15 ms·

Humanity is better off for knowing these proofs. This strikes me as academic NIMBYism.

Panoramix 5d ago
Your point is largely addressed in the article, did you try reading it? "In many fields and activities, years of training have traditionally served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas. However, building on a vast body of previous human work, AI systems are becoming increasingly capable of producing the results of such work directly, and these goals cease to align." The point is that these proofs are largely useless without the insights. The value of a proof is largely in the travel, not so much in the destination.
- yieldcrv 5d ago
  Different person here, I read the article and they are all wrong. Hope that helps. Okay, to elaborate, substantively, their point is that the people using these AI models are not doing it for the love of the game, but for marketing. And instead of them - and nobody - spending millions of dollars to solve the problem, successfully, they want every problem of their academic industry to persist because even though they never solve the problem, they synthesize and solve lots of other problems nobody asked for. And get to boost their egos? Yeah, stop that. Actual alignment is on the humans themselves, if they want to remain relevant as academics and mathematicians, they need to learn how to replicate the proofs and the steps that alluded humans for decades and don't worry about the narcissistic elements that slow their industry down.
  - SpicyLemonZest 5d ago
    Their claim is only indirectly related to the motivations of the people using their models. What they're saying is that doing math in this way does not produce the same value as traditional mathematical research, and the people using these AI models aren't concerned about that because their marketing objectives don't depend on whether their results produce mathematical value. If people doing valuable work are made irrelevant by people doing a larger volume of non-valuable work, that's not a positive outcome.
    - deleted 5d ago
      [deleted]
    - ndriscoll 5d ago
      But it does produce value. We now have an explicit solution and even a proof. Humans can then work on clarifying why it's true. Unsurprisingly, not all that different from software, where models can generate working code just fine. The details will all be there and all correct, but the architecture is currently not ideal, so a human guiding it can greatly improve the proofs. I actually found this to be the case with some basic linear algebra notes I was recently doing in Lean (without using mathlib). The model could generate working proofs, but they obscure the basic ideas (actually I wonder somewhat if this is because the Lean code that's out there to train on doesn't make a huge effort to read like textbook proofs, which was my motivation in the first place). I give it a skeleton of a couple lines of `calc`, letting it fill in the reasoning for each line, and it does much better. Then ask it about making some macros to simplify "trivial" or "obvious" things, and it does even better. etc. I suspect there's a good workflow where a big SOTA model makes an impenetrable proof (or code) and then a human works with a FIM model to simplify it (with the larger gnarly proof right there in context for FIM), but unfortunately everyone seems to only care about agents right now.
      - YeGoblynQueenne 5d ago
        >> Humans can then work on clarifying why it's true. Presumably you're a human. Are you going to do that?
        ndriscoll 5d ago
        There are vanishingly few research mathematician positions and it's one of the most competitive fields, so no. But I'm not sure how that's relevant. As the OP says, usually the value of a proof is not the knowledge that something is true per se, but the reasoning techniques to understand why. How can it be anything other than helpful then to have a truth oracle as you try to figure out why things are true?
  - valegrete 5d ago
