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Comment by curt15 | Hacker News Reader

The amount of money they're lately ploughing into proving math theorems is inconsistent with how societies and markets have priced pure mathematics. The entire US federal budget for math research is something like $100M annually. A single college football coach can already earn 10 percent of that.

Pretty much the only enterprise that historically pays some mathematicians handsomely is quant finance, but those people are actually compensated not for proving theorems but rather for statistical modeling and programming skills. And even that industry is so technologically driven these days that pure research mathematicians no longer hold a clear edge over strong programmers with undergrad level probability and statistics at their fingertips.

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Math is one of the most verifiable domains, esp thanks to LEAN, which also build coding skills.

The $$$ they're pouring isn't just for marketing. Think of these papers/results more as "useful side effects" from large-scale RL rollouts and post-training. Every token being generated contributes to post-training in some way.

There isn't a hard boundary between "training" or "inference", modern post-training is arguably inference-bound :)

It was shown quite some time ago that training LLMs on programming tasks improves their logical reasoning skills also in other natural language domains. So I could see math also being a training gym for AI even if the final use case is not directly math-related. Having to solve math problems efficiently can build in skills that come handy in all kinds of more everyday tasks or science and engineering.

It’s also very useful signal that the reasoning trace is leading to solving open problems - you can be certain that you’re not landing somewhere inside the training data.

I think the entire thing here is that these solutions _are_ inherently interpolations of existing work in the field. That's the "super power" that LLMs have. To interpolate mass amounts of multi-dimensional data.

This depends on some handwavy use of the term "interpolation", not the mathematical definition. Mathematically interpolation usually means that the query point is in the convex hull of the data points, and that almost never happens in high-dimensional spaces.

See: Learning in High Dimension Always Amounts to Extrapolation Randall Balestriero, Jerome Pesenti, Yann LeCun https://arxiv.org/abs/2110.09485

I guess you mean by "interpolation" that it's some kind of nonlinear combination of the training data, but that's an almost vacuous statement. Any input-output relationship has to be so by definition.

Or perhaps you mean that interpolation is when the test input comes from the same distribution as the training input (though this is not technically the meaning of "interpolation"). But this is also quite difficult to pin down.

> There isn't a hard boundary between "training" or "inference", modern post-training is arguably inference-bound :)

Ah this is an enlightening point. 8 hadn't thought about it this way, but you're right.

  Don't hire a straight-A student, unless it's to take exams; or a professor, unless it's to write papers.
 -- Nassim Taleb

How interesting that Anthropic and OpenAI are full of professors and straight-A students!

It’s unclear to me what point you’re making here - can you elaborate?

Solving test questions well doesn’t necessarily translate into productive outcomes in the real world.

So education is useless?

No, more like the average can be more important than some outlier's impact, at least for steady progress. So while you do need the Mozart and Einstein, a more productive effort would be to raise the general population's education level.

Humans learn in the side effect of education, nobody is busting out some poor method that schools teach to solve antiquated problems in the real world.

Neither does being unable to solve test questions. That's probably even worse.

If you track the best students futures and look the best workers pasts, there is not nearly as much overlap as society generally believes.

A lot of people have been surprised by the interest that AI companies have in solving maths problems without obvious applications, as well as in the financially precarious state of these companies. I imagine that they thought that these companies were helmed by mere bean-counters, like at Boeing. But no, I reckon that they're still academics at heart, and so they work on Navier-Stokes and L=BPL, instead of on how to maximise value for their (soon to be) shareholders.

Don't quote Nassim Taleb, unless it's to be an arrogant dick.

-- Me

-- Michael Scott

imbecile!

As long as they continue making headlines they will continue spending. This is just marketing at this point.

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