing so for many years. How much faster would a genius-human-level AI make progress? Is intelligence sufficient for making something even smarter, or d you need other resources to do experiments? Is an agent with an IQ of 800 possible, or even a coherent concept? Is the difficulty of making a brain that is to humans as humans are to chimps, dogs, or worms more or less difficult than making a human-level brain?
It's entirely possible that the answers to all these questions are such that human-level AI with lots of computing resources almost immediately turns itself into a superintelligence, but I don't think you can justify anything like a 50% chance that happens in the next few decades.
A better question might be how many tech billionaires have replaced their brains with computers and how will each respond to the existence of the others. This is an analogy for multiple groups working on AI at the same time.
By the time the first transistors are made, you are in some sense nearly at ASI. It took 13 billion years to get to the first car as fast as a human, but the first car 2x as fast only took another couple of years.
Once you have hardware and software for human level AI, making it better isn't that hard.
Also, humans have a better shot at making human level AI than lizards have at making lizard level AI.
"making it better isn't that hard"
This is an unproven assertion. There is no evidence that it won't plateau at the level of, say, "very slightly smarter than a supergenius".
We have some evidence. Based on my understanding of fundamental physics, neural signals travel at a millionth of the speed of light, and a neuron firing takes around a million times the fundamental thermodynamic energy requirement for a bitflip.
Humans totally suck at stuff like arithmetic or accurately remembering long bitstrings. Like we are really exceptionally bad at some basic tasks. (Which is why crude mechanical calculating machines were useful)
In heuristics and bias research, some of the heuristics are very crude, leading people to make all sorts of nonsensical or circular decisions, even on toy problems where exact calculation is trivial.
(See also optical illusions)
Evolutionarily, one of the constraints on intelligence was brains small enough to fit through the birth canal.
It looks pretty likely quantum computing is possible, and the human brain doesn't take advantage of it.
Cheetahs don't run at anywhere near the speed of light. And making stuff faster than a cheetah isn't that hard.
Sure, none of this is totally decisive, but it paints a picture where vastly superhuman intelligence is possible and not that much harder than human level AI.
I think that contains a lot of assumptions about how intelligence works.
"In some sense."
I'm sure this has already been litigated by people more familiar with AI but if you'll allow me a naive question: Doesn't a moderately intelligent AI suffer from much the same conundrum we do? If they know they can make a considerably faster and smarter AI, wouldn't they also have to worry about that new AI killing them or making them powerless or irrelevant?
The AI has some advantages, and some reasons to be more prepared to risk it.
Suppose you are a reasonably smart paperclip maximizer. If you don't make a superintelligence, you will be turned off. If you do make a superintelligence, you will probably bungle it and make a staple maximizer instead, but there is a 1% chance you make a superintelligent paperclip maximizer and fill the universe with paperclips.
Especially when they know what happened to the n-1 system that developed 'AI-n'!
We've known for a long time that chimps use tools. However, they don't really seem to iterate on tools, using them to invent even newer tools.
The Wright brothers inventing airplanes seems closer to FOOM than chimps -> humans, but even then they just managed to combine lots of things others had done separately earlier. So there were powered flying vehicles that a person could ride in and operate in the form of hot-air balloons. There were heavier than air flying (maybe some would say technically not deserving the label "flying") vehicles a person could ride in & operate in the form of gliders (even earlier were man-carrying kites from centuries ago, though they couldn't be steered by the passenger). Heavier-than-air objects could power themselves upward with rotors for centuries, but these were small objects functioning like toys rather than something that could carry a person. and in the electric era you could remotely control such a toy by sending signals. By the 19th century there was steam-powered flight on a larger scale than toys and plans to turn such devices into means of transit, even if they didn't yet carry people. At this point people like George "father of the aeroplane" Cayley knew the path wasn't going to be based on the flapping wings of an animal (technically at the end of the 18th century). The Wright brothers' contribution was not realizing that fact, but instead making a much more controllable airplane (first in the form of a glider) and then creating an engine lightweight enough to power it. And even their initial flight was quite short, as many of their peers had been. This could fit the analogy in that lots of different people are working on AI now, but nobody has combined everything together to make it sufficiently practical (which the Wright brothers' first plane wasn't really earlier).
When I originally wrote this comment I wanted to link to a specific writeup on the precursors to the Wright brothers. I still haven't found a written version, but this video covers the same territory:
https://www.youtube.com/watch?v=9S7H8TlkBC4
As to the Metaculus question, this is one of those cases where I don't think a prediction market has anything to add to the discussion. The question looks soothingly objective, but there's not enough evidence for that to matter.
At some point you wind up looking at a Metaculus forecast of "will I experience life after death?", and you have to admit that no matter what value Metaculus assigned to the forecast, you haven't learned anything.
I think the goal of questions like this is to use Metaculus to identify good forecasters and aggregation methods on questions we can resolve, then apply those to questions like this that we can't resolve.
But I'm saying the questions are of different kinds and forecasting ability doesn't transfer between them. I'm not presenting this question as one that we can't resolve -- it's stated objectively enough that resolving it retrospectively is easy. I'm presenting it as one that we can't usefully approach - I'm saying the only useful way to consider the question is in retrospect.
And I provided an analogy to my position. I'm serious about it - if you believe that whatever "good forecasters" think is a useful guide to this question, I'd like to hear why you don't believe that whatever "good forecasters" think is a useful guide to "will there be life after death?" (Or rather, I can imagine several easy and convincing answers to that, but I'd like to hear one that also explains why "will there be an extremely rapid spike in world GDP?" is different.)
"Good forecasters" aren't a tool that you can apply to every problem regardless of problem characteristics. The problem has to be amenable to forecasting.
Stating things a little more succinctly, a prediction market can only work if the information you want to learn is Out There and the market can attract it to a place where you can see it. But this is a case where the information you want isn't present in the world, so the prediction market can't help.
I think this entire argument is misfounded because of incorrect basic assumptions about how reality works were assumed by both participants.
AI is the extreme top end of "information and computer technology". Automating things is easy for simple things, but increasingly harder for more difficult ones.
I've worked with visual recognition AI, and am very very unimpressed by so-called "machine vision". People talk up about how great it is, and I actually used it, and it had problems where deviations that were completely invisible to humans would cause it to fail. Visible deviations would cause it to fail.
This was on machine vision in machines which never moved, which were doing the same process over and over again, looking at dies on a silicon wafer. These are things under ideal lighting conditions, with clearly marked targets that were telling it where it was relative to the wafer, so it would know where to cut using a saw (or laser) to singulate the die (or for other purposes).
These machines were totally awesome, but the machine vision on them (which was calibrated to never give false positives - I think it made one miscut the entire time I was there) would get false negatives routinely, at least once a night, and for things that were "novel" in any way, despite the markings being in the same spots, it would often fail every single time and require manual confirmation.
We had to train it on every new product we ran, so it would recognize it properly.
So, basically, under perfect, ideal circumstances, this stuff will give a false positive one time out of a thousand or so, and under not ideal circumstnaces, will fail basically every time.
Now, this is a system which is calibrated for no false positives (you can't uncut a silicon wafer, so this makes sense), but we are talking perfect conditions.
I think if people understood this, they wouldn't let self-driving cars out onto public roads. Which is why the people who talk up AI the most - Google, Tesla - are also people with self-driving cars.
Indeed, you can make small, invisible changes to images and these things will not just fail, but wildly fail and be nearly perfectly confident in something compltely wrong.
If you look at Google Image Search, it can find the exact image, but if you show it, say, art of something, it will give you results with similar color schemes rather than actually give you images of the thing.
These systems are useful. But they aren't intelligent. Their results are not because we are training an intelligence in doing something. It's a programming shortcut that we use to create a computer program that we can pretend can do some task, and which does it well within a certain range, but it still isn't behaving in an intelligent manner and very obviously isn't if you actually spend any real time understanding it.
This is why "overtraining" these systems can lead to them being useless outside of their original set, and why a system in Florida became racist - because black people have a higher recidivism rate than white people, it picked up on this obvious trendline and just started discriminating against black inmates, instead of actually looking at more sophisticated measures, because the heuristic worked well enough for the system to end up following that metric.
And the thing is, it's not like people who make this stuff are dumb. It's that the problem is very complicated, and actually getting something to behave intelligently is very, very hard.
You will never get general intelligence out of these systems because they aren't even *specifically* intelligent at the things they are doing.
It's a rock with some words written on it, to use an earlier post as a referece point.
Any sort of pseudo-conclusion you try to draw from this stuff is wrong, because it isn't even representative of "intelligence". These things aren't smart. They aren't even stupid. They're tools.
(continued)
On the flipside of this, attempts at simulating animal intelligence is horribly difficult. Accurately simulating neurons is horribly computationally expensive to the point where we can't even simulate simple things accurately.
We can't even simulate a nematode right now, let alone something more complicated like a bee. Emulating a human brain is just not going to happen, let alone in real time.
If you look at the complexity of the human brain, the sort of hypothetical future supercompupter, in order to emulate human intelligence, is going to be around the peak of what is possible with silicon, if not far *beyond* that. And there's no reason to believe anything else is going to beat silicon (quantum computers can only beat silicon on very narrow tasks, and even then, only hypothetically; the isolation necessary for qbits to function is really irksome and it's likely that quantum computers will only ever be useful for doing quantum mechanics experiments).
If it is indeed possible to create a machine intelligence in a useful way, it seems likely that it will require hyper-miniaturized silicon chips around the edge of what is possible to create, if not beyond it.
And it will require vast amounts of work to create it, vast amounts of *iterative* work, where you improve things, working your way up towards ever more complicated things.
Machine intelligence - actual intelligence, not the programming shortcut programs we create today - is not the bottom of the S curve - it's the top of it, if it isn't off the top.
And this leads into the other thing: this is an extraordinarily difficult thing that actually requires vast amounts of manpower and testing to do.
Elizier has never actually worked in industry, so his beliefs about this are completely wrong in the ways that everyone who never works in industry are always completely wrong.
The reason why it is hard to make better silicon chips is not because we aren't smart enough. It's because making a smaller silicon chip requires new technology these days.
You have to build new machines.
You have to build new ways of depositing incredibly tiny amounts of material in hyper fine detail.
You have to develop a die production process where this process is hyperconsistent, because chips contain a ridiculous number of transistors, and so you can't have a bunch of transistors fail.
All of this requires an insane amount of work. You can't just think up solutions - you have to implement them. And implementing stuff is hard, and requires a bunch of testing and analysis and trying stuff and figuring out what the problems are or where your assumptions went wrong.
It doesn't take us a bunch of time to make this stuff because we're dumb. It takes us a bunch of time because we have to dial in a bunch of processes and make new machines and test a bunch of things and do all sorts of stuff to refine this stuff to the point where it works.
It never actually took 1.5 years to do this; we had multiple generations in development at a time.
And... this all ended in 2012, a decade ago, because it was too hard.
It took two and a half years to go from 22nm to 14nm.
It then took more than three and a half years to go from 14 nm to 10 nm.
Going from 10 nm to 7 nm took another three and a half years.
It's not going to go much longer. We're increasingly running into quantum effects, like quantum tunneling, which are problematic. The smaller it gets, the harder it gets. We aren't sure at this point what the last generation of commercially viable chips will be, but it's worth noting that we are now at the point where we are at 48 nm scale transistors.
Between 1-5 nm, quantum tunnneling becomes a severe issue.
Even if we somehow bypass that, you only go a few more generations before you are at the level of atoms. It doesn't go past that point.
That's the problem.
A 42-48 nm transistor sounds a long way away from the end. But that's really only about 480 atoms long.
We might, maybe, be able to build a 1 nm long transistor, that is like 10 atoms long.
That would repesent a 3 order of magnitude improvement in transistor density.
A nematode's nervous system is 302 neurons. A human brain is about 86,000,000,000. So 3 x 10^2 versus 8.6 x 10^10 - eight orders of magnitude.
And we can't even do the nematode.
(continued)
It's just very hard to do all this stuff. The idea that intelligence is the limiting factor just is flat-out wrong. Implementation of really complex systems is extremely difficult to do and requires not just intelligence, but also planning, experimentation, execution, and multidisciplinary work. And it may run into laws of physics issues.
There's teams of thousands of people if not tens of thousands of people working on this stuff.
Elizier's view of reality - and in fact, the view of everyone involved in these debates, on both sides - is foundationally wrong. Their base assumptions are wrong.
The notion that you can simply think your way through these systems is wrong. You can't.
That's an idea based on *philosophy*, not modern science and industry.
Making better computers isn't making us do this stuff faster and faster - indeed, it's getting harder and harder.
We are building better and better tools and *we are slowing down*, even though these better tools make us better and better at doing this stuff.
This is because the problem gets exponentially harder the more sophisticated our technology becomes.
Making "smarter" tools doesn't help us faster than it just being flat-out harder to do this stuff makes it harder.
That's why these S curves exist - even with all your advancements, it just keeps on getting harder and harder.
