Comment by solenoid0937
3 hours ago
Give it a verification loop and enough compute, and AI will soon cook algorithmic R&D like it cooked mathematics. There is just no question whatsoever of this happening, it is guaranteed.
3 hours ago
Give it a verification loop and enough compute, and AI will soon cook algorithmic R&D like it cooked mathematics. There is just no question whatsoever of this happening, it is guaranteed.
Theoretical fields like mathematics inherently suffer from a form of Goodhart's Law. The progress in mathematics you hear of is almost never the kind of progress non-mathematicians would care about.
It's always easier to measure progress according to the internal metrics of the field than to evaluate the contributions to the wider understanding of the topic. In theoretical computer science (which I'm most familiar with), people have long complained about results focusing on shaving sublogarithmic factors from complexity bounds (while making the algorithm worse in practice) and about reviewers being impressed by the technical difficulty of proofs. But progress like that is easier to measure than new algorithmic ideas or conceptual understanding.
But it's not all bad. The researchers chasing the metrics are almost always genuinely interested in the topics they study. Their actual contributions mostly come from the ideas they explore while trying to achieve measurable progress. And because they are not expected to produce anything of direct value (Goodhart's Law for applied researchers), they can explore a wider range of ideas.
I personally noticed this when I moved from theoretical computer science to algorithmic bioinformatics. When I start a new project, the expectation is that researchers in genomics should be using sofware that uses the new algorithms five year from now. That expectation is useful, but it's also a strict constraint on what I can afford to try.
> it cooked mathematics
Weird, these are all still here? https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_m...
Several of the items on that list are claimed to have been resolved by the recent OpenAI theorem-dump. Another is the Navier-Stokes question that's one of the Millennium Prize problems, also claimed to have been resolved by OpenAI's models.
So: unless all those claims by OpenAI turn out to have been mistakes[1]: no, actually, those are not all still there.
[1] It's certainly possible that some will. They've already retracted a few things.
There is already a pre-print making the claim that the Navier-Stokes solution provided by OpenAI does not meet the criteria required by the prize (https://arxiv.org/html/2609.20803v1)
(and as noted on Wikipedia, the Clay institute still lists the problem as 'active' not solved https://www.claymath.org/millennium/navier-stokes-equation/)
It will cook only as long as brute force through search space is cheap and economically viable.
Have you seen my draft? For easy task I may write the straighforward solution, but for complicated stuff it's a mix of throwing stuff to the wall and see what sticks. It's informed search by experience, but AI also use weights to pick the attempts.
AI is not a calculator. OpenAI did not solve hundreds of open problems in math by "brute forcing the search space," there was a lot of intelligent and novel thought (gasp!) in the way AI chose the paths it did.
Yes, AI can explore thousands of ideas at once, no, that's not "brute forcing the search space" because the search space is way larger than you think it is.
> ...and economically viable.
And there are so many indicators that it's not currently economically viable and will only be if retail price is massively increased and strong regulatory barriers to competitors are erected. ;)
Very interesting if it's not currently economically viable. Do have any sources on OpenAI's margins?