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After OpenAI has admitted AI safety into the mainstream, AI safetyists have naturally accepted the invitation.
The Future of Life Institute has published an open letter calling to pause «Giant AI experiments». (Archive).Their arguments are what one should expect by this point. Their prescriptions are as follows:
Do we control our civilization? Maybe the folks at FHI do, I sure don't. Well, anyway…
Signatories (over 1000 in total) include Elon Musk, Steve Wozniak, Yuval Noah Harari, Yoshua Bengio, Connor Leahy, Stuart Russell, Andrew Yang, Emad Mostaque, Max Tegmark, Gary Marcus, Steve Omohundro, Matt Mahoney, Christof Koch,
Sam Altman*, LessWrong disciples embedded in DeepMind/Meta, and various NGO/«policy» suits. Bolded are people who are reasonably well positioned and incentivized to, in fact, organize and authorize training «AI systems more powerful than GPT-4» in then next few months, though except Altman they all only barely qualify; actual GPT-5 is believed to already be in training and is, or was, planned to come out in late 2023.Curiously absent – for now – are Yann LeCun, Jeff Dean, Demis Hassabis and John Carmack, and a few more. LeCun, at least, commits to not sign. Here's to hoping he won't find a horse's head in his sheets or something.
I do not have much of a comment at the moment. My perspective is that I despise people overly concerned with «Moloch» and want as many competitive superhuman AIs as possible, so on one hand, slowing down and enabling the state to catch up and subjugate this tech for its purposes is a very bad, yet highly expected and perhaps inevitable, outcome of this race. This attitude is born out of desperation; in principle, their «AI Summer» option, where we increase capabilities over many years, getting the equivalent of 20th century civilizational shift in a decade instead of an explosive singularity, is not bad at all; I just don't believe in it.
On the other: seeing as nobody is closer to GPT-5 than OpenAI themselves (excepting DeepMind with Gato-2 or something better, as Gwern worries), it could be beneficial for our long-term outcomes to equalize the board somewhat, giving China more of a chance too. Geopolitics dictates that this should preclude the possibility of this policy being pursued in earnest, but really China is so colossally outmatched in AI, so well and truly fucked by technological restrictions, and mired in such problems and gratuitous stupidity of its own policymakers, it may not be a factor in either case.
I must go, so that's all from me; hopefully this is enough to pass the «effort» bar required by the mods and prompt some discussion.
In happier news, arguably the most powerful opensource chatbot today is LLaMA-7B with a transfusion of ChatGPT 3.5-Turbo quirks, (not very) creatively called GPT4all. It's far beyond basic Alpaca (already an attempt to extract OpenAI's magic) and absurdly good for what it is, a 4.21 Gb file of lossily compressed 7 billion weights trained… well, the way it's been trained, the AI equivalent of a movie camrip superimposed on the general web dump; the worst part of it is that it genuinely apes ChatGPT's politics and RLHF-d sanctimonious «personality» despite being 25 times smaller and probably 10 times dumber. It runs happily on very modest computers, and – unlike Alpaca – not only responds to instructions but maintains awareness of earlier parts in the dialogue (though it's sometimes overeager to say your part as well). I know that models vastly stronger than that should also be usable on commodity hardware and must be made available to commoners, but we may see regulation making it not so, and very quickly.
Consider the attached image representative of its mindset.
* (EDIT: I believe I found him there with ctrlF when first opened the page, but he's not present in any extant version; guess it was a hallucination. I really need to sleep, these slip-ups are worrying).
/images/16800616737543523.webp
Is there anyone here who 1) thinks that AI x-risk is a threat that should be taken seriously, and 2) also thinks that this letter is a bad idea? If so, can you explain your reasoning? And also explain what restrictions on AI development you would support?
For a group of people who are allegedly very concerned with the possibility that AI will soon wipe out humanity, Rationalists are suspiciously resistant to any proposals for actually slowing and regulating AI development. A lot of the comments on this letter on LW and /r/ssc are very critical. If your stance is "I wish we could slow AI development and I support the letter in spirit, but I think it's unlikely to work", then that's one thing. But the critical comments seem to suggest that the comment authors either don't support any AI regulation at all, or else they're engaged in motivated reasoning to try to convince themselves that it's not even worth trying (e.g. "this letter will have a net negative impact due to its effect on capabilities researchers who don't like it" - the good ol' "if you fight your enemies, they win" tactic for concern trolling).
It lends support to my intuition that most AI x-riskers don't actually take the idea of x-risk very seriously, and on a gut level they think the benefits of AI are so likely to outweigh the downsides that there's no issue with pushing full steam ahead on capabilities research.
(Obviously if you're a full on utopian optimist and you consciously affirm that x-risk is not a serious threat, then there is no contradiction in your position and none of this applies to you.)
I think there's a possibility that we're not yet to "hardware overhang", that alignment research will only progress if it has high-capabilities test models to work on, and that we're going to reach "hardware overhang" in the next few decades. If all three premises are true, then going full-speed-ahead to AGI now, when the closest it could get to "going foom" would involve directing the construction of new chip fabs from scratch, would be the only chance of success at alignment research. Slowing down now would just mean that, once someone eventually breaks the pact and hits AGI, it will already be superhuman and inhumanly dangerous.
I think "we're not yet to hardware overhang" is the weakest of those premises, though. I'm not even sure we could have avoided reaching hardware overhang via the techniques we're using; if you've got enough compute to train a model via massive brute force, you've necessarily got more than enough compute to run the model at superhuman speeds. The limiting factor right now is that we don't know how to make a general model superhuman; we can go into a "self-play" phase when training a Chess or Go AI and watch it take off, but there's no "self-play" for reality.
You're probably right. The technique of using the model's own outputs, and its assessment of those outputs, to further tune itself probably caps out at a fairly small amount of gain, if you don't ground the model in further interactions with the physical world.
Oh, yeah; I'd expect that sort of "self-play" to get peak model performance from "average human responses" to "best-human responses recognizable by an average human". And the economic effects of getting there, even if it caps out there, might be astounding. But frankly if we just end up having to retool 90% of the economy I'd be glad to see it, when the alternatives include extinction-level scenarios.
I think the most "real" thing I can imagine high-speed self-play for is math (theorem proving). Adding an inference step to a computer-verifiable theorem seems like it's as basic an output as choosing a move in a board game, and coming up with "interesting theorems" seems like it could be done automatically (some function combining "how short is it to state" with "how long is the shortest proof"?), and yet an AI training to play that "game" might eventually come up with something more useful than a high ELO.
Pretty sure self-play would be viable for leetcode-style or project-euler-style programming problems too, if you give it access to an interpreter. Or just any task where recognizing a good output can by done by a less capable language model than it takes to generate an output that good.
Recognizing good output is half the problem; generating an enormous array of problems is important too. With complex board games every non-deterministic opponent (including every iteration of an AI during training) is a fount of problems; with math the new problems practically generate themselves as conjectures based on previous problems' intermediate definitions. I don't see how to come up with millions of independent programming problems automatically. That might just be a failure of my imagination (or more charitably, I just haven't thought about the idea for long enough), though.
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