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> You just described most of the parts of the mathematical community Just as science (as in the scientific method) isn't the same as Science, the community soc

> You just described most of the parts of the mathematical community Just as science (as in the scientific method) isn't the same as Science, the community social practice and logistics and tenure rules and committee compositions and journal page limits etc, mathematics is not the same as the academic mathematics community. They are not the sole producers of mathematics and not the sole users. The funding is largely in the hopes of the usefulness of the resulting math. Not all. Some funding is like art and culture funding, for propagating a cultural legacy, like folk dance also gets funding and experimental performance theater also gets some tax funding. But that's not the bulk currently in math. I have read the letter. It's all about stuff of holding back because they don't want the answer key because prestige reasons, and how will grad students train their brain if we have too many answers. This doesn't consider that many people, like engineers, use math as a tool. They don't want to hold math back. It's fine to think that applications are too dirty. Again, they want funding, they have to explain why exactly they should get it. And it better be an explanation that still holds water with AI available. They can do the aesthetic math as an art side project like an accountant can paint or sing off the clock. But generally you also don't pay everyone for their hobbies even if those hobbies are nice culturally rich endeavors.

gjulianm 4d ago
> I have read the letter. And you have not understood it. > they don't want the answer key because prestige reasons No, that’s not the reason. They don’t want the answer key because the answer key is useless. > This doesn't consider that many people, like engineers, use math as a tool Of course it considers it. Many people, like engineers, could not care less about the answers to most problems mathematicians work on. They do care about the tools they develop in the process. I’ve already shown examples of this but I’ll do it again: Galois theory was developed when trying to answer whether there are formulas to solve roots of polynomials of degree 5. Fourier analysis was developed when trying to find an analytical solution to the heat equation. Riemann developed his geometry to explore which Euclidean axioms were actually important. None of the direct answers were as important as how they got to them. > Again, they want funding, they have to explain why exactly they should get it. And it better be an explanation that still holds water with AI available. The explanation will be exactly the same. Only it will not be just hard to explain why the problem is important, but also it will be harder to explain that no, just the answer by itself is not useful without the understanding. Just like I am here having a really hard time explaining to someone who doesn’t understand how mathematical research works why “just getting the answer” is not a useful output.
- bonoboTP 3d ago
  This hangs on the strange assumption that the AI will "just give the final answer" and will be unable to give a human-preferred explanation. I find this assumption strange. If it holds, then mathematicians will still have a job digesting these proofs. If the AI can digest it better, then academic mathematicians will become humanities professors funded similarly to theology, philosophy and dance professors etc. for the sake of preserving a cultural practice, like traditional handmade broom makers etc.
  - gjulianm 3d ago
    Why is it strange? Are you familiar with how unsupervised proofs are made? It’s basically iterating on Lean code, even if it were able to explain the code to humans it doesn’t mean it can extract actual understanding and structure from it. The papers being outputted by these approaches are nowhere close to being understandable, much less useful. > If the AI can digest it better, then academic mathematicians will become humanities professors Cool, then once that happens we can discuss what to do. In the meantime, AI does not digest proofs properly, does not explain them and does not generate any understanding, and it doesn’t look like that’s going to change. Hence the letter and the criticism made to the approach taken by AI labs. It is not that hard to understand.
    - bonoboTP 3d ago
      I find these discussions pointless. It's like complaining that diffusion models make hands with the wrong finger counts. Yes there was a year or a few months when that was a legit complaint. The phase where AI proves Millennium Prize level problems but can't write up a human-like paper explaining the proof strategy may be similarly brief. It feels pointless to build a grand theory of what AI can fundamentally do in the space of math proofs based on a few months of existence of such strength models. I find it interesting how many people are incapable of imagining that the tech capability will not be frozen at today's level and where we were e.g. a year ago and that a similar change may happen until next year. Instead they make sweeping assumptions that the current limitations will be with us for our lifetimes. You need a much stronger way to adapt to the new reality.

gjulianm 4d ago

> I have read the letter. And you have not understood it. > they don't want the answer key because prestige reasons No, that’s not the reason. They don’t want the answer key because the answer key is useless. > This doesn't consider that many people, like engineers, use math as a tool Of course it considers it. Many people, like engineers, could not care less about the answers to most problems mathematicians work on. They do care about the tools they develop in the process. I’ve already shown examples of this but I’ll do it again: Galois theory was developed when trying to answer whether there are formulas to solve roots of polynomials of degree 5. Fourier analysis was developed when trying to find an analytical solution to the heat equation. Riemann developed his geometry to explore which Euclidean axioms were actually important. None of the direct answers were as important as how they got to them. > Again, they want funding, they have to explain why exactly they should get it. And it better be an explanation that still holds water with AI available. The explanation will be exactly the same. Only it will not be just hard to explain why the problem is important, but also it will be harder to explain that no, just the answer by itself is not useful without the understanding. Just like I am here having a really hard time explaining to someone who doesn’t understand how mathematical research works why “just getting the answer” is not a useful output.

bonoboTP 3d ago
This hangs on the strange assumption that the AI will "just give the final answer" and will be unable to give a human-preferred explanation. I find this assumption strange. If it holds, then mathematicians will still have a job digesting these proofs. If the AI can digest it better, then academic mathematicians will become humanities professors funded similarly to theology, philosophy and dance professors etc. for the sake of preserving a cultural practice, like traditional handmade broom makers etc.
- gjulianm 3d ago
  Why is it strange? Are you familiar with how unsupervised proofs are made? It’s basically iterating on Lean code, even if it were able to explain the code to humans it doesn’t mean it can extract actual understanding and structure from it. The papers being outputted by these approaches are nowhere close to being understandable, much less useful. > If the AI can digest it better, then academic mathematicians will become humanities professors Cool, then once that happens we can discuss what to do. In the meantime, AI does not digest proofs properly, does not explain them and does not generate any understanding, and it doesn’t look like that’s going to change. Hence the letter and the criticism made to the approach taken by AI labs. It is not that hard to understand.
  - bonoboTP 3d ago
    I find these discussions pointless. It's like complaining that diffusion models make hands with the wrong finger counts. Yes there was a year or a few months when that was a legit complaint. The phase where AI proves Millennium Prize level problems but can't write up a human-like paper explaining the proof strategy may be similarly brief. It feels pointless to build a grand theory of what AI can fundamentally do in the space of math proofs based on a few months of existence of such strength models. I find it interesting how many people are incapable of imagining that the tech capability will not be frozen at today's level and where we were e.g. a year ago and that a similar change may happen until next year. Instead they make sweeping assumptions that the current limitations will be with us for our lifetimes. You need a much stronger way to adapt to the new reality.