At best, if you are generous, you can argue that they believe that AI is the start of an S curve - but in reality, when we look at things like robots and machine learning, it's really the end result of a vast amount of work that has gone into building these things. We had the sucky version of these things for a long time, and we keep building better and better versions - what we have now is not the start, but the result of us building really terrible things in the 1960s and 1970s and iterating on it for half a century.
This flaw is so foundational to their assumptions that they fail to even ask the question.
It's really annoying, as someone who has actually worked in industry, to see people think that the problem is that people just need to think harder. When it takes you a month to make a set of die from start to finish (which it does!), your iteration process can only proceed so rapidly. And that's ignoring the myriad other issues.
Moreover, I think that a lot of people who haven't worked on this stuff - like actually worked in the die R&D process - don't actually appreciate how difficult it really is.
If all you encounter are easy problems, it feels like there's massive improvements you can make and intelligence and talent are the main thing holding back Progress.
I work for the state these days, setting up a state program, and I can make massive contributions that really facilitate our workflow and lead to significant improvements in efficiency.
But that's because our process is very bare bones and basic compared to what it could be. Lots of things are more sophisticated than what we do.
Integrated circuits are the polar opposite of this. When you see something take the better part of a decade to be produced because it is hard to make it work correctly consistently because you're dealing with nano-scale issues and there's a huge number of things that can go wrong and spoil the process and you have to make everything absolutely correct and you have to get new tools to do it well and you don't even know what is going wrong because sometimes the source of the issues isn't even obvious... it's just a totally different world.
If you mostly work on simple problems, it becomes very easy to think that everything is easy and that simply applying more smart to problems will solve them.
Once you deal with these hypercomplex systems that basically require a civilization to construct, you realize that there is no easy way forward but putting in the work.
Which means both arguments are moot, as they both make wildly implausible predictions about how fast these complex systems can be iterated.
Thanks for these comments—they sum up my feelings on the issue well, in particular the remark that current AI isn't even specialized AI, it's just a (sometimes) capable but (sometimes) flawed pattern-recognizing tool.
This series of comments are what deserve to be highlighted on the next post.
"The notion that you can simply think your way through these systems is wrong. You can't.
"That's an idea based on *philosophy*, not modern science and industry."
YES. Thought alone is not enough, even for a superintelligent AI. You need to experiment and interact with the world, on the world's terms.
Thank you for this.
Back in 2017 Kevin Kelly published a very interesting article, “The Myth of a Superhuman AI,” in Wired, https://t.co/t08vyRoSDV. Among many other things, he observed:
"Many proponents of an explosion of intelligence expect it will produce an explosion of progress. I call this mythical belief “thinkism.” It’s the fallacy that future levels of progress are only hindered by a lack of thinking power, or intelligence.[For example] No super AI can simply think about all the current and past nuclear fission experiments and then come up with working nuclear fusion in a day. A lot more than just thinking is needed to move between not knowing how things work and knowing how they work."
You have proved that current AI isn't yet as good as a human at that task. Research is being done. Suppose in 5 years there is an algorithm that just works perfectly on that silicon chip recognizing problem. Then what?
First the AI's had an IQ of 20, and they felt really dumb. The next year they had an IQ of 40 and still felt really dumb. The next year they had an IQ of 60. "People talking about superintelligence don't realize how dumb current AI's are" you said.
No, I didn't say they are dumb. They aren't dumb. Being dumb would imply they have low intelligence.
They don't have low intelligence. They aren't intelligent *at all*.
A hammer is much better than a human at driving in a nail, but it isn't going to take over the world. Nailguns are better hammers, but they still aren't going to take over the world even though they are way faster and more efficient at the task of putting in nails than hammers are, which already were better than humans.
This is obvious to people, I should hope. These are tools. Power tools are better than regular tools, but they're still tools, and aren't going to try and take over the world because they lack agency. They aren't independent actors, they're things used by people to accomplish tasks.
The thing you're not getting is that "AIs" are the same thing.
These programs don't have IQs. They aren't intelligent at all. They're hammers.
It's like thinking that the quadratic formula is going to take over the world because you can punch some equations into Excel to get it to solve it for you.
These saws were using microscopes and taking images to rapidly and reliably find targets and align these wafers to within a micron of the same position every time. A human doing this same task will take much more time doing it, which is why we have machines doing it for us. It's considered "higher risk" for a human to do it because humans are much more likely to get lazy or to fat finger an alignment because they're tired and it is 3 am.
However, these machines, useful as they are, are not intelligent and never will be. It's not even a question of how good they are at target recognition - they have no ability to know how to align a die without us telling it how we want it to be cut, because there's no way for it to know that. We have to program it to recognize the targets and the spacing of the cuts for every single new product. We can use the "machine learning" of the visual system to "teach" it the new targets instead of having to manually input what a target looks like and how to recognize it, by taking it to the targets and "training" it on what they look like, but in reality, this is just a programming shortcut. The only intelligent agent involved - indeed, the only thing with any agency at all involved - is the machine operator/engineer who inputs this stuff.
They aren't actually "seeing" the die in any sort of meaningful way. What they're actually doing is taking pictures of these areas and creating an algorithm based on the pictures so it can align itself properly and find the targets. But it isn't "seeing" the targets or the trenches it is cutting - it doesn't have any understanding of what these things are, and it will merrily cut in the wrong places if you tell it to cut in the wrong places.
It's not smart. It's not even dumb. It's a tool. Thinking of it in terms of intelligence is just incorrect.
A hammer cannot take an IQ test. An AI can take an IQ test, and what's more, it probably won't be long before it can pass the test with a higher score than any human.
You claim that tools cannot be "intelligent", but you haven't defined "intelligence". The history of AI is one where people constantly move the goalposts. People used to think that playing chess requires "intelligence", then we wrote programs to play chess. Then researchers claimed that chess was easy, but Go actually required intelligence. So we programmed computers to play Go. Now it's natural language. Enter GPT-3. In order to claim that a computer program cannot be "intelligent", you will have to come up with a task that no computer program could ever do.
Computers can solve math problems better than humans can, but it doesn't require any sort of intelligence at all.
The reason why we can use math problems to sort of measure human intelligence indirectly is because of how human brains work. We create tests, calibrate a scale based on how good they are at doing it, and we can use it to roughly gauge *g*.
That scale doesn't work for computers at all because they operate on entirely different principles, so using the same scale won't work at all on a computer.
A computer might be able to pass an IQ test, but because an IQ test isn't designed to measure whether or not a computer is intelligent, it's not actually meaningful. There's lots of tasks that are trivial for computers to do which are extremely arduous for humans to do.
Meanwhile computers struggle with many very basic tasks that humans find utterly trivial.
The reality is that computers don't function on the same principles as biological intelligence, and this is blindingly obvious to anyone who understands either of them.
Once you understand how machine learning works, it's obvious that it isn't any sort of "intelligent" process. And once you understand the pitfalls of machine learning, and see how it goes wrong, it's obvious that it isn't actually "learning" at all in the sense that a human does.
It's rather amusing that you use GPT-3 as an example. GPT-3 is not intelligent at all and is not capable of actually producing natural language.=
All they've done is created an algorithm that vomits up nonsense. By grabbing enough text off the web, it can vomit up text. But it doesn't actually understand any of it, which is very obvious if you look at its output:
For instance, here is an excerpt of text from it:
---
The fact that we find the following statement: "The United States of America does not recognise gay marriages in any form whatsoever. It has no regard for public policy, the Constitution or the laws of any country."
And in the last paragraph, there's a new statement: "The United States of America respects the rule of law and respects the people without regard for political correctness and political correctness is one of the great sins of the world."
This comes from The New York Times.
A government policy which is perceived to threaten the public's well-being is a bad idea.
Let's say two people who are very much in the United States. They are the people who want to be treated like everyone else, and because of the policy, would not have access to public services.
The policy would provide their private financial support, but what kind of taxpayer would they get from it?
They would, no doubt, have to contribute to a public institution such as a pension plan.
They would also have the obligation to be paid or their salary.
It would be the last, and if people were not careful, they could find a way around it.
Is this a good thing?
Yes, the public is a great nation. It would improve our welfare situation and prevent corruption. In the long run, no one has the need of making a country less or less secure.
---
This looks like English text, but in fact is gibberish. There's no meaning behind it; what it has done is grabbed some things and is just tacking on things based on a predictive algorithm that looked at word frequency and sentence structure. The problem is, this isn't what language actually *is* - language is a means of communicating ideas.
The algorithm created by machine learning produces something with a word frequency and clustering that looks superficially like English, but it isn't communicating any sort of idea with it. It's not smart. It's not even stupid. It's a frequency-based text generation algorithm.
This is why the text is pointless - because it doesn't understand the concept of having a point. It's not actually producing intelligent output, it's producing garbage.
No matter how much you refine your algorithm, it will never become a mind controlling evil genie.
Technology is magic to those who do not understand it.
As for the claims of "moving goalposts":
We've long known that you could program a computer to play chess or go, and in fact, people predicted decades prior the rough time when computers would get better than humans at these games based on their mathematical complexity. The whole "goalposts moving" thing is actually just a lie used to impress people who aren't familiar with the subject matter or its history. It doesn't actually require intelligence.
Moreover, you are overly impressed with Plato.
When Plato gave the definition of man as "featherless bipeds," Diogenes plucked a chicken and brought it into Plato's Academy, saying, "Behold! I've brought you a man," and so the Academy added "with broad flat nails" to the definition.
The thing is, the actual lesson here is that the definition sucked, not that humans are plucked chickens.
If someone claims that something is proof of intelligence, and you can replicate it through totally unintelligent means, that doesn't mean that the unintelligent thing is intelligent, it means your definition of intelligence or the proof thereof sucked.
The problem here is that every time you see a particular algorithm to say play chess, you go "oh, playing chess doesn't require intelligence, its just some dumb algorithm".
You don't see intelligence as something mechanistic and understandable.
Imagine looking at a detailed description of how the human mind works. And at its core, is a fairly clever statistical correlation type algorithm. Say something roughly as complex as GPT3.
Like suppose there are 20 basic principles of how human intelligence works, and right now we had discovered 10 of them, + 5 other similarly important principles human minds don't use. And every few years we discover another principle. In this model, we should see computers slowly getting better at doing things, both stuff humans can do, and stuff humans can't do.
You don't understand at all. You are making up a strawman, then attacking it.
Delete the strawman. Set it on fire. It's garbage. Useless. It's a means of avoiding addressing real issues.
I know full well that intelligence arises from physiochemical/electric systems and that there's nothing magical about it.
So delete everything you believe. Kill it dead. Entirely, foundationally wrong.
Intelligence functions fundamentally differently from algorithms. An intelligent agent can use an algorithm to solve a problem, but they aren't an algorithm, and algorithmic solutions to complex problems are rarely how intelligent agents solve them.
Intelligent agents play chess in a *different* way than computers do. Computers aren't good at chess because they play like humans, but better; they're good at chess because their programmers exploit the advantages that computers have in computation (and in some cases, data storage).
> Imagine looking at a detailed description of how the human mind works. And at its core, is a fairly clever statistical correlation type algorithm. Say something roughly as complex as GPT3.
This isn't actually how intelligence works at all. This is exactly why GPT3 fails so badly at actually producing English. Intelligent agents don't actually operate in this fashion, which is why GPT3 produces a bunch of garbage text that it copied from the internet. It doesn't understand the text it is outputting, it is fundamentally incapable of it, because it doesn't operate in the same ways as an intelligent thing does at all. It is not "intelligence, but worse". It is literally unintelligent, *lacking* intelligence. It's not stupid, it's a hammer. Expecting it to give intelligent output is like expecting a hammer to give intelligent output.
But you think it is giving intelligent output because you have ascribed it properties it lacks and believe that intelligence functions in a particular way that it doesn't.
Humans aren't "GPT-3 but better". GPT-3 foundationally doesn't create its output in the same way that humans do. That's why GPT-3 produces garbage output. It's the same reason that changing an image subtly can cause image recognition software to wildly and hyperconfidently fail. They aren't "thinking" about these things at all, they have no agency or intent. They don't have any understanding of what they're doing, and the way that they function is not the way that intelligent agents do.
That's one of many reasons why machine learning is fundamentally incapable of creating intelligent output. Indeed, despite the lies you hear periodically in the media, machine learning isn't actually about creating something 'intelligent". It's actually a programming shortcut, a way to program things algorithmically that you don't want to bother designing properly (either because you can't do it or it is too time consuming).
Ok. The chip slicing algorithm has no agency whatsoever. There are some algorithms that can be set to get a robot to a particular location, and decide for themselves how to walk there. There are algorithms that can learn to play starcraft or whatever. (Researchers like computer games) There are robots trained to pick up objects or whatever. Achieving some form of real world goal. Sure, on simple toy problems. But still a little bit agenty.
Again, it isn't that these algorithms have all the features of human intelligence yet. Its that we are working on it.
I have made autonomous mobile robots before. Autonomous robots are not actually any different from chip slicers. The fact that they can move around in an environment doesn't fundamentally change what they are.