    Mathematical breakthroughs with commercial relevance are few and far between, and often depend on dusting off old results which were, at the time of discovery, "solutions nobody asked for." The NS counterxample is actually, by any market measure, a "problem nobody asked for" in the sense that its existence doesn't have any commercial relevance (beyond juicing OpenAI's IPO). So the only long-term value solving it could have is by virtue of whatever reusable theory/insights were generated along the way to the counterexample itself. The letter is absolutely right on that point. It's not actually clear that those insights will come faster from reverse engineering this LLM proof vs. humans building theory to solve the problem themselves. So what you're saying may or may not even be an efficient way of operating. Also, it implicitly depends on mathematicians to do the hard work of creating problems and then deciphering LLM hieroglyphics for essentially free while the only immediately profitable component gets outsourced to a frontier lab. In what world is that model going to work? Reading between the lines, it seems like maybe you have a personal grudge for some reason and simply think the technology will advance enough to where we won't need academics at all. But you should say that in the first place.
    - yieldcrv 5d ago
      What irks me is the ego My stance is that solving the problem is aligned with humankind the rest is just hypothesizing a way that academics fit in this world at all
      - valegrete 5d ago
        I just wonder whether a lot of smart people who never needed to go beyond the "solve for X" algorithmic math of a typical calculus sequence are actually reading the declaration the way the signatories wrote it. The Navier-Stokes problem is not exhausted by a simple 'no' counterexample (which most people familiar with the equations already expected to exist). In fact, I haven't heard a single person's explanation for what relevance this counterexample has for humankind. It is something impossible in our physical reality so we have gained zero insight into anything we actually model with NS. Mathematicians agree that "solving the problem is aligned with humankind." They disagree that releasing a counterexample this way actually constitutes "solving the problem" precisely because there is now little incentive to do the hard theory-building work that actually has the track record of leading to human advancement.
  - gjulianm 5d ago
    > I read the article and they are all wrong I would recommend a bit more humility and trying to better understand why 25 Fields medalists, among them people like Terence Tao (who isn't anti-AI by any means, he's even promoted a registry of AI Lean proofs), are saying this. > Okay, to elaborate, substantively, their point is that the people using these AI models are not doing it for the love of the game, but for marketing. No. The point is that AI companies are using the models to solve problems in such a way that the useful part of problem-solving, i.e. the theories and tools developed during the process, is not present. And they are doing that because the companies seem to be motivated not by honest advancement of math but by marketing and publicity. > they synthesize and solve lots of other problems nobody asked for. No one asked Fourier to solve series representation of functions when he was studying the heat equation, and yet thanks to that we have Fourier analysis. > if they want to remain relevant as academics and mathematicians, they need to learn how to replicate the proofs and the steps that alluded humans for decades The point they are making is that if AI keeps being used as "problem solver" rather than "theory understanding", replicating the proofs and getting the useful parts out of them will be far more difficult.
vector_spaces 5d ago
Do you "know", in any meaningful sense, any of OpenAI's recently publicized proofs? Do you suppose that there is any large community of non-academics that does? One of the points the parent makes, along with the TFA, is that academia -- or more specifically, the "mathematical community"-- is a setting primarily for creating and ingesting mathematical knowledge, and disseminating it to the next generation and to other fields. Humans absorb this material slowly, through lots of discussion and collaboration -- it is necessarily a slow process. Facilitating this is one of the important functions of academia. Your usage of academic as a slur here is a bit silly for this exact reason. I don't claim it is perfect, and we can argue about pedagogy in elementary courses till the cows come home. That's not really material. But this is one of the only settings in which such knowledge is broadly valued for its own sake, and in which there is a semblance of incentive to help others "know" this stuff as well, be they future generations of mathematicians, science and math educators and communicators, practitioners in other fields, or genuinely curious amateurs.
- adastra22 5d ago
  Why/how do you think I used academic as a slur here?

