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I'm a former research mathematician who worked for a little while in AI research, and this article matched up very well with my own experience with this particular cultural divide. Since I've spent a lot more time in the math world than the AI world, it's very natural for me to see this divide from the mathematicians' perspective, and I definitely agree that a lot of the people I've talked to on the other side of this divide don't seem to quite get what it is that mathematicians want from math: that the primary aim isn't really to find out whether a result is true but why it's true.

To be honest, it's hard for me not to get kind of emotional about this. Obviously I don't know what's going to happen, but I can imagine a future where some future model is better at proving theorems than any human mathematician, like the situation, say, chess has been in for some time now. In that future, I would still care a lot about learning why theorems are true --- the process of answering those questions is one of the things I find the most beautiful and fulfilling in the world --- and it makes me really sad to hear people talk about math being "solved", as though all we're doing is checking theorems off of a to-do list. I often find the conversation pretty demoralizing, especially because I think a lot of the people I have it with would probably really enjoy the thing mathematics actually is much more than the thing they seem to think it is.

Interestingly, the main article mentions Bill Thurston's paper "On Proof and Progress in Mathematics" (https://www.math.toronto.edu/mccann/199/thurston.pdf), but doesn't mention a quote from that paