131–140 of 231 posts
I'm asking this question out of ignorance: if you were able to do this, why would you make it public for $10MM instead of keeping it private and exploiting it. Say, in algorithmic trading models?
I see no reason to believe such a machine would be helpful for algorithmic trading. Why should it? How well do current gold medalists do in trading?
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Well math solving is exactly what the rumored Q* is aiming towards too. I don't think it'll take more than 2 years before some LLM + RL system can take the gold medal. I think companies like OpenAI are aiming for something far more ambitious, like solving a millennium prize problem (even with human assistance). That's the kind of news release that'll add another $100 billion to your market cap.
I have a couple friends who did the Math tripos at Cambridge (so a pretty high level!) who work in tech and have unanimously said they have 0% expectations of an LLM doing a millennium problem anytime soon
That's a tough thing for AI to do.
On the other hand, Terrence Tao had an interesting article on his blog a while back where he was trying to solve a problem and asked chatGPT about it in a high-level strategy sense. ChatGPT suggested several reasonable approaches, one of which turned out to work.
That's nowhere near solving a millennium problem, but it is very interesting and suggests fairly sophisticated conceptual understanding of mathematics nevertheless.
Current architecture and training methods I don't think are enough to get there. However, with enough compute, I can plausibly envision some sort of meta training of LLMs using an analogy to GANs where one network tries to synthesize new correct ideas and the other shoots them down as not novel, not correct, or not sufficiently interesting.
Such an approach I think could perhaps work, but the compute needed would probably be pretty high.
I was recently in Palo Alto, and bumped into a newly founded startup (I don't remember the name unfortunately) who set themselves the grand the vision of exactly this: winning a gold medal on the international Olympiad using AI. Their plan was to build mostly on LLMs as a start, and iterate as they go. In their barebones office space, they had a poster with a countdown of the number of weeks till the event: it was 36…
I wish there was a serious study of Dunning-Kruger effect in Silicon Valley.
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Just to nitpick, while unlikely, it is theoretically possible that the second player has a winning strategy
I would assume this is possible for any sufficiently complex game. Would you mind answering a few questions from someone near-completely ignorant about Go? Does second mover in Go have some sort of artificial benefit in scoring or playing? As in -- is there something to compensate for moving second? On the face it seems like first mover would have an advantage in any turn-based game. But maybe, in some games, seeing…
Yes. The second player typically gets an extra 6.5 or 7.5 points.
Earlier quoted context omitted.
Well math solving is exactly what the rumored Q* is aiming towards too. I don't think it'll take more than 2 years before some LLM + RL system can take the gold medal. I think companies like OpenAI are aiming for something far more ambitious, like solving a millennium prize problem (even with human assistance). That's the kind of news release that'll add another $100 billion to your market cap.
I have a couple friends who did the Math tripos at Cambridge (so a pretty high level!) who work in tech and have unanimously said they have 0% expectations of an LLM doing a millennium problem anytime soon
Earlier quoted context omitted.
I have a couple friends who did the Math tripos at Cambridge (so a pretty high level!) who work in tech and have unanimously said they have 0% expectations of an LLM doing a millennium problem anytime soon
Yeah, millennium problems almost certainly require truly novel nontrivial ideas to solve. That's a tough thing for AI to do. On the other hand, Terrence Tao had an interesting article on his blog a while back where he was trying to solve a problem and asked chatGPT about it in a high-level strategy sense. ChatGPT suggested several reasonable approaches, one of which turned out to work. That's nowhere near solving a m…
Earlier quoted context omitted.
I have a couple friends who did the Math tripos at Cambridge (so a pretty high level!) who work in tech and have unanimously said they have 0% expectations of an LLM doing a millennium problem anytime soon
Yeah, millennium problems almost certainly require truly novel nontrivial ideas to solve. That's a tough thing for AI to do. On the other hand, Terrence Tao had an interesting article on his blog a while back where he was trying to solve a problem and asked chatGPT about it in a high-level strategy sense. ChatGPT suggested several reasonable approaches, one of which turned out to work. That's nowhere near solving a m…
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Their current ambition is to be able to solve school math, which is quite far away from solving unsolved conjectures or math olympiads. I really doubt that any of this is within LLM/transformer scope, except maybe in some auxiliary sense to other, much different architectures.
Art isn't an easier problem than math. An artbot would have sounded more sci-fi than a mathbot only 2 years ago. Yet it only took the AI world 1.5 years to go from drawing child scribbles to replicating top artists with like 90% similarity (I can barely tell the difference between AI and human drawn art anymore with the new NovelAI model). It won't be long before AI starts to go superhuman in art skills. It won't tak…
Someone can tell you when a math problem is solved. Someone else can't tell you when you've successfully art'd with remotely the same degree of confidence. In so far as they can, however, many experts claim that AI cannot, indeed "do an art".
Also, it's easier to formulate a hard math question (there are plenty of unsolved problems), but that's (IMHO) harder to do for art. Sure, you may think this is the first time the phrase "Astronaut riding a Llama and holding an avocado" was writ, but those are all well represented concepts in the dataset. For more abstract prompts, there really isn't a way to verify "correctness".
I was recently in Palo Alto, and bumped into a newly founded startup (I don't remember the name unfortunately) who set themselves the grand the vision of exactly this: winning a gold medal on the international Olympiad using AI. Their plan was to build mostly on LLMs as a start, and iterate as they go. In their barebones office space, they had a poster with a countdown of the number of weeks till the event: it was 36…
I wish there was a serious study of Dunning-Kruger effect in Silicon Valley.
1. https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...
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I have a couple friends who did the Math tripos at Cambridge (so a pretty high level!) who work in tech and have unanimously said they have 0% expectations of an LLM doing a millennium problem anytime soon
The field of LLM reasoning is far from stable atm. I think it's pretty hard for anyone to give confident predictions about what they can or cannot do in 5 years. In that light, I am skeptical about any claims that they cannot do something anytime soon.