Your point is largely addressed in the article, did you try reading it? "In many fields and activities, years of training have traditionally served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas. However, building on a vast body of previous human work, AI systems are becoming increasingly capable of producing the results of such work directly, and these goals cease to align." The point is that these proofs are largely useless without the insights. The value of a proof is largely in the travel, not so much in the destination.

yieldcrv 5d ago
Different person here, I read the article and they are all wrong. Hope that helps. Okay, to elaborate, substantively, their point is that the people using these AI models are not doing it for the love of the game, but for marketing. And instead of them - and nobody - spending millions of dollars to solve the problem, successfully, they want every problem of their academic industry to persist because even though they never solve the problem, they synthesize and solve lots of other problems nobody asked for. And get to boost their egos? Yeah, stop that. Actual alignment is on the humans themselves, if they want to remain relevant as academics and mathematicians, they need to learn how to replicate the proofs and the steps that alluded humans for decades and don't worry about the narcissistic elements that slow their industry down.
- SpicyLemonZest 5d ago
  Their claim is only indirectly related to the motivations of the people using their models. What they're saying is that doing math in this way does not produce the same value as traditional mathematical research, and the people using these AI models aren't concerned about that because their marketing objectives don't depend on whether their results produce mathematical value. If people doing valuable work are made irrelevant by people doing a larger volume of non-valuable work, that's not a positive outcome.
  - deleted 5d ago
    [deleted]
  - ndriscoll 5d ago
    But it does produce value. We now have an explicit solution and even a proof. Humans can then work on clarifying why it's true. Unsurprisingly, not all that different from software, where models can generate working code just fine. The details will all be there and all correct, but the architecture is currently not ideal, so a human guiding it can greatly improve the proofs. I actually found this to be the case with some basic linear algebra notes I was recently doing in Lean (without using mathlib). The model could generate working proofs, but they obscure the basic ideas (actually I wonder somewhat if this is because the Lean code that's out there to train on doesn't make a huge effort to read like textbook proofs, which was my motivation in the first place). I give it a skeleton of a couple lines of `calc`, letting it fill in the reasoning for each line, and it does much better. Then ask it about making some macros to simplify "trivial" or "obvious" things, and it does even better. etc. I suspect there's a good workflow where a big SOTA model makes an impenetrable proof (or code) and then a human works with a FIM model to simplify it (with the larger gnarly proof right there in context for FIM), but unfortunately everyone seems to only care about agents right now.
    - YeGoblynQueenne 5d ago
      >> Humans can then work on clarifying why it's true. Presumably you're a human. Are you going to do that?
      - ndriscoll 5d ago
        There are vanishingly few research mathematician positions and it's one of the most competitive fields, so no. But I'm not sure how that's relevant. As the OP says, usually the value of a proof is not the knowledge that something is true per se, but the reasoning techniques to understand why. How can it be anything other than helpful then to have a truth oracle as you try to figure out why things are true?
- valegrete 5d ago
  Mathematical breakthroughs with commercial relevance are few and far between, and often depend on dusting off old results which were, at the time of discovery, "solutions nobody asked for." The NS counterxample is actually, by any market measure, a "problem nobody asked for" in the sense that its existence doesn't have any commercial relevance (beyond juicing OpenAI's IPO). So the only long-term value solving it could have is by virtue of whatever reusable theory/insights were generated along the way to the counterexample itself. The letter is absolutely right on that point. It's not actually clear that those insights will come faster from reverse engineering this LLM proof vs. humans building theory to solve the problem themselves. So what you're saying may or may not even be an efficient way of operating. Also, it implicitly depends on mathematicians to do the hard work of creating problems and then deciphering LLM hieroglyphics for essentially free while the only immediately profitable component gets outsourced to a frontier lab. In what world is that model going to work? Reading between the lines, it seems like maybe you have a personal grudge for some reason and simply think the technology will advance enough to where we won't need academics at all. But you should say that in the first place.
  - yieldcrv 5d ago
    What irks me is the ego My stance is that solving the problem is aligned with humankind the rest is just hypothesizing a way that academics fit in this world at all
    - valegrete 5d ago
      I just wonder whether a lot of smart people who never needed to go beyond the "solve for X" algorithmic math of a typical calculus sequence are actually reading the declaration the way the signatories wrote it. The Navier-Stokes problem is not exhausted by a simple 'no' counterexample (which most people familiar with the equations already expected to exist). In fact, I haven't heard a single person's explanation for what relevance this counterexample has for humankind. It is something impossible in our physical reality so we have gained zero insight into anything we actually model with NS. Mathematicians agree that "solving the problem is aligned with humankind." They disagree that releasing a counterexample this way actually constitutes "solving the problem" precisely because there is now little incentive to do the hard theory-building work that actually has the track record of leading to human advancement.
- gjulianm 5d ago
  > I read the article and they are all wrong I would recommend a bit more humility and trying to better understand why 25 Fields medalists, among them people like Terence Tao (who isn't anti-AI by any means, he's even promoted a registry of AI Lean proofs), are saying this. > Okay, to elaborate, substantively, their point is that the people using these AI models are not doing it for the love of the game, but for marketing. No. The point is that AI companies are using the models to solve problems in such a way that the useful part of problem-solving, i.e. the theories and tools developed during the process, is not present. And they are doing that because the companies seem to be motivated not by honest advancement of math but by marketing and publicity. > they synthesize and solve lots of other problems nobody asked for. No one asked Fourier to solve series representation of functions when he was studying the heat equation, and yet thanks to that we have Fourier analysis. > if they want to remain relevant as academics and mathematicians, they need to learn how to replicate the proofs and the steps that alluded humans for decades The point they are making is that if AI keeps being used as "problem solver" rather than "theory understanding", replicating the proofs and getting the useful parts out of them will be far more difficult.

vector_spaces 5d ago

Do you "know", in any meaningful sense, any of OpenAI's recently publicized proofs? Do you suppose that there is any large community of non-academics that does? One of the points the parent makes, along with the TFA, is that academia -- or more specifically, the "mathematical community"-- is a setting primarily for creating and ingesting mathematical knowledge, and disseminating it to the next generation and to other fields. Humans absorb this material slowly, through lots of discussion and collaboration -- it is necessarily a slow process. Facilitating this is one of the important functions of academia. Your usage of academic as a slur here is a bit silly for this exact reason. I don't claim it is perfect, and we can argue about pedagogy in elementary courses till the cows come home. That's not really material. But this is one of the only settings in which such knowledge is broadly valued for its own sake, and in which there is a semblance of incentive to help others "know" this stuff as well, be they future generations of mathematicians, science and math educators and communicators, practitioners in other fields, or genuinely curious amateurs.

adastra22 5d ago
Why/how do you think I used academic as a slur here?