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georgemcbay | on: Mathematics has changed. Let's act like it
Sure, but given the approximate tens of millions of dollars in API costs of solving these major math problems currently, we will also have to acknowledge that getting credit for major mathematical contributions is likely now to be far less about meritocracy and far more about access to capital to kickstart the original proof.

Less capable (but good enough) mathematicians with more capital will have a huge advantage over great mathematicians who are broke.

I'm not a mathematician, but that situation rubs everyone (except those with the most capital) the wrong way.

JacobAsmuth | parent | next
Tens of millions? You're thinking specifically of the proof of Navier-Stokes. According to https://github.com/openai/math/blob/main/README.md:

"On average, each result used three hours of ChatGPT Pro thinking compute with that model. Over the course of the evaluation, the model was posed approximately 4,000 problems. Aggregating the output into result families and manuscripts and requiring an appropriate level of significance led to the catalog outlined above."

I will note that this includes the Quasi Riemann Hypothesis. The model that solved NS was significantly dumber and less efficient that OpenAIs more recent math-models and you shouldn't over index on "it's expensive now". AI has advanced so quickly that your <2 month old opinion is out of date by 5+ orders of magnitude!

Paracompact | root | parent
Question: Have you actually used AI to do research-level math? At the current time, it is not "tackle any problem you wish and you can get headline results in ~3 hours." Their method is spraying-and-praying across the entire mathematical frontier, which is something only AI demonstrators are interested in. And as far as I know, they have never elaborated on what their "average of 3 hours" metric even means (e.g. including or excluding all unsuccessful leads?).
nradov | root | parent | next
I suspect the AI frontier labs will also soon turn their tools loose on the entire corpus of historical math papers to check for errors. They'll likely find that some major areas of math are built on a very shaky foundation.
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neerajsi | parent | next
This is true in experimental sciences, where people who have grants and funding can obtain results using equipment unavailable to others. But their raw data can help much less funded theory groups to formulate new understanding that can lead to new experiments.

As long as the raw proofs, potentially ai generated, are available a lot of less funded mathematicians can still use them to generate understanding and created new programs to be resolved with new ai aided mathematical experiments.

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danielmarkbruce | parent | next
I never had a chance against Terence Tao. Neither did you. There are a few dozen people on the planet who had a giant advantage in math and could do things no one else could do.

It was not some great awesome fair thing that math was dominated by some outrageous outliers on the iq spectrum. It's not some great awesome fair thing now either.

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asgraham | parent | next
This has unfortunately been the case in experimental academia forever.
Animats | parent | next
For now. It once took Deep Blue hardware to beat Kasparov. Now most laptops can do it.
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