I’m generally a bit of an LLM skeptic, at least as concerns extravagant claims around “AGI” or self-improvement of any of that nonsense.
But there are activities where an inherently high-temperature assistant (e.g. some Instruct-inspired tune) are a great fit, and those are almost definitionally at the boundary of “objectively faithful” and “a bit stochastic”.
I’ve never done any work in novel mathematics myself, but I’m a big fan of those who do, and any study of those who do paints a picture of roughly that boundary: those who are proficient can easily spot a falsehood but only with difficulty generate inspiration for a new idea.
Of all the highly optimistic applications for 2024 LLMs, mathematics sounds pretty plausible?
I’m generally a bit of an LLM skeptic, at least as concerns extravagant claims around “AGI” or self-improvement of any of that nonsense.
Of all the highly optimistic applications for 2024 LLMs, mathematics sounds pretty plausible?
You probably already know this, but I think it bears pointing out explicitly: in this context we're not talking (only) about LLM's. There's a LOT more to AI than just Large Language Models, and the list of resources linked reflects that.
FWIW as a former mathematician, I believe that if LLM’s can crack mathematics in the sense of say, solving the Riemann Hypothesis through chains of logical reasoning, then we’d be in the back half of reaching “AGI” in the sense of solving broad classes of general problems like drug discovery, or getting fusion to work, or even improving itself.
Oh sure, I think that’s pretty uncontroversial. An LLM that can with any consistency start at “prove or disprove P = NP” and arrive at a proof is in some sense super-intelligence.
Even the best funded boosters are making no such claims AFAIK, I think a much more reasonable and sober assertion might be like “they are useful to doing real mathematics”.
The curious thing is if you can ever hit a snowball type point, because by proving theorems you are generating training data, and maybe you can get to a point where you effectively have limitless data.
> Science needs experiments and those will not go faster because of AI.
Even though I am deeply skeptical whether AIs (as they are commonly understood today) will be helpful for hard scientific problems (for quite different reasons), I don't think this argument necessarily holds: couldn't a (hypothetical!) AI propose much better experiments and experimental designs?
That‘s not how science has progressed in the past. It has not progressed by giant leaps of superior intelligence but incrementally and hardly a genius was more than 10 years ahead of his time.
Yeah I did say “back half”. I just think that if we can build an AI that can logically reason better than any human, combined with the total knowledge of all humans, it can start running experiments and doing “science” itself.
The word “if” in both the parent and GP is doing a lot of lifting here though, if they can do amazing feat X, then they will likewise do amazing feat Y. And I think in every instance you’ve made a credible argument.
But I don’t think we have the if yet. If I could just slip GPT-4 an executive summary of the code I meant to write and the emails I meant to send on a given day I would be doing it.
But it can’t: it’s catastrophically wrong with extreme confidence routinely. These things are easy to cherry-pick in citation but still fuck up on HellaSwag in instances a child would not.
Oh, I 100% agree we are not there. Every time a new model comes out I cook up a few undergrad math homework questions to see if they’ve gotten any better, and so far they haven’t. But, the thing I like about “reducing” AGI to solving math is that it kind of sidesteps these thorny questions about interacting with the real world to conduct experiments, etc. It’s something that, in principle, can be done with the current text interfaces, and it’s pretty easy to tell whether or not we’ve gotten there.
The impact extends beyond mathematics. We have seen some pretty positive outcomes for LLMs as an assistant for scientists - with a lot of custom agent dev though - and not off-the-shelf models.
This is already being used by a few hundred scientists in a lab. We are aiming to extend to thousands next year with a focus on CS, climate & bio.
Thanks! Most of the current work is not yet out as press or blogs - we generally do that post code release/ publications/ arXiv submissions. Will share them as they are made public.
MIT, the Steel Factory, Cal, UDub and what, 37 authors pushed a preprint and no one heard about it?
@dang I can’t be the only one who has had it on this GPT-5 test flight shit. I don’t care how many clean Azure IPv4 blocks Altman has, throw this motherfucker out.
But there are activities where an inherently high-temperature assistant (e.g. some Instruct-inspired tune) are a great fit, and those are almost definitionally at the boundary of “objectively faithful” and “a bit stochastic”.
I’ve never done any work in novel mathematics myself, but I’m a big fan of those who do, and any study of those who do paints a picture of roughly that boundary: those who are proficient can easily spot a falsehood but only with difficulty generate inspiration for a new idea.
Of all the highly optimistic applications for 2024 LLMs, mathematics sounds pretty plausible?