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| ▲ | pickleRick243 5 hours ago | parent | next [-] | | Actual scientists are not interested in using these tools to their full capability because of a fundamental psychological block where they have the need to feel superior to the LLM. The point is that AI labs have a thesis that AI will supersede humans in all cognitive work, and since few scientists have this view (or have the temperament to even contemplate this view), they have to do it themselves. In any case, they are attracting strong/talented biologists (Dario himself has a biology background) to work on this and provide their expertise, but presumably they filtered for scientists who are in fact "AI-pilled". Look at what's happening in the math community- rather than excitedly embracing the power of AI's ability to generate new proofs, they are screaming for it to slow down (and quite a few want it simply stopped). Many scientists, even in ostensibly more real-world fields are broadly cut from the same cloth. | | |
| ▲ | HarHarVeryFunny 3 hours ago | parent [-] | | There may be a few scientists rejecting use of AI for that reason, but I would expect them to be a tiny minority, just as I would expect the number of developers who accept that AI is a powerful coding tool, but refuse to use it just because they want to feel superior, is very small! A much more practical reason we're not yet seeing a lot of headline scientific mathematical breakthroughs is just that it is ungodly expensive! e.g. The Navier-Stokes result cost around $20M at API prices, and academics just don't have that kind of money to spend. You'll see more mathematical and scientific results from practitioners when either the cost of compute needed for these sort of brute force results is more in line with the size of academic grants, and/or the AI companies donate more compute to the scientific community. There are different reactions from different mathematicians of course - Terrance Tao vs Cedric Villani, and no doubt a lot of shock at the speed of advance, but it seems the reasoned complaint why they don't want the AI companies themselves working on these problems is because the outcome is not the same - you get a result that in of itself may have been suspected or useless (Navier Stokes), but no write up of any new math or insights that were developed along the way, which is the real reason mathematics and people like Erdos pushed these famous problems in the first place - because they were expected to yield interesting mathematics, just as years of work on FLT had done. Imagine if instead of Wiles's work, and all that had gone before him, all we had was a $20M compute bill, hundreds of pages of impenetrable math, and a billion lines of Lean proving it was true?! |
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| ▲ | zhaoziyuan2000 7 hours ago | parent | prev [-] | | It seems that in a frantic pace to prove general usefulness of these LLMs the AI companies are also picking up the role of traditional scientific researchers without really have enough proper communication with the scientific community at large... I agree putting tools in the actual scientists will be very helpful but do they have the patience waiting or even parsing their feedback? | | |
| ▲ | HarHarVeryFunny 6 hours ago | parent [-] | | Sure - the AI companies want some quick trophy kills to feature in their IPO prospectus, but they are not going to themselves be cracking the genuinely tough problems. There is a difference between what's easy/hard for a human vs computer, and LLMs haven't changed that. You might expect a computer to be good at tasks requiring prodigious memory and compute, and it turns out that some of these long-standing math problems are of that nature - not requiring new breakthroughs but rather just massive exploration of what is already known and what they were trained on. There will no doubt be more math results like this, but presumably also ones that are "hard for a human, easy for a computer", requiring massive search (e.g. find an example/counter-example cf Navier-Stokes & Jacobian conjecture) rather than creativity. |
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