alt Hacker NewsRegardless of what you think of the priority dispute issue discussed on sibling threads, I’m highly skeptical of the closing quote that this Navier Stokes result means that the same approach of casually spending a few million on agentic computation is going to solve end to end materials design or drug development.
Those problems can’t be formally verified with an automated theorem prover. We have a lot of physics based simulation tools, but they tend to focus on small subsets of the full design problem and they make limiting approximations because otherwise they’d be too computationally expensive, or we just don’t have the right data to parameterize them beyond describing qualitative behavior. Agents are helping accelerate research in these fields but I think it’s mostly a different class of problem that’s a lot harder to specify and verify
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For any practical application, numerical solvers for Navier-Stokes already exist and do a good job.
This proof is just checking the boxes for mathematicians.
>We have a lot of physics based simulation tools, but they tend to focus on small subsets of the full design problem and they make limiting approximations
Do you think it is possible that better math will lead to better physics models?
I’m pretty sure that OpenAI has some of the best mathematicians prompting the models and analysing the results. While they are marketing as if the model solves problems themselves.
Working with AI on science (not LLMs though), couldn't agree more.
> Those problems can’t be formally verified with an automated theorem prover.
It certainly seems like any problem that is amenable to reinforcement learning will be solved.
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Yeah, I think you can't just throw money randomly at problems and expect results unless you know a line of attack that can get you all the way. OpenAI chose the line of attack only after it became known to them via rumors. They "front-ran" the researchers.