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Comment by omnicognate | Hacker News Reader

> is doing math at the level of the best mathematics

Terence Tao a few days ago [1]:

> When we point the AIs at really difficult problems where none of the standard techniques apply, they are still very, very bad, I mean they're just randomly guessing.

Please don't reply saying they'll be proving the Collatz conjecture next week. It adds nothing.

[1] https://youtu.be/svl_1upFpQo (worth watching on the role of LLMs in maths)

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The best mathematicians are also not very good at solving the hardest problems though. By definition.

Aren't the best mathematicians the best at solving the hardest problems? I don't see how you can argue this without assuming an arbitrary scale for being "good" at something.

It can be simultaneously true that a group of not very good at something and that that group is the best at doing something.

As I said, I don't think so as long as you derive your scale of difficulty from the domain itself.

I recognize you are probably just trying to win an argument, but what actually matters is that the ecosystem of human mathematics has produced strong results and can continue or not continue to do so depending on how we adapt to this new technology.

Let's see you solve some open problems before you dismiss LLM capabilities. They're working at a level far beyond all but the best of the best.

And we're only a couple of years in. Their capabilities aren't going to be getting worse over time. These kids will be hitting the workforce after AI has another decade of improvement put into it.

I quoted Terence Tao. Are you suggesting he hasn't solved any open problems?

Sure, if these kids can all perform at Tao's level, they could likely be able to keep up with the AI capabilities from a decade before they would enter the workforce.

I was clear on what I was responding to, and quoted it: "is doing math at the level of the best mathematics". This is misinformation and a public perception that will harm maths as it continues to spread. For some actual insight on the relationship between human and LLM maths (including on why thinking of it in terms of "levels" is misguided), see the video I linked.

Your broader point is based on a future extrapolation of LLM capabilities that is far from guaranteed to play out, so it sounds like poor risk management to me, even accepting your framing of it purely in terms of value in the workforce (which I don't).

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