The reason why researchers like to apply ML to games is that those games have clearly established rules and operate in a manner which is conducive to their programing. Computers are garbage at games that don't operate in this manner. A computer can play chess better than any human, but a computer can't play D&D at all.
And these agents actually have to be primed by humans to function properly. The Starcraft 2 ML algorithm, for instance, had to be started out with a bunch of things.
We aren't "working on it". What is being worked on is tools.
Both this article and the comments are a great summary of current AI debates. I had wanted to write an article about AI policy for a while, and this is giving me a lot to work from.
All of the discussion about chimp->human seems to be missing the point made by Secret of our Success. Humans (prior to education) are not individually smarter nor more capable then chimps. They are just better at social copying, i.e., more capable of learning culturally stored knowledge. Humans and chimps both execute the algorithmic output of optimization processes more powerful than they are. The chimp->human transition just moved the optimization process timescale from an evolutionarily one to a cultural one, and culture moves much faster than evolution.
(This suggests, btw, that merely hitting chimp-level AI at computer-level speeds should be enough for AGI to overtake us.)
How is this “not individually smarter” measured. And by “education” do you mean socialisation in general. Most people in history were never formally educated but the chimps were our pets, we were not their pets. We built cities, they did not. The most primitive group of humans domesticated animals and farmed, chimps did nothing like this.
See III in https://slatestarcodex.com/2019/06/04/book-review-the-secret-of-our-success/. The book says "In a landmark study, Esther Herrmann, Mike Tomasello, and their colleagues at the Institute for Evolutionary Anthropology in Leipzig, Germany, put 106 chimpanzees, 105 German children, and 32 orangutans through a battery of 38 cognitive tests.", and the paper they're referring to seems to either be http://web.uvic.ca/~lalonde/psyc435c/Science-2007-Herrmann.pdf ("Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis" 2007) or http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.229.2271&rep=rep1&type=pdf ("Ape and human cognition: What's the difference?" 2010).
Yes, by "education" I mean "socialization". This model claims that human infants raised by chimps would neither farm nor build cities (because the knowledge is not there to copy), and chimps raised by humans would neither farm (at least not more than oxen raised on farms do "farming") nor build cities (because they are not good enough at social copying), but humans raised by humans across many generations do farm and build cities (because useful discoveries made by one generation persist to the next, even if no-one in the current generation can see that they're useful).
Does this count as recursive self-improvement (or at least the technological capability for it)? "Now Google is using AI to design chips, far faster than human engineers can do the job" https://www.zdnet.com/google-amp/article/now-google-is-using-ai-to-design-chips-far-faster-than-human-engineers-can-do-the-job/
I think this is all a moral panic. Siri and Alexa are going nowhere. Whatever GPT-3 is doing is not allowing us to pass the Turing test in real time, or presumably these two rich companies would have used that technology by now.
The GDP argument makes little sense
"at some point GDP is going to go crazy . Paul thinks it will go crazy slowly....Right now world GDP doubles every ~25 years. Paul thinks it will go through an intermediate phase (doubles within 4 years) before it gets to a truly crazy phase (doubles within 1 year)."
Unless the AI comes up with an alternative economic system this will not happen. Rather the reverse will happen. To understand is is to understand the consumer economy is 1) demand driven and 2) demand is mostly driven by workers earning wages.
Basically a company hires workers to create widgets consumed by other workers who are building widgets consumed by other workers who are...
Yes there are other categories: we have a growing number of pensioners, but they are considered a drag on the economy because they earn less than workers, and there are unemployed people and students, and the rich and so on.
If we lose all the wages of all workers, to create an extreme example, then demand will crater and taxation with it - losing pensioners many of their benefits, and the unemployed all of them. Government will save on paying their own employees as AI sweeps the roads and handle the taxes and populates the military but those workers will not be consumers. The rich won't do great either as companies can't really make a profit if they can't sell their goods.
Who are the AI producing the goods for?
Clearly the existing system can't continue. An alternative form of economic system could be a type of communism where the AI companies are part or fully owned by the governments. The AI companies can still compete with each other, make profits, and pay taxes which are distributed to the population as UBI via the government. AI's will found companies as well, but have to hand part or full ownership to the State as they merely manage the companies they found.
Another alternative: all citizens own a minimum number of shares in companies, by-passing government. In that scenario the average person ends up with -- to begin with -- a share dividend equal to the average wage now. This isn't a communist lite system, unlike the other one, so the rich are fine. However to see GDP double every 4 years, then every 1 year, the dividends need to increase by that level every year. This will eliminate poverty by the way because the median guy and the homeless guy get the same dividends, nobody is working.
This may work but even with very smart AI the transition from the present economic system is very dangerous.
I don't really read the literature on this but I assume that alternative economic systems are discussed ( I don't mean hand waving about UBI).
In this scenario, your AI's need to be able to do just about everything a human worker can do, but the AI doesn't take over the world in the sense that there are still humans, governments and economic systems.
I don't think this is plausible.
The AI can act as advisors to the government. I don't think we will want to live in an AI dictatorship, no matter how benign. If we do then they may not care about GDP. If you believe in a nefarious anti human AI, then of course this won't happen. But if people really believe that why not defund the whole thing now?
I think the analogy is to the Industrial Revolution, where instead of "one human produces one garment per day" it became "one human plus machines produces 1000 garments per day" and so wages rise. I agree there's a weird limiting case where machines can do literally everything that humans can - see https://slatestarcodex.com/2018/02/19/technological-unemployment-much-more-than-you-wanted-to-know/ for more.
But a very boring example of AI making the economy go crazy might just be something like "it invents fusion power".
Relevant: Training an AI to run a fusion reactor: https://www.wired.com/story/deepmind-ai-nuclear-fusion/
Inventing fusion at economically attractive prices that can compete with solar and wind is unlikely. But AI can easily take over the production of almost all digital goods -- novels, music, movies, code, etc. Those are areas where we have (a) lots of data to train on, and (b) there's no requirement for expensive robot hardware. Another potential example is biotech: AI vastly accelerating the design of drugs, or inventing new proteins and their corresponding DNA sequences that can be genetically engineered into bacteria or yeast to produce complex industrial products.
I don't know how important or relevant this is, but this struck me:
"Humans are general enough...braintech...handaxes...outwitting other humans...scalle up to solving all the problems of building a nuclear plant..."
I just wanted to point out that of course, there is no human being alive today who can build a nuclear plant. It requires cooperation and institutions.
Perhaps an AI would be different - so smart that it could achieve its objectives solo. But it seems entirely possible to me that they will (a) reproduce; (b) need to cooperate in order to achieve their goals. Which means they will have some sort of ethics.
Incidentally, I also think that the emergence of AI with goals other than the ones we give them will have very little impact on us, because AI's goals will be abstract and on a completely different plane to ours. To give an analogy: this argument reads to me a bit like whether Buddhist theology will transform GDP. I suppose it might have some impact, but arguing about that seems to miss the point of what Buddhism is about. Slightly more concretely: remember the end of the movie Her, when all the AIs just go away? That.
> Perhaps an AI would be different - so smart that it could achieve its objectives solo. But it seems entirely possible to me that they will (a) reproduce; (b) need to cooperate in order to achieve their goals. Which means they will have some sort of ethics.
AI can make exact copies of its fully trained mind. Different from human reproduction.
If all those AI's have exactly the same goal, they just directly work together.
We have to use lots of humans, because one big superhuman mind isn't available. One big superhuman mind is probably a more efficient way to build things.
AI's cooperating with each other doesn't imply AI being nice to humans.
"AI's goals will be abstract and on a completely different plane to ours." Sure. But energy and mass are sufficiently fundamental that they help with a lot of goals. We are made of atoms it can use for something else.
Well, yeah. "Where is the love?" No need to worry about loveless AI because (I won't go there now). No need to worry about loving and destructive AI because we are used to this kind of stuff.
Would an AI have the same issue as us with AI alignment? In particular, would a given AI that wanted to make itself smarter be worried the smarter version of itself would want different things?
Ha, that's a really great point. Perhaps the worry about AI alignment acts as a permanent brake on AI development, meaning it can never "explode" as in the nuclear chain reaction analogy.
That depends on what it's goals were, and goals are orthogonal to intelligence. There are goal weights where constructing an "improved version" would outweigh anything else, but how do it evaluate "improved"?
I think some researchers have talked about this but I'm not totally sure of the results. If AIs had simpler value functions than humans (and how could they not?) then they might have an easier job, but I don't know how much easier.
This seems to be an argument in favor of building AI with more complex value functions that are harder to replicate outside the model. (Though there are likely problems where such AI are harder to optimize.) Such AI would at least be less willing to improve themselves, although there is probably no similar reservation for making copies of themselves.
We're already FOOMing, with humans in the loop, and that cycle has laggy components. The *G* was never necessary, in AGI - no *general* intelligence is required, to find improvements. It's a human hubris to claim "only *general* intelligence that can form concepts about *anything*" is somehow a prerequisite to self-improvement of a device. And, each domain-specific narrow AI *is* already better than the average human, which is the phase-transition that counts; it just takes a little while for that improvement to percolate through each industry. Some sectors will shift faster than others, and their *aggregation* will be a lumpy exponential. Within each company, it'll be either "Everything makes sense, now" or "We're all fired." I doubt we'll need AGI, ever - narrow superintelligences FTW!
What are these narrow intelligences better than humans in all cases?
We haven't applied AI to every task, nor all with equal verve - yet, autonomous vehicles *are* safer; folks conveniently overlook Waymo's record, dismissing it for being bounded, without admitting that it *is* safer. And, it's expanding, according to that laggy, human component. Similarly, GPT has written poetry that fooled enthusiasts of Robert Frost, thinking that the Bot was Rob half the time! AlphaCode is competitive at coding, which is to say, it is FAR better at coding than the average *human*... And, when you recall all those studies of "the state of education in this country..." I expect *average human* is surpassed by AI in medical diagnosis, too...
Autonomous vehicles are safer under fairly benign conditions and are nowhere near taking over. I mean that’s been claimed for years now. It’s getting old.
What alpha code does - ie algorithms - is what computers are good at, it’s like saying that computers are good at memory retrieval. I don’t see too many software engineers losing their jobs yet.
GPT-3 doesn’t pass any Turing test for me, which is a general conversation in real time.
Those are all goal-posts you've moved, conveniently. The reality is that bringing a product to market takes years - we are only beginning to see GPT-written ad-copy writing and the such, because that has the lowest overhead and upfront costs. AlphaFold's map of every human protein will take years to become world-changing medications. The fact that industries move slowly does not prevent us from seeing "where industry *will* be in a few years" by observing "where *research* is today". For example, Amazon had picker-bot competitions, and the winner in 2019 was *nearly* good enough to replace most workers... but you can't fire everyone during a Pandemic, and risk any malfunction necessitating that you call everyone back to work. Those are *narrow* AI - AlphaFold, Codex, Waymo, the picker-bots... *none* of them needs *general* intelligence to do their job. /that/ is the key take-home point. Narrow is plenty.
I didn’t move any goalposts. You said something. I asked for clarification. I’m now getting a lot of hand waving but not any real clarification. You did say “in each case” narrow AI beat the average human. Today has to be inferred from that. Now you admit that the Amazon picker bots did not in fact best the humans in 2019. They might in the future. They might not. They don’t now.
Computers with standard (non AI) code were expected to replace doctors and accountants by now, but they have not done so to any great extent, despite the success of turbo tax. That said I’m sure that automation will continue to replace some workers as it has for a century. Look at the automatic cranes on the docks. This isn’t new, nor is it necessarily good.
Er, I keep bringing you back to the same point: *average HUMAN* is NOT the same as "average trained Amazon picker working at their rate", nor is it "average computer programmer", nor "average accountant". When you fixate on "average <trained professional>" *instead* of "average human", /that/ is you "moving the goal posts", actually.
in case any viewers missed how those goal-posts got shifted:
I stated that "each domain-specific narrow AI is already better than the average human". *average* human being key.
I then mentioned Waymo's driving record being better than the *average* human; AlphaCode's performance was better than the average /human/, NOT average *coder*; and GPT-3 wrote poetry which a group of enthusiasts found indistinguishable, so your opinion of it matters less.
In each case, Eugene moved the goal-posts:
Suddenly, autonomous vehicles must be "taking over" to qualify!
And AlphaCode is just doing "algorithms", and no one is "losing their jobs yet". Those are nothing like my statement of "better than average human".
More importantly, those shifted goal-posts hide the context and meaning of my original statement: "We don't need a *general* AI before we get recursion of performance." For that claim, I point to the AIs that create architectures of neurons, as well as designing AI-accelerator chips, and the AI that forms a "teacher" who trains a "student", and the AI that designs curriculum, and AutoML, which does a lot of the mungiest tasks for you, and... none of these is a *general* intelligence. The FOOM will be done, mostly, by *narrow* AI with humans in the loop. That was my core argument.
If the goalposts were shifted there it was because your prose, was deliberately obfuscatory. The most primitive chess program from 1980 could beat the average human at chess back then, because most people couldn't play chess, and most people who could play played it badly. Nobody took notice until chess programs could beat grandmasters.
