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The open ai math dump was actually what got me off the singularity wagon.

There were mistakes that could easily be seen with a diagram rendering three big results wrong, exposition is terrible, no coherent underlying theory generated from solving so many problems, as Thomas Bloom pointed out even duplicate proofs of the same statement in a single paper.

It clicked for me, "this is slop but slop can be superhuman and useful!". Similar to programming where it can generate as many criticisms of your codebase but you have to weed them out.

by QuesnayJr8 hours ago|

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If anything, that makes the case that math is fucked even stronger. Nobody had any idea of how to prove quasi-Riemann. OpenAI produced three proofs, one of which was just a lemma in solving another problem. They have a machine that is better than every human being alive at math that solves problems humans cannot even when working inefficiently.

by vatsachak8 hours ago|

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Math was never only about deduction. It's also about data curation.

I was taking to my friend in OA who was really surprised that the proof of L(F_2) = L(F_3) could be done in the 1980s. We just didn't have a superhuman deducer back then.

by dist-epoch8 hours ago|

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All scientific break-troughs are obvious in retrospect.

Special relativity which Einstein got a Nobel for is now taught in high-school.

"Of course if you move a magnet in a piece of copper you will generate a current, it's extremely obvious it should be so"

by vatsachak7 hours ago|

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That's true. I'm not saying the work is trivial, I'm calling out the difference between "deduction from known data" and "distilling known data"