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I was referring to the overall pattern of apparently sniffing around for recent mathematical progress then setting the AI on it to see if the problem is now easy enough to solve (if you have the money).

Terrance Tao has lamented this practice as being unhelpful for mathematics, and likely to lead to humans working in private to avoid this.

Tao has also noted that many of these AI math proofs don't really help mathematics (nor does it seem they are intended to), since for many of them the proof was never the point, it was the math expected to be needed to be developed along the way, which the AI solutions don't provide.

by bwfan1239 hours ago|

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> has lamented this practice as being unhelpful for mathematics

A related point is that the actual solution approach is never revealed. What was the role of humans guiding the agents ? was it fully autonomous ? etc. It is in the incentive of the AI labs to trump the powers of the LLM, but in practice it is humans guiding the agents on the overall approach, This is never admitted. For example, in the announcement on NS there was only an output artifact given but no indication of how it was arrived at, and not even a writeup. This is what disappointed many folks as it was done purely for one-upmanship. As other have noted, the benefit is in the journey or process and not in arriving magically at a destination.

by cma7 hours ago|

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I don't think it's as bad as that sounds; in math people work all the time with conjectures they aren't sure if true, and work out a lot of other interesting math based on whether it is or not. Something like Turing's Oracle machine gives lots of interesting math just assuming one could exist, even if it couldn't. It may be that there are things proved we can never come to a human understanding of, but still keep getting interesting math that relies on it that has aspects we can appreciate and enrich our knowledge from.