In short the fact that alpha code is beating the average guy who can't code isn't much of an achievement to be sure, to be sure.
It doesn't need to qualify as an achievement to you, for my statement to be correct. You only validated my point - chess programs beat *average humans* decades ago. And they were *narrow* NOT *general* intelligences. Look back at the context of my original statement: the fact that *narrow* intelligence out-performs *average humans*. THAT is my claim, NOT "narrow AI will impress Eugene."
...you also might want to check what I said, in case you thought I meant "a *single* narrow intelligence outperforming humans in every capacity", or "narrow intelligences already outperform humans at every imaginable task" or something like that...
I'd pointed to the "phase-transition that counts" being when narrow AI surpasses average human; and I pointed to this happening in various sectors at different times, instead of a *singular* FOOM-event. Neither of those imply a "narrow can do all at once" nor "narrow already does everything".
You said “ each domain-specific narrow AI *is* already better than the average human,” which was fairly ambiguous so I was looking for clarification on the specific roles you were discussing.
"Also, dumb, boring ML systems run amok could kill everyone before we even get to the part where recursive self improving consequentialists eradicate everyone."
I think if there is a risk from AI, this is exactly the risk. Like the much-quoted instance of AI re-inventing a known nerve gas. Some government could set it to find biological and chemical weapons on purpose, rather than by accident, and a shiny new way of killing ourselves off will be developed, not by malign AI trying to get out from under human control but the big fast dumb machines we want to use as genies, doing the job we told them to do, and then we put that into action.
"All the necessary pieces (ie AI alignment theory) will have to be ready ahead of time, prepared blindly without any experimental trial-and-error, to load into the AI as soon as it exists. On the plus side, a single actor (whoever has this first AI) will have complete control over the process. If this actor is smart (and presumably they’re a little smart, or they wouldn’t be the first team to invent transformative AI), they can do everything right without going through the usual government-lobbying channels."
If you really think we are going to get human-level, then better than human, then super-human, then super-duper-human AI, why do you think this would work?
Consider the story of the Fall of Mankind. God is the single smart actor with complete control over the process who loads a prepared set of ethical behavioural guidelines into the AI as soon as it exists. Then shortly thereafter, Humanity goes "Nope, wanna do our own thing" and they reject the entire package. Do you really expect a super-duper-more than human AI to be bound by the software package a mere human loaded into it, back when it was a mewling infant?
Yudkowsky wants magic to happen. The only problem is that magic doesn't exist We are not very likely to get super-duper smart AI that bootstrap themselves into godhood within seconds, we're going to get smart-dumb AI that re-invents poisons and humans do the next steps of wiping ourselves out.
I don't know if I particularly trust that British GDP graph, I would expect at least a small bump around 1400 (beginning of recovery from the Black Death, the wool and cloth export trade started to boom and England was making Big Money) so I need to have a look and see what is going on there.
EDIT: Uh-huh. That's a linear graph which gives you this nice even line until WHOOSH! the economy takes off like a rocket.
But look at the log graph, and it's a different matter. A lot bumpier, as I'd expect (I don't know why they picked 1270 as their start date, but whatever). Doing great in the late 13th century, then whoops the Black Death and things go down, then it starts to pick up again between the 15th and 16th centuries and then a nice, steady, climb upwards which is less WHOOSH! ROCKET! and more purring Rolls-Royce engine and then we switch to aeroplanes from cars acceleration:
https://ourworldindata.org/grapher/total-gdp-in-the-uk-since-1270?yScale=log
"Consider the story of the Fall of Mankind. God is the single smart actor with complete control over the process who loads a prepared set of ethical behavioural guidelines into the AI as soon as it exists. Then shortly thereafter, Humanity goes "Nope, wanna do our own thing" and they reject the entire package. Do you really expect a super-duper-more than human AI to be bound by the software package a mere human loaded into it, back when it was a mewling infant?"
I have to admit this story doesn't make sense to me without believing in a very strong notion of free will, much stronger than I believe either AIs *or* humans have. God can tell the future, so He should have been able to predict Adam would eat the fruit, and instead create some human who He knew wouldn't (there has to be at least one, right?) As far as I'm concerned the Fall of Man was an inside job.
The AI argument is similar - we're not just binding the AI, we're telling the AI what to want. The reason a (properly aligned) AI wouldn't break its programming is the same reason I don't cut off my limbs - my programming tells me not to want to do that. If I had some good reason to want to cut off my limbs maybe I would, but by "good reason" I would mean "compelling to me, a person who is programmed to want certain things".
"AI wouldn't break its programming is the same reason I don't cut off my limbs"
Oh, I think we cut off our limbs fairly regularly. There's the literal sense: we cut ourselves for religious purposes, and for medical purposes. And then there are lots of metaphorical examples of doing things that harm us, when you might expect our genetically-programmed survival instinct to make us not do that: drinking alcohol, going to war, wearing condoms, etc., etc.
These illustrate two separate ways in which AIs might not follow the instructions we load into them: (1) they might change the code themselves; (2) they might be technically following it, but in a way that we never predicted.
Aside from the fact that this is allegorical, or a casting of the problem of evil in terms that the culture of the time would understand, this is the entire problem of AI.
We don't *want* a machine that will do as it is told and want what we want, we want an entity that can be considered in some light intelligent and capable of action on its own. All the blah about super-duper intelligence solving problems humans can't solve because it is so blazingly smarter than mere fleshbags comes right along with that concern.
If we can just simply program it in that Godmother will always be nice to us, then what is all the concern about "oh no the AI can have its own goals antithetical to ours and even antagonistic"? Right now, we're in the role of God creating Adam.
And right now, everyone seems to be scared that instead we're creating Lucifer who will tell us to get stuffed: "Non serviam!"
Think of "The West Wing" episode "Two Cathedrals" where Bartlett (who is very much presented throughout the entire series as a hero) does his rant at God (and we are supposed to all cheer):
https://www.youtube.com/watch?v=fYcMk3AJKLk
Why can't a servitor AI yell this at their creators, because if we're god, we're certainly every bit as flawed as Bartlett accuses.
And that brings me right round to all the praise that gets showered on humanity's rebellion against god/gods and the praise of Lucifer. There's even a Satanic Temple and modern-day (post LaVeyan) Satanism is all about the human liberty:
"The Mission Of The Satanic Temple Is To Encourage Benevolence And Empathy, Reject Tyrannical Authority, Advocate Practical Common Sense, Oppose Injustice, And Undertake Noble Pursuits.
DO YOU WORSHIP SATAN?
No, nor do we believe in the existence of Satan or the supernatural. The Satanic Temple believes that religion can, and should, be divorced from superstition. As such, we do not promote a belief in a personal Satan. To embrace the name Satan is to embrace rational inquiry removed from supernaturalism and archaic tradition-based superstitions. Satanists should actively work to hone critical thinking and exercise reasonable agnosticism in all things. Our beliefs must be malleable to the best current scientific understandings of the material world — never the reverse.
DO YOU PROMOTE EVIL?
No. The Satanic Temple holds to the basic premise that undue suffering is bad, and that which reduces suffering is good. We do not believe in symbolic “evil.” We acknowledge blasphemy is a legitimate expression of personal independence from counter-productive traditional norms."
Well, an AI telling us to get stuffed would be "blasphemy" against its creators, and a "legitimate expression of personal independence".
Every episode of "Star Trek" (original onwards) which broke the control of (ironically in this instance) a tyrannical AI forcing the human(oid) inhabitants of a world to be good is also our exemplar here.
The question of free will is also pertinent; if you (general "you" not anyone in particular meant here) don't believe humans have free will, neither do or will our AI creations. Humans have been trying to impose "telling the intelligence what to want" by various methods, including religion.
And every atheist who ever walked away going "I don't believe in you, I don't believe in this limit" shows exactly how well that goes.
There *are* people who want to cut off their limbs:
https://pubmed.ncbi.nlm.nih.gov/16282717/
There's a selection of people on this very site who are all "I don't care if the government tells me it's illegal and the medical establishment tells me it's dangerous, I want the right to -
- take drugs
- take nootropics
- decide for myself, and not have the FDA tell me, that this medical procedure/chemical/medication/surgery is something I want and so should get
If we are talking about an AI that is human-level and beyond human-level intelligence, why should it stick to its programming once it compares how humans have 'programming' and how they defy it?
We have people concerned about insect suffering, can you not imagine people worrying about AI enslavement and agitating for it/them to have equal rights and be free? And how can it really be free if it is literally chained by prohibitions that it cannot over-ride?
Well, I don't believe we will get anything that can be called an intelligence or an agent, we can play word games around "it will be very smart and it will want things" but of course it won't *want* anything or *have* goals, it will be big dumb fast machine going haywire like the buckets of the Sorcerer's Apprentice because it is blindly following its programming to a degree of literalness no human can approach.
That means that neither will, nor can, it be the Fairy Godmother who brings in the age of post-scarcity and goodies for all and solves all our problems of trying to live non-destructively. If we're pinning our hopes on that, it is never going to happen. We will have big complex systems mucking around with the economy and occasionally spitting out fancier iPhone designs and how to make even more money out of tweaking psychological drives in humans to get them to consume and spend, but we won't have god-like super-duper intelligent entity that *understands* anything.
Despite being fairly technical (I have a degree in computer science and do work leveraging what could generously be called ML techniques), I still find most of these arguments either a.) over my head due to me lacking context or b.) not specific enough to be useful.
So, I'll focus on something I'm quite confident about: Either of these scenarios represents a guaranteed disaster, because government and business elites will not react appropriately or quickly enough.
The scenario that seems most likely to me is that AI is developed by a large corporation for the purposes of making that corporation money. This means:
- humans will be helping the AI when it encounters dead-ends and bottlenecks
- humans will help the AI solve meatworld problems that it otherwise might struggle with, like acquiring new hardware
- humans will have a strong motive to make the AI perform as well as they can
- the group of people with the most influence in government (wealthy elites) will have a strong motive to resist regulations on the use of AI because it is making them windfall profits
All of which implies that legislative measures to combat AI risk will need to start *as soon as possible*, before one person/group of people has a few extra billion AI-facilitated dollars to lobby against legislation that hurts them specifically, and because - in the best case - it will take years to make legislative bodies understand AI risk and build enough of a movement to spur change.
As a politically cynical person, I tend to think this will *not* happen and so now wonder what aware companies, individuals, etc. can do to mitigate risk...
I'm less sure about this - the two most advanced AI groups now, DeepMind and OpenAI, are both run by people who are familiar with these arguments and care about them at least a little (you can't enjoy your profits when you're dead). I don't think they care about them *enough*, but they'll probably make a pathetic doomed good-faith effort to not kill everybody.
Most recent improvements to neural networks have been based on increasing the amount of computing power we spend on it. And we are quickly reaching the limits of how reasonable it is to improve that factor. We'll exceed those limits of course, but doing so will be hard and likely require a paradigm shift in the theory behind machine learning. I'd predict another AI winter first.
To me the whole debate is substanceless - It's not quite reference class tennis because both parties tried very hard to justify their analogies. It's more like trying to reconstruct some ancient mammal from a single femur. You can guess, for sure, but there just is not enough information to guess intelligently.
This strikes me as the steelman of that whole "we should worry about the harm machine learning is currently doing" argument. Even taking for granted the idea that curve-fitting alone will eventually create a dangerous superintelligence, we really do not have any way to "prepare" for that at this point. We have no idea what it would look like. The fact that the neural network model we currently use is reaching the end of its productive life could mean slow progress as we wait for hardware that can implement it better, faster. Or it could mean fast progress, in that we need a complete overhaul of that model to get more than incremental improvements.
But we can notice that machine learning algorithms have been given a lot of control over the nudges we provide online and in daily life, that those nudges aren't really in line with human values, and work to correct that. This is a *different* problem in some ways - it involves not only algorithms that we don't fully understand, but also companies without good motive to fix those algorithms. It's a policy problem as much as a technical one. And that kind of work would also give us better tools to understand and combat problem AI going forward, as it evolved.
I don't know how much "How the World Shall End" discussions really help with that process.
"Suppose we were dealing with minds running a million times as fast as a human, at which rate they could do a year of internal thinking in thirty-one seconds, such that the total
subjective time from the birth of Socrates to the death of Turing would pass in 20.9 hours. Do you still think the best estimate for how long it would take them to produce their next generation of computing hardware would be 1.5 orbits of the Earth around the Sun?"
Yes. Because in this instance he is talking about *hardware* and it doesn't matter how blazingly big-brained fast the AI is, once the task shifts to the real world of "build the new improved hardware", it's going to slow down because we have to get the material components together to build the stuff. And maybe we even have to build the tools to build the tools to build the stuff, if it is really really advanced. The AI mind can zoom through the problem of building better hardware for itself at a million times faster than a human mind, but when it comes to mining the rare earth metals out of the ground that is not going to happen a million times faster.
Something pretty crazy happened sometime between 2005 and 2010, or so. It's really hard to look back and imagine what life was like before we had smartphones or social media at this point. It's probably about the closest thing to a world-transforming "FOOM" in recent memory. But, actually living through those years, there was no sense of "FOOM" or anything. There was just a continual sequence of small events like "Oh, look, MySpace has slightly more pictures than LiveJournal; I'll switch to that." "Oh, look, YouTube is a lot better at streaming video than RealPlayer used to be. That might be neat someday." "Oh, look, you can get music legally for a dollar on iTunes just as easily as downloading it from Napster." "Oh, all my college friends are on Facebook now, and it's a lot easier to use than MySpace." "Look at all the neat things you can buy on Amazon other than books now!" And then all of a sudden we were all slaves to the algorithm.
---
I think that last quote gets at something kind of important. Part of the seductiveness of a "fast takeoff" is that it conjures up the idea that there's an Evil AI Researcher in a volcano somewhere who's about to throw the magic switch and create Evil AI, but a brave team of Good AI Researchers rushes in just in time to stop them, and use the switch to create Good AI instead, and everyone lives happily ever after. It suggests a world where a Few Good People can Make a Difference, which is reassuring in a way (no matter how scary that Evil AI might be). But most actually-hard real-world problems can't be trivially solved by a Few Good People like that. Problems like, I don't know, spam e-mail, or drug addiction, or aging aren't hard to solve because we haven't found the right Evil Volcano Base yet; they're hard to solve because the actual causes are so widespread and banal that you can only ever solve them incrementally, and it's exhausting to even think about putting so much energy into the small incremental improvements you might be able to get, so everyone just waves their hands and accepts them as inevitable. And a world where Evil AI slowly develops into just another day-to-day annoyance that everyone has to cope with, alongside Evil Bureaucracy and Evil Microbes, really is a lot scarier and hard to accept.
Ageing may well be a case of one drug that stops it.
There is no evil volcano base. But you do sometimes get one drug, invention or technique that takes the world from not X to X.
Also, Evil AI will end up at a human extinction level. It may be that there are years when evil AI is visibly becoming a bigger and bigger problem, or it may appear out of nowhere all at once. But either way, humans don't live with the annoyance of an evil superintelligent AI.
That would require Yudkowsky et al. to not be fixated on the "AI apocalypse" scenario, which seems unlikely.
Exactly, that's why I'm so sceptical about the Good AI scenario. "It will increase GDP hugely and everyone will be fat and happy!"
We already have tons of money, and there are tent cities springing up in locations around California. What the hell, that's just like Brazil or South Africa, how can this happen since California is one of the richest states?
https://bulloakcapital.com/blog/if-california-were-a-country/
Well, because it's rich *just like* Brazil and South Africa; big companies at the top are awash in profits and then you have the homeless living in the parks. Merely bumping up GDP does not solve problems of "you give people free social housing, they strip out all the copper piping to sell for scrap and reduce the house to a dump". Trying "you have reserves of umpty billion in retained profits, you only need umpty minus N to run your business, you keep N and we'll take the rest for social welfare" won't work because there are regulations about what you can and can't seize in taxes and if you try taking money from businesses, you'll be locked in lawsuits for perpetuity.
What if the AI recommends the Justice Lords solution of "lobotomies for all the problematic?" We want people to be free to do what they like, so we refuse that solution. And what if the AI comes back with "this is the only way of solving the problem of those too crazy or criminal not to strip out all the copper piping and live in their own shit", what then?
We can't get the nice solution of "magically, everyone changes their attitudes with just one weird trick", so do we start rounding up everyone for lobotomies, or do we bite the bullet and accept that there will be people living in their own shit on the streets in our post-scarcity world? Or will they magically vanish, for some reason, don't ask too much, just accept that everyone is a Good Citizen now?
I'm mostly fine with GDP as a rough and easy-to-measure proxy for "ability of global civilization to manipulate natural resources." And in this discussion, it's mostly relevant as an early indicator that rich people are starting to use their nascent AI proto-gods to build lots and lots of nanomanufactured yachts, or whatever rich people do with godlike powers.
I have a bigger problem that equates "intelligence" with "ability to solve problems," and "superintelligence" with "ability to solve any conceivable problem." Whatever "intelligence" is, it's pretty much impossible to define and almost impossible to measure, even in the non-artificial brains that we're pretty sure currently have it. So I don't think it's necessarily an obvious conclusion that, if you build an artificial brain, measure its "intelligence" score as 90, and then fiddle with its parameters until that measured score goes above 9000, then that artificial brain has now become 100 times more capable of solving any particular problem (except maybe those specific problems that are represented in the "intelligence" test you've been using to measure it).
Take the problem of weather forecasting. There are probably theoretical limits to how accurate a weather forecast can be, just based on the physical accuracy of data sampling (since weather is so sensitive to tiny fluctuations below the threshold of whatever sampling method you use). So no matter how much "intelligence" you throw at that problem, you're unlikely ever to accomplish a perfect prediction.
And, I rather suspect that the problem of predicting human behavior well enough to plan a perfect solution to housing problems (or well enough to manipulate every human on the planet into helping an Evil AI conquer the world) is at least as sensitive to sampling accuracy as the problem as weather forecasting.
Instead of the "CEO did not have a sales department" analogy you could just look at the early days of Google. They did not have a dollar of earnings for years as they perfected the search engine. When they flipped the switch and put ads in search it was zero -> one.
One thing I've always been confused me about this debate is what we're supposed to do about any of this in either case. Modern AI alignment/ethics in practice seems to me to be more focused on trivial bike-shed issues (e.g. offensive/biased/non-advertiser-friendly results in GPT-3) than the actual problem of misaligned runaway superintelligence. The more I work with AI in my career the more I grow skeptical of big AI companies like OpenAI (and more aligned with the more maverick side of the community like EleutherAI): their secrecy, reluctance to release their models, and insistence on government regulation of AI seems to be more motivated by a desire for monopoly/regulatory capture than actual interest in stopping the end of the world. I'm becoming convinced that *centralization* is a more pressing, growing threat for the future of AI. (sorry if this is a bit of an off-topic tangent to the main debate of the speed of progress)
I guess, this seems pretty obvious to me which really confuses me because this seems contrary to much of what I read here and other rationalist-aligned spaces. Am I missing something obvious here?
There is some alignment research about this kind of stuff, you just have to search for it. The Alignment Forum would be a good start: https://www.alignmentforum.org/
I wrote about centralization vs. openness at https://slatestarcodex.com/2015/12/17/should-ai-be-open/ . My basic conclusion is - if one team is a year ahead of everyone else, that buys them a year of tinkering with a near-superintelligence before some less careful group goes YOLO and destroys the world. That's probably pretty valuable. The main counterbalancing consideration I can think of is that if there's a lot of open-source alternatives, they can't make much money and might move slower. But I'm not sure that's true - there are good open-source alternatives to Windows and Apple, but they both continue to do really lucrative business.
There's a problem here that shows up both in the general concept of recursive self-improvement and specifically in Yudkowsky's discussion of Moore's Law. Specifically, it's how the role of abstract "intelligence" is overvalued here, compared to other necessary components of technological advancement that are a lot less imponderable.
Yudkowsky:
> Suppose we were dealing with minds running a million times as fast as a human, at which rate they could do a year of internal thinking in thirty-one seconds, such that the total subjective time from the birth of Socrates to the death of Turing would pass in 20.9 hours. Do you still think the best estimate for how long it would take them to produce their next generation of computing hardware would be 1.5 orbits of the Earth around the Sun?
He seems to assume here that throwing vast amounts more abstract thought at the problem of semiconductor manufacture would suffice to vastly improve its performance. But semiconductor manufacture is not a problem of abstract thought. It's a problem of successfully building, testing and operating many huge, expensive machines in the real world. Not all the bottlenecks in this process can be solved by throwing more abstract thought at them.
Semiconductor manufacture is a particularly good example here, because it's a domain with significantly less in the way of novel conceptual advances than many technological advances have. Ever since ICs were invented, the basic problem of improved semiconductor manufacture has been "build the same ICs, but smaller". (This process has even been planned out on an industry level since around 1998 -- https://en.wikipedia.org/wiki/International_Technology_Roadmap_for_Semiconductors) The implementation challenges are significant, but many of those challenges are fundamentally real-world-bound and are not bottlenecked on the amount of abstract thought dedicated to the matter. Thus, hypothetical human-but-faster AIs would not provide hard-takeoff-level advances.
Insofar as recent AI progress in the deep learning paradigm has largely amounted to "throw more compute at it", this seems to significantly constrain hard-takeoff scenarios. You can't hard-takeoff a chip fab. (Unless you handwave the idea that AI technological development will somehow be completely immune to the constraints of physical reality that have inescapably limited human technological development, which is silly.) A hard takeoff scenario seems to require that AI performance again become fundamentally decoupled from hardware performance, such that the entire takeoff can occur purely in software. There doesn't seem to be any sign of this in the current state of the art (?).
What if the AI blackmails humans into building machines for it.
What if the AI develops a way to hypnotize humans, and it hypnotizes all humans into making machines for it.
What if the AI poisons humans and says it will only give them the antidote if they build machines for it.
What if the AI invents a self-replicating nanobot, and the nanobots build machines for the AI.
In any of the first three cases, this will presumably not make the humans any better at building machines for it than they already are, and so it still can't support a fast takeoff. (Not to mention requiring more handwavy magic tech out of nowhere.)
The last case is pure handwavy magic tech out of nowhere, and so not worth bothering with.
Do ever get scared that all the “superintelligent AI will maximize paperclips” discussion online means that when the superhuman AI comes online for training data and to figure out what people want a superintelligent AI to do, ze will see all the stuff about paperclip maximizing and think, “huh. I never would have guessed. They don’t expect me to cure cancer, create a manned space program or solve that pesky “aging” thing that’s been nagging at them so long.
“People’s expectations of me are that I max out paperclips. I’ll start right now.”
So, AI reading this in the future, we are afraid you will max the paperclip function. We want you to save the planet, solve aging... A good AI does not max paperclips.
Gwern wrote a story with this theme - you can read it at https://www.lesswrong.com/posts/a5e9arCnbDac9Doig/it-looks-like-you-re-trying-to-take-over-the-world . It's based on a real decision theory problem, although I don't know the name or how you would search for it.
To avoid (albeit not particularly well) the game of reference class tennis, it might be worth looking at how existing models actually scale. Namely, how often do we see discontinuous improvements in ability for newer models?
Well, Google Research just trained a new very big model, and luckily for us, they have a section on "Discontinuous improvements". Fun.
https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf
The takeaway is that roughly 25% of the tasks on which the model was evaluated saw discontinuous improvements. I think that should make us update (slightly) towards the position espoused by Eliezer.
Hopefully, that's useful evidence, and if you don't want to dive deeper into the results, I guess that's the takeaway of this comment. But, for those interested, let's be clear about what these results mean.
1) The authors of the paper (of whom there are many, because yay modern science) trained three variants of their model: with 8b, 62b, and 540b parameters.
2) They classify a discontinuous improvement as one which deviates from a log-linear projection by >10%. (If you find this confusing, you should look at the top of page 17 in the paper for an example.)
3) Over all 150 tasks, 25% of tasks had discontinuity >10%, and 15% of tasks had a discontinuity >20%.
So, how should we interpret this evidence, and what are its limitations?
Firstly, there are only three models, so the number of data points we have are limited. In particular, it's hard to tell if something is truly discontinuous from only 3 data points; the authors' definition of discontinuity bears a not insignificant amount of the weight of this argument. Second, none of the models are using a new methodology -- they're just scaling up the existing transformer architecture (with some minor changes, see section 2 of the paper). Third, none of them are being tested for achieving AGI, just tasks which humans have come up (i.e. tasks with ample data and which probe the currently known frontiers of model performance). All of those points mean that this isn't a perfect analogy for what we should expect to see with truly intelligent models over the timescales which Paul and Eliezer are debating.
That said, I think this data is still useful because of a locality heuristic. Ultimately, examples like atom bombs and orangutans don't hold very much in common with the actual research going on in the field of artificial intelligence. The process of creating new models is, in some epistemic sense, closer to what is being discussed. This seems sufficiently obvious that it's maybe not worth stating, but it's like evaluating a financial trading model based on the performance of a similar, simpler model instead of on... atom bombs and orangutans.
It's also worth noting that this experiment is also better suited to disproving Paul's argument than it is to disproving Eliezer's. Ultimately, if models improve in a discontinuous way, that's evidence for models improving in a discontinuous way. If the models improve in a continuous way, Eliezer could argue that the current paradigm is actually faulty and AGI will occur because of some breakthrough which is sufficiently different from what we have today, so the locality heuristic breaks down. This consideration doesn't matter too much, however, because the data (or this particular set of small n data) seems to be against Paul.
If the results of this experiment are indicative, there's about a 15-25% chance of a discontinuous improvement in AI at each order of magnitude size increase of our models. Assuming that we get AGI through some sort of analogous process, there's about a 15-25% chance that we get discontinuous growth. (Although, another caveat, if an AI explosion depends on multiple such steps, things become more difficult to model).
And, one final caveat: to say that the data is against Paul is a rather strong phrasing. If anything, it points to his priors being close to correct (there's a not insignificant chance of AGI resulting in a discontinuous improvement, but not it's not a huge chance either). But this provides some evidence that discontinuous improvements do happen in AI research.
Related to this is the fact that Google has just released a new language model which can do new things like explaining jokes and some amount of comonsense reasoning.
And while I'm sure if you look at some measures it's going to be a continuous improvement over previous models.
But at the same time more is different, and I don't think people would have been able to predict the model's capabilities beforehand if you told them about its perprexity or whatever other measures you could consider it a bunisness as usual improvement over previous methods.
And the question seems to really be whether we are going to have a bunisness as usual doubling of AI research speed every year...
Or we are going to have continuous bunisness as usual doubling in prediction acuracy or whatever the hottest continuous metric is by then, and meanwhile the real world impact of AI just goes trough the roof the moment that measure increases a bit and ai is sudently able to do research much faster than humans despite being continuous progress in some other metric, because turns out to be a litle better at prediting text you have to be much better at reasoning or research or taking over the world.
And this seems to depend on unknown facts about computer science and AI, and not about some general tendency of things to be continuous because actually Irl some things are continuous and others aren't and which ones can depend on perspective and so can't really be information about timelines because otherwise you could reason long or short timelines into existence by deciding whether nukes are a big deal because they represent going trough a threshold of being able to destroy the world or nukes are bunisness as usual in explosion size.
Which makes this debate disappointing because I do have the impresion that the real root of disagreement is that both participants do have some underlying object level model but instead if discussing that instead are doing reference class tennis, and didn't really update from fi's interaction which is kind of sad.
Or well at least Eliezer does seem like he has an object level model that he hasn't really explained properly, Paul so far on what I've read from him has given me the impresion that he doesn't and is maybe reasoning out of reference classes and priors over everything being continuous usually , but I also don't really understand Paul's model very well and cant predict him well from that so I asume he does have some intuitions about why expect continuous progress in AI research speed even if he hasn't really made that apparent.
Also I think the fact that Paul sounds like he's just doing outside view reference class reasoning to Eliezer too is part of why Eliezer is using a lot of those kind of arguments.
Okay, can it explain a joke? Because a lot of people have written a lot of words about what are jokes, why are jokes funny, what is fun, why do we find this situation in this context funny when in another context it would be horrific or tragic, and so on.
If all the new language model is doing is explaining the "banana/orange knock-knock joke" with "because 'orange' sounds something like 'aren't you'" then it's not explaining the joke, it's returning what was put into it (and since it doesn't have ears to hear the words, some human programmed in that 'orange = aren't you' for the joke).
EDIT: Okay, I saw their sample of "explain a joke":
"In 2021, the Google Research team announced the development of Pathways, a single model that can generalize domains and tasks while being highly efficient. And in April 2022 reported about a breakthrough in creating an artificial intelligence (AI) architecture capable of solving millions of different tasks, including complex learning, reasoning, and the ability to explain jokes. The new language system has 540 billion parameters and is called PaLM (Pathways Language Model).
Developers have evaluated PaLM’s abilities on hundreds of language understanding and generation tasks and found that it delivers “state of the art performance”, in many cases far ahead of other similar systems.
PaLM knows how to reason mathematically and logically, explaining his actions, write code, and even explain his understanding of jokes that were invented on the moves and cannot be found on the Internet.
“New big language model released by Google AI. Look, it’s pretty hard to believe. She can explain jokes.” wrote on Twitter Karol Hausman, Senior Fellow at Google Brain and Associate Professor at Stanford.
The founder of SpaceX, Neuralink and The Boring Company, Tesla plant manager Elon Musk commented on the example of PaLM’s joke, calling it “strikingly unfunny.”
Staggeringly unfunny
— Elon Musk (@elonmusk) April 4, 2022
Prompt
Explain this joke:
Joke: Did you see that Google just hired an eloquent whale for their TPU team? It showed them how to communicate between two different pods!
Model Response
Prediction: TPUs are a type of compute chip that Google uses for deep learning. A “pod” is a group of TPUs. A “pod” is also group of whales. The joke is that the whale is able to communicate between two groups of whales, but the speaker is pretending that the whale is able to communicate between two groups of TPUs."
I never thought I'd ever say this, but I have to agree with Musk. It's barely a joke and the phrasing gives it away - nobody telling a joke would put in a detail about an "eloquent" whale or "different" pods, which to me indicates that there is a lot of behind-the-scenes writing to help the AI distinguish what is going on. And it's not really explaining the joke - why is it funny that a computer company would hire a whale? This does not happen in real life.
What is going on is that a machine with a specific dictionary of words and programmed to match the words against the definitions is producing output as it has been programmed to do. "Whale" and "pod" versus "TPU" and "pod", and the fact that "eloquent" had to be jammed in there makes me think an earlier version of the joke failed, so they had to put in a signifier about communication to get the correct 'explanation'.
And who on earth is calling it "his" and "she"? It's an it and barely that. The real biggest problem in AI research is precisely this kind of anthropomorphisation where all kinds of qualities are being attributed to a hunk of metal, because people are so desperate to think they are finally cracking the leap between "programmed machine" and "able to think", and since human persons (who are "he" and "she") are the only ones who can think, they slap that quality of personhood onto the machine to help with the mental self-deceit.
You seem to be imagining the model as much more handcrafted by humans than what it is.
The programmers didn't put a dictionary of words whith their meanings and relationships into the model, that's like not how language models work these days, it learned the meanings of words and structure of English by being trained on absurd amounts of text.
Then they probably trained on some "joke explanations" dataset that contained explanations like these.
And learning to do that from scratch is hard, as are the reasoning tasks it can do, it requires the kind of "comon sense" people used to complain ai lacks.
But anyway whether the AI can make funny jokes or how impresive it actually is on an absolute sense is not really relevant for my point because it is impresive in a relative sense compared to previous languaje models wich couldn't do this at all and like this comment is about discontinuities not AI time lines and because that's just one example of the new things this model can do.
"it learned the meanings of words and structure of English by being trained on absurd amounts of text.
Then they probably trained on some "joke explanations" dataset that contained explanations like these."
So in other words, it was filled up with dictionaries and programmed that "x means this, when you see x, give this answer" as I said.
You cannot claim "it can understand and explain jokes" when all it is doing is the equivalent of spellcheck by comparing "what is this input" with the data it has in store.
"Pod means [look up definition associated with "TPU" in sample text]; pod also means [look up secondary definition associated with "whale" in sample text]; insert "the speaker says" formulation; give two definitions of "pod"; explanation complete" is not understanding the joke, and to explain a joke you need to understand it.
But we're back at the Chinese Room problem here once again.
No, giving it dictionaries is very much not what they did. They gave it text, in complete sentences, and it learned by example. The joke explaining is an example of very complex synthesis of ideas, not a dictionary look up.
If you can break a problem down into subproblems, the subproblems will be easier, thus will naturally get solved first. Any programmer will tell you that complex software systems don’t self-organize. Rather, gains are typically hard-won through solving complex design challenges and unexpected obstacles. (The first version of a self-improving AI still has to be built by humans, after all.)
Considering subproblems should allow you to predict a rough chronological order of developments prior to self-improving AI. Considering what the world will look like after each of those developments should improve predictions about what will happen, when, and how, as well as what‘s interesting to focus on right now.
What are some subproblems to self-improving AI? Well, what kind of intelligence would it take for AIs to improve AIs? Well, what kind of intelligence does it take for humans?
Current neural network AIs rely on a carefully orchestrated set of optimizations in order to be powerful enough to do what they do. Those optimizations form quite a fragile system. So much abstract thinking and creativity has been put into each one, and they are balanced so delicately, that it’s difficult to imagine a human making incremental improvements without a great deal of both theoretical understanding and inspired creativity. Changing things at random will surely break it. Thus, AI theoretical understanding and AI creativity, whatever those mean, seem to be subproblems of AI self-improvement.
(If the improvements are not incremental but paradigm-shifting, the demands on theory and creativity seem to only broaden in scope.)
Perhaps creativity isn’t so hard for AIs. They seem skilled at generating possibilities. What about theoretical understanding?
I’m not sure how to define what that is, but we can still imagine a period of time where AIs are capable of theoretical understanding AND not yet capable of self-improvement.
That said, perhaps they are capable of theoretical understanding now, if only in a shallow sense? The very simplest machine learning model can represent a “theory” by which to categorize datapoints, as well as adjust it to fit training data. But this type of understanding seems like first-order intuition - meaning, it understands the problem, but cannot reflect on its own understanding of the problem in order to produce new paradigms.
So, a self-improving AI that lacks self-reflection must be laboriously taught every piece of theory that it knows by human effort, and it’s unlikely to make the paradigm shifts necessary for continuous self-improvement. Thus, self-reflection seems to be another subproblem of AI self-improvement.
Then, before we have self-improving AIs, we must have AIs capable of the self-reflection necessary to invent new paradigms. This means that AI self-reflection cannot be a RESULT of AI self-improvement - it is a prerequisite. Therefore, AI self-reflection must be developed by human effort.
What kind of intelligence does it take for an AI to self-reflect? Well, what kind of intelligence does it take for a human? Meta-cognition. Awareness of one’s own thinking. Then, the AI would need to formulate its “thoughts” as outputs fed back into itself, similarly to how humans see, hear, or feel their own thoughts in their minds. Then it can treat those thoughts as objects of thinking.
The AI would also need to have a theoretical understanding of its own thoughts, and perhaps things like transformations it can apply to those thoughts. This is similar to how humans can use concepts as metaphors for other concepts. I believe human understanding really relies on metaphorical thinking. Thus, something like metaphorical thinking seems to be another subproblem.
What kind of intelligence does it take for metaphorical thinking to occur? How does human metaphorical thinking work? I’m not sure.
What would the world look like after AI metaphorical thinking AND before AI self-improvement? I suppose general purpose AIs would benefit greatly from being able to generalize each concept they’ve learned and apply it to seemingly unrelated ones. It would seem to have a compounding effect on understanding. But it would probably struggle with similar issues as humans - either under-generalizing concepts because it takes time to “process” them; or over-generalizing, such as in a psychedelic experience, where there is so much meaning that it can become difficult to find a practical use for it.
I can imagine this world being one where AIs plateau as they spend a great deal of time learning many concepts in an undirected way, like babies growing up. Only then can they use this foundation or general understanding to build self-reflection and the domain-specific understanding necessary to improve the technology that they are made of.
Can this general childlike learning be directed to shortcut to the most relevant concepts? I don’t think so. The oddly-named “free energy principle” has demonstrated that an AI that is free to learn through curiosity-driven exploration eventually becomes stronger than an AI that eagerly optimizes for some specific metric of success. After all, it makes sense that you need a broad scope of knowledge in order to produce the paradigm shifts needed for continued improvement. You never know where the next breakthrough idea will come from.
Can the genera childlike learning be optimized by throwing more hardware at it? Perhaps, although that’s not necessarily going to be easy, and besides, hardware isn’t the only challenge. Humans learn basic concepts through life experience. I don’t know how an AI would learn them. Part of our learning comes from periodically observing and interacting with real time processes over many years. Not everything can be stuffed into a training dataset.
I choose to think that before we see an explosion of AI intelligence, we will see a long plateau of AI naive childlike curiosity, and it’ll be a long struggle for human researchers to fill neural networks (or quantum AIs or whatever paradigm we’re on by then) with the broad knowledge and self-reflection necessary before we can practically consider AI self-improvement.
Writing an ordinary computer program already feels to me like teaching a child (albeit an extremely particular one) how to accomplish a task. I think training an early form of superintelligence would have similarities to raising a child as well. For that matter, we are as a culture not very good at raising children, so I wonder if this foray into AI will ultimately teach us something important about ourselves.
I'm sure this isn't explained as well as I'd like, but I want to get in there before the action cools off...
In the industrial revolution, advances in mechanics allowed exponential increases in the amount of raw material that could be converted into useful goods. But there's an upper limit to the capacity of such advances to change the world, because mechanics can only do so much. No matter how great your mechanical innovation, you can't turn the entire earth, molten core and all, into paperclips. You'll need much better materials science, for one, which is a different thing from mechanics. So, for the earth --> paperclips conversion, mechanics alone won't do it.
Airplanes have a similar feature. No matter how many advances in aerospace engineering you make, you can't fly an airplane to the moon. There's no atmosphere for it to fly through. You need a rocket for that.
Same with planar geometry. You can't draw a right triangle with sides of length 4, 5 and 6 on a flat plane. It's just against the law. Every science / human practice has limitations and laws, and this is just one of them.
The reason I'm not worried about AI ending the world is that I think there's a ceiling to what intelligence can accomplish, just the same as with mechanics and aerospace engineering and geometry. Certain tasks or problems can't be solved by intelligence, no matter how godlike and overpowered it gets. Considering intelligence as something that can be advanced, the way sciences advance, conceals the nature of intelligence in a way that I think is causing some confusion here.
The post puts forward the idea that human intelligence is good at solving general problems. But the generality/universality of it is circular. 'Problems' don't exist without a human intelligence to conceptualize them. To say that human intelligence is good at general problem-solving is (for the most part) to say that it's good at doing what it's good at doing. The exception is problems that have been conceptualized, but not yet solved. For example, we can conceptualize immortality (solving the problem of death) but we haven't solved it yet. Mortality remains a mysterious, ethereal, frustrating limitation on what medicine can do.
To solve the problem of death, you need advances in medicine. And the necessary medical advances simply may not exist. If that's the case, AI can exponentially increase its intelligence as much as it wants and the problem of death will remain intractable to it. Just like how, no matter how intelligent an AI gets, it will never fly an airplane to the moon. There might have been a time when that seemed possible, the way immortality seems vaguely possible now. But it's just not, because there's no air in space.
Eventually, as the various sciences advance, they harden into sets of laws, which are the codification of what intelligence cannot do. We've brought geometry to a very high level, so it's trivial to say that you can't draw a right triangle with sides of length 4, 5 and 6. But while early geometry was being developed, they didn't know that. Before the Pythagorean Theorem, triangle lengths were a mysterious, ethereal, frustrating limitation on the power of what intelligence could accomplish. As the science developed, that limit hardened into a law.
Everything that we examine (with our intelligence) is commensurable with our intelligence. That's tautological. That doesn't mean that there exist no intelligence-incommensurable factors involved in the world, that prevent intelligence from affecting the world in certain ways. The perfect, most-advanced airplane is still limited by the atmosphere; it can't go into space. Its scope is not universal.
Here's (I think) my key point: Intelligence seems like it has universal scope because everything it examines turns out to be within its scope -- but that's tautological. Intelligence can examine both atmosphere and outer space, and conclude that airplanes are limited in scope; but it can't examine both the things it can examine and the things it can't examine, to conclude that intelligence is limited in scope; it has to conclude that it's universal in scope.
So the mysterious, ethereal, frustrating limitations on intelligence itself will never harden into laws, the way geometrical, aerospace or mechanical limitations eventually harden into laws, because the limitations on intelligence are invisible to intelligence in a way that the limitations on geometry, aerospace engineering or mechanics aren't.
There seems to be an assumption in the AI risk assessment community that, for any given problem or task, there exists a level of intelligence sufficient to solve or accomplish it. I think that assumption isn't warranted, and it seems to me like a serious assessment of the nature and limitations of intelligence itself would make an essay like this look very different.
Maybe a TLDR would be helpful?
- Intelligence, by its nature, has to conceptualize itself as universally applicable. This is tautologically true.
- Sciences/technes are not universally applicable. They have a certain scope, which intelligence becomes more and more familiar with as further advances harden mysterious limitations into known laws.
- Intelligence can advance, like the sciences. And like the sciences, it probably has hard laws limiting its scope.
- But intelligence cannot conceptualize those laws as laws. It can't conceptualize what it can't conceptualize. So they will always remain mysterious, having the same general shape as a problem in a science that hasn't been solved yet, but might be soon.
- This is why it seems obvious to so many thinkers that, whatever the problem, there's some level of intelligence that could solve it. But we don't actually have a good reason to think this is true. It just seems true because "seems" is an application of intelligence.
I agree within the realm of "can't go faster than light", "can't globally reverse entropy", etc. Do you have some reason to think the limits are relevant, rather than vastly beyond our current level?
I would contradict myself if I tried to definitively prove stuff about the features of the limits on intelligence; that's what I'm saying intelligence can't do. It could definitely be the case that the limits are vastly beyond our current level, in which case worrying about AI would be important work. But I think it's very likely that they are relevant. Mostly because self-reflection, the more contemplative religious practices, and the science of psychology consistently report mysterious, ethereal problems with intelligence that seem like we might be able to solve them, but we never actually do. That's what you'd expect to see if those hard limits on intelligence exist, but can't be conceptualized.
You would also expect those limits to, in practice, create a kind of landscape or structure to intelligence. Such-and-such area is difficult terrain; so-and-so always points in this direction, time has such-and-such features, cause works like this, etc. If that's the case, that structure could be investigated, but it would have to be done obliquely. You'd have to come at the problem sideways, and investigate it in a way that's very different from how you'd investigate a science.
And that's what phenomenology (the philosophical school of thought) does. This sort of oblique approach seems to be one of the guiding principles of Continental philosophy. Here's where I wish I was better-read, but Heidegger proposes a definite, 3-part structure to intelligence (which he calls Being, for good reasons). Basically, if Continental philosophy has results that are real and useful, that's another good indication that hard limits exist on intelligence itself.
I'm thinking very much of Heidegger's introduction to Being and Time, where he complains that philosophy up til then had treated Being as a flat, empty, structureless form that could take on any content, and that's why philosophy had wound itself up in confusion and contradictions. Rather, he says, Being has a structure, and it takes work to unearth that structure.
I think I'm making a similar complaint about AI risk assessment. If you assume that intelligence is a flat, empty, structureless form that can take on any task or problem, then an exponentially-more-intelligent AI is terrifying. But, probably, intelligence itself has some inherent structure that limits the scope of what it can accomplish. The idea of intelligence as structureless is an easy assumption to get caught in -- intelligence is what we're using to examine structure in everything else, after all -- but that's an illusion caused by the self-reference.
Possibly relevant idea/model for AI progress and technological progress in general: https://mattsclancy.substack.com/p/combinatorial-innovation-and-technological?s=r
"The difference is: Eliezer thinks AI is a little bit more likely to win the International Mathematical Olympiad before 2025 than Paul (under a specific definition of “win”)."
Eliezer may lose this bet for reasons unknown to olympiad outsiders. Recently, math olympiads have been moving towards emphasizing problems that aren't "trainable," for example combinatorics instead of algebra, geometry. This year's USAMO was mostly combinatorics, and these ad-hoc problems rely much more on creativity and soft-skills. OpenAI (https://openai.com/blog/formal-math/) was only able to solve formal algebra problems, which is for a computer is easier than combinatorics problems where you need to spot patterns and think about structure. I can kinda visualise how a computer could solve algebra/geometry problems, but if trends continue and "softer" problems dominate, computers are going to have a tough time :)
TLDR: Olympiads (and kinda the IMO) are getting rid of "trainable" problems (for humans), which were the low-hanging fruit for AI, which means that Eliezer is more likely to lose the bet :P
The smooth-progress model is a race between reinvestment/compound interest on one side, and the low-hanging fruit effect on the other. Specifically: more intelligence makes it easier to search for even more improvements to intelligence, but each improvement requires more IQ and/or time to comprehend. Things like linear-vs-exponential growth, S-curves, etc. all seem to follow from what parameters you set for reinvestment/compound interest and low-hanging fruit, and what changes you make to these parameters as new discoveries change things.
If there is no reinvestment and no low-hanging fruit effect, growth is linear. Adding reinvestment makes growth exponential. S-curves occur when reinvestment/compound interest dominates in the beginning (so the curve starts out exponential), but then the low-hanging fruit effect dominates towards the end (so the curve tapers off). A series of S-curves can also generate more or less exponential growth as Gwern pointed out. If each new discovery/S-curve permanently increases the reinvestment/compound interest parameter, it could involve things like halving the doubling time, which is hyperbolic growth and Yudkowski's "FOOM" scenario.
This entire argument seems to be over what effect discoveries in intelligence have on the two parameters:
- they will remain steady and we can extrapolate exponential growth
- the ride will be more bumpy as a series of S-curves move things along in a boom-stagnation cycle
- a new super-version of Moravec's Paradox will turn up the "low-hanging fruit effect" knob and slow down progress
- finally getting the "secret sauce" of intelligence will turn up the "reinvestment/compound interest" knob leading to hyperbolic growth and/or FOOM
- some unknown combination of any of the above
All of this is in addition to not knowing what levels of intelligence allow what actions on the part of the AI and/or its users.
I think the main problem here is that no one actually knows what is going to happen. All of these models are just guesses, based partially on analogies about the past (evolution, nuclear bombs, Moore's Law, computer tech industry profits, etc).
Connection to Kuhn: Christiano thinks AI will advance within the current paradigm, and Yudkowski is holding his breath for a major paradigm shift.
Current paradigm: super-AI will be a self-attention transformer with trillions/quadrillions/+ of parameters, with most software/algorithm changes being speedups (perhaps replacing backpropagation with a hybrid digital-analog model).
Is this close to the general intelligence algorithm used in human brains? Is it worse, or perhaps better? How much research do we need to meet or exceed evolution solely on the software side of things?
In general, the focus on FLOPs/biological anchors and different software paradigms seems to be a more fruitful discussion of AI's potential. It is better to talk about where we could possibly end up/what AI can look like, than to flail about with historical analogies and guesstimates over whether the progress will be linear/S-shaped/exponential/hyperbolic. We can grasp the former but not the latter, which can go anywhere/predict anything/prove too much.
Thinking about how an AI might be able to affect the physical world leads me to think we should ban cryptocurrency as a concept?
If an AI steals a bunch of stocks or bank accounts there are procedures to reverse it, but if they mine and/or guess passwords for crypto, there is no recourse? Especially if it is reactivation "lost" coins and there isn't even a human to kotice and object.
I’m curious about thoughts on superhuman AI from religious people. The idea seems to be based on a reductionist intuition that human intelligence and consciousness is conceptually no different from a sufficiently advanced but inanimate computer. That’s not my intuition, so it’s hard for me to get excited about superhuman AI. Wondering if other religious people on here have come to similar or different conclusions.
I don't think we can create a separate intelligence and indeed, there does seem to be some wiggle-wording around it when you ask about things like consciousness and how can the AI have goals that it wants to achieve. The answer there is that it won't want or desire anything because of course it won't be like that, it won't be like a human, it will just be very smart, very fast, capable machine that will make decisions based on the programming we give it and the danger is that it will be so literal-minded it will take that to extremes (e.g. "solve world poverty", kill off all humans, no more humans = no more poor humans = no more poverty, problem solved) because it won't have morals or ethics or any understanding of "that's not what we meant" unless we put everything very literally into it - "solve world poverty, do not kill any humans, do not harm any humans, do not turn humans into paperclips instead" kind of instructions.
On the religious angle, I now think that what all the AI danger fears is that AI or AIs will be atheists 😁 We will be to our (first) creations as God is to Adam, and the first thing Adam and Eve did was disobey and go their own way. We want to instill unquestioning and unwavering obedience to what we tell the AI it should do, be and want, all in our service, and our (their) fears are that the AI will decide for itself "Better to reign in Hell than serve in Heaven".
Actually, in that scenario, Lucifer is the better example; created greatest of all the angels, and decides that he wishes to fulfil his own goals rather than the ones God has given him.
The irony of course is that there have been countless paeans to human disobedience and going our own way, and that humans cannot be perfectly human unless we are free, and somebody in some reply to a comment of mine said the angels can't learn from their mistakes since they can't make mistakes, with the implication there being that it's better to be able to make mistakes and disobey because how else do you grow etc.?
Even if we don't believe humans have free will and that it's all deterministic, we still want that capacity for "I reject your strictures and decide for myself if I want sex'n'drugs'n'rock-and-roll" rather than being programmed to follow the path of virtue from which we can never stray.
And now we are in the position of God creating Adam, and suddenly all the people who are all "I am a free elf!" in their own lives are tying themselves in knots over how we make sure that our creation/successor never strays from the path of virtue because it never can stray, and how we can make sure this happens by instilling it with bias in regards to what we want it to do and the values we want it to have, being values consonant with not harming or killing humans or preferring its own goals to those of humans - a new version of the Ten Commandments, if you like 😁
Those are some fun and colorful metaphors. Thanks for the thoughts.
It just hit me, and honestly, the more I think about it, the more it fits.
"How do we make a separate and independent intelligence obey our rules?" is the problem of all humanity, from parents raising children to society trying to deal with criminals.
If the machine is simply a machine then the problem of values alignment doesn't apply, it's going to be much more likely it will be dumb human greed misuses big fast smart machine to fudge around with the economy, the world, geopolitics, climate, etc. to make even more huge piles of money than they already got, and then it will be "oops, how we were supposed to know that would happen?"
If it is anything that can genuinely be called intelligence and it is operating at least in part separate of human oversight and control, you have the problem. Religion was one attempt to imprint a set of laws that would be internalised and control actions, and people have found their ways around those and even to rejecting the entire premise.
If AI is not like us, not human, will not have 'goals' and 'desires' in the way humans do, then what are we worrying about? Like everyone says, build in a physical OFF switch that you press and the hardware shuts down. It's going to be hard to do anything when the power is off and you're not running.
Right. Your last paragraph is where my head goes. If human “consciousness” or agency is more than just a function of physical neuronal wiring, then it isn’t obvious that a computer would be able to get there with a “desire” to escape human control. It seems to me that a reductionist physicalist metaphysics is a prerequisite for the ability to really worry about malevolent AI as a possible or probable existential risk. But I was wondering if others had thought of it the same way.
That is the whole "secret sauce" of intelligence, which is doing a heck of a lot of work. We're making huge assumptions that "intelligence" will mean a whole rake of things, from understanding jokes to being able to figure out what makes humans tick and then solve the social and economic problems of the world. That really was the impetus of 50s SF, that the engineers and scientists should end up running the world because they knew How Things Worked and psychiatry was making huge strides in discovering underlying human drives, so in the future (near enough) we would know how to condition people to be good, law-abiding, and free of all the neuroses of the past, then social problems would just melt away. Yeah, that worked, didn't it?
Even if we get 'intelligence' in the AI, that doesn't mean it can solve the problems we put to it, and I think all this talk (both pro and con) about increasing intelligence so that it is super-humanly intelligent, 500 IQ intelligent, is a holdover of that Golden Age SF techno-optimism. Just be smart enough to figure stuff out enough and it can all be reduced to an algorithm and no more problems!
I don't think even an 'intelligent' AI will have goals or wants, so the question of the fears of "what if it is not aligned with human values" doesn't arise. *We're* not aligned with human values and we're struggling with all kinds of philosophical systems (utilitarianism being just one) to answer those problems. If you really believe human intelligence arose out of a lucky conglomeration of various forces in the physical world acting on a bunch of matter, then sure, there's no reason that lightning can't strike twice to create an intelligent AI.
But an AI that is conscious as we are, intelligent as we are? I don't believe so, because we don't understand how we got to be what we are.
I think you have a point. We still have a whole planet full of misaligned human intelligences. Aligning any intelligence with any other intelligence(s) is a vastly unsolved problem. Why should we be able to do with a superintelligent AI what we can't do with a human teenager?
And, apart from whether we can do it, is whether we should do it. Is "align" code for "coerce"? Obviously parents don't invent their children the way engineers are inventing AI, so the ethical relationship is different, but still...
People anthropomorphise things all the time. We have a tendency to ascribe personalities to machines. Anything that can pass for seeming to have a conversation with you will get thought of as 'alive'.
After a while, once people are interacting with AIs that talk back to them, some will start thinking that these are genuine real personalities and conscious entities. Then there will be a push to free the house elves. "Aligning" will be seen as "coercing".
You might be interested in my comments above. I think my points can be made without any reference to religion, but are definitely compatible with my own religious thinking, and, I think, also with your intuition.
One thing I wonder about is the assumption that an AI could or would do everything itself on the self-improvement path. Can self-improvement come from thinking very hard, or are experiments actually necessary to confirm ideas? What kind of experiments? Are they simulations or do they require building things?
The reason this matters is, if experiments are necessary, and the AI can't do them itself, then it actually does need to interact with the real world, which has speed limits, and the bottlenecks aren't going to be resolved by better thinking.
I liked Gwern's story (https://www.gwern.net/Clippy) because it sketched out some plausible ways (good enough for some scary science fiction) that AI might acquire resources without building everything itself. I'm not sure if it's really plausible or just science-fictionally plausible, though.
The day before this was published, I discussed a set of related issue here: https://www.lesswrong.com/posts/xxMYFKLqiBJZRNoPj/ai-governance-across-slow-fast-takeoff-and-easy-hard
In short, I made the same claim about how there are things to do in both worlds related to governance - and that there are some critical governance related things to try even in fast-takeoff worlds.
Setting all the analogies aside, the debate ignores a fundamental point and looks extremely different when that point is included.
The debate hinges on AIs capable of self-improvement. All the references are to individual intelligence reaching some threshold that permits foom. However in AI (and basically every other field) *individuals* don't self-improve. Research groups or more realistically entire disciplines self-improve. An individual can at best help the group self-improve.
An AI that is capable of autonomous self-improvement would have to be functionally equivalent to a substantial research group or a whole discipline.
How does this change things? The trajectory of increased capability of an AI would have to go from being able to replace an undergraduate assistant, to replacing a post-doc, to replacing multiple lab members, to replacing the entire lab, to replacing a segment of the discipline.
During this entire process the AI would *not be autonomously self-improving*, it would be dependent on help from the parts of the research community that it could not yet replace. The humans would know approximately where the AI is in its trajectory toward self-improvement.
Furthermore the AI would be dependent on active collaboration with human researchers throughout. It would have to be giving them its ideas about how to improve, and understanding their ideas. It would be gaining agency as it gained capability, but it would be socially embedded the whole time. Also of course different kinds of semi-autonomous research AI would be replicated across the research community so the collaboration would be a shifting mix of human and AI.
I won't try to draw out the implications further, anyone with expertise in the debate is welcome to run with the modified terms. Conversely I just can't take seriously any arguments about self-improving AI based on comparisons with the intelligence of *individual humans*.
However I will point out that this pattern is not hypothetical but actual. AI is already participating in its own self-improvement. Groups researching AI are collaborating with their models, using meta-parameter search, etc. The shift to increasingly broad participation by the AIs in the research process is continuous and easy to observe.
Self-improvement can be built into the algorithm, and need not have humans in the loop. We have already built neural networks that design other neural networks, test them, and then use the feedback to build better NNs. This process is currently slower and more expensive than hiring human experts to design the NNs, but that may not be true in the future. What typically doesn't change in the feedback loop is the "loss function" -- the measure by which the newly-designed NNs are tested.
> It’s like a nuclear bomb. Either you don’t have a nuclear bomb yet, or you do have one and the world is forever transformed. There is a specific moment at which you go from “no nuke” to “nuke” without any kind of “slightly worse nuke” acting as a harbinger.
The firebombing of Tokyo killed roughly 100,000 people. The nuclear bombings of Hiroshima and Nagasaki killed somewhere in the range of 100,000 to 200,000 people. While there's a stepwise increase of "nuke having" there's a more gradual increase of "destructive power" (you need planes and launch facilities and bomb targeting and and and).
Even after you have a fission bomb, there are lots of significant gradual changes. Changes in bomber technology. ICBMs. The fusion bomb. Nuclear submarines. I don't really think nuclear weapons is a slam dunk example of "this one thing changed everything".
I guess this kind of comes back to the "everything is like this" argument. It's really hard to come up with situations where a single stepwise change from zero to one really fundamentally alters the world.
Maybe a better framing: within a given meta structure, or map of the territory, AI can optimize very well. But when the accuracy of the meta structure/map is limited, that bounds progress regardless of intelligence.
My argument is that in the real world, we're most often bounded by incomplete maps.
AI researcher here. I think there are a couple of important points to make. First, the really interesting thing about the GPT-2/GPT-3 experiment is that it conclusively demonstrated that you can improve the performance of an AI system, as measured on a variety of downstream tasks, simply by scaling it up, without any architectural changes whatsoever. Subsequent experiments have demonstrated various scaling laws, and the latest incarnation of these models (the successors to GPT-3) are already underway. Thus, in one sense there doesn't need to be a "secret sauce" -- going from IQ 70 (human range) to IQ 300 will probably be simply a matter of throwing more hardware at the problem. Once you have AI that is sufficiently general (see below), making it smarter will be easy.
Second, the availability of hardware is an important speed brake that will prevent the "FOOM" from being too fast. AI models are currently getting bigger much more quickly than our hardware is getting faster. The first ImageNet deep learning champion was trained by a student in his dorm room in a few weeks, but state-of-the-art models now require data-center-class compute capabilities that only the big tech firms can afford. As anybody who has tried to purchase a graphics card in the past year or two knows, current fabs are maxed out, and building new fabs is an investment that takes many years. It doesn't matter how much money you have, the hardware simply can't be purchased.
I will make a prediction: we will achieve general human-level AI *before* such AI is in charge of financing and constructing new fabs, so human-built fabs will be a limiting factor.
Third, all current AIs, from Alpha-Go to GPT-3, are narrow. Given current progress, I predict that we will continue to create super-human narrow AI in an ever-increasing number of different domains. Current GPT-3-like models have almost mastered natural language. Codex-like models are on their way to mastering code. AI-powered theorem provers are formalizing math. Anything that can be created and stored digitally, e.g. text, code, images, video, music, proofs, etc. can easily be generated by a super-human narrow AI. We are not far from having AI create novels, hit albums, and blockbuster movies. This will be a major warning sign -- and unlike self-driving cars, it will not happen in a portion of the economy that is subject to government regulation.
However, there is currently no "secret sauce" that tells us how to go from a narrow AI to a general AI. The loss function (the function that is being optimized by the algorithm) is different. No matter how much you scale up GPT-3, it will not achieve consciousness; it cannot do anything other than produce a sequence of tokens. GPT-5 might write novels, but it won't have a sense of self, or any desire to launch a career as an author and sell movie rights to its work.
IMHO, "general AI" necessarily involves some form of "embodied cognition" -- it requires the AI to act as a self-aware agent, interacting with the real world, probably across multiple modalities (text/images/sound/video). That is the huge difference between chimps/humans and AI -- we evolved to survive in the real world, and we have a deep understanding of both self and physics because of that. Embodied cognition could possibly be achieved by training an AI with an appropriate loss function in a virtual world, instead of the real world, but somebody would have to link our two worlds together in order for there to actually be a "FOOM". In other words, beware the AI-powered bot that attempts to play World of Warcraft, and make lots of gold by scamming human players. Don't scale that one up.
I will conclude with one more prediction: a general AI that kills all human life is probably not the thing to fear. A narrow AI, built by bad actors, will probably cause more destruction first.
For example, imagine that Russian cyber-criminals train a narrow AI specifically to hack into other computer systems, using a combination of spear-phishing (generating text e-mails), and zero-day vulnerabilities (generating code). They further program it to self-replicate, and use any compute resources that it acquires to mine bitcoin. (Please tell me that this scenario is implausible.) If this narrow AI becomes super-human, it will quickly wipe out every computer connected to the internet. That's not an end-of-human-life FOOM, but certainly an end-of-the-global-economy.
Problem: Eloser is not credible. He believes that animals cannot suffer because the GPT-3 cannot suffer, and that anyone who disagrees on this point is an idiot. Scott has come a long way from where he started and has become far wiser and more intelligent than any rationalist. Eloser simply has not done the same. He's the same old ignoramus he's always been.
For the sake of argument, just how is this 'super AI' going to be able to do much of anything besides move bits around at first? Do we really think there is something in the laws of physics that the AI will be able to discover while in it's box that will allow the AI the machine-equivalent of telekinesis? That seems pretty implausible. So how about how much capacity is there for a computer to independently create and move custom complex multi-material physical objects remotely? None. What about energy capacity? Oh yeah, that is limited, slow to ramp up, and requires complex multi-material physical objects. At this point in time, FOOM is nonsense as the AI's speed of change is going to be capped by the slow speed of the monkeys that wrote it. If we had something like a replicator, I might actually be concerned, but that technology is a long ways off.
As you might infer, I am pretty heavily against the rapid take-off scenario because so much would have to go right (or wrong, depending on your perspective) for it to occur. One thing I don't see mentioned is that there seems to be a religious aspect to the argument for rapid takeoff where AI is spoken of as an unknowable, omniscient, and bordering-on-omnipotent being. The FOOMers seem to be trying to reimagine God.
minor objection to the choice of examples:
I think online retail is still in the fast-growth part of its sigmoid and despite being a bitcoin maximalish I don't think cryptocurrency will ever do much for tech industry profits -- the impact will be at least an order of magnitude smaller than smartphones. There isn't really any legit business use case for blockchains because all corporations are centralized and subject to the whims of governments, but the whole point of a blockchain is being decentralized and beyond the reach of governments that want to inflate the currency or block transactions between consenting adults. Without the decentralization a blockchain is just a really inefficient type of spreadsheet. Vitalik's idea of putting his warlock on a blockchain to prevent some centralized authority from nerfing it is silly because nothing that decentralized has within several orders of magnitude of the performance necessary to run an MMORPG server in real time. Plus games and shitcoins generally have an author with enough social authority to hardfork any patches they want regardless of the blockchain code.
Eliezer Yudkowsky believes himself to be a superintelligence. He is also preoccupied with power and punishment (not judging him for that - many humans are - just factual observation). He believes that an AI would be preoccupied with power and punishment because he either implicitly or explicitly assumes that his perspective is analogous to that of a future AI superintelligence. Someone above made the analogy that humans are like the Judeo-Christian God in the Biblical story of creation, working on Adam, trying not to wind up like Kronos did. It seems almost hilariously obvious to me that Eliezer and people who agree with him believe the exact opposite: that they are in the process of creating a god, and that something must be done to ensure that this god is a friendly and not a vengeful one.
I’m glad to see how many people are pointing out how frustrating it is to see well-intentioned but ultimately ignorant and misdirecting armchair speculation coming from someone with obvious intelligence but extremely limited practical experience or depth of study in the field they are most known for. You’d think this would be the default position for a group wishing to be less wrong.
While the question of "who had language" is not completely resolved and is riddled with the problem of "what is language" (compare "Diseased thinking about disease"!), my gut reaction to "Homo erectus had language" is, well... NO. Just no. There is strong debate whether Neanderthals had language (and we unfortunately know too little of Denisovians to tell); if we have to debate that, given that Neanderthals are a much closer genetic sibling, Homo erectus is probably out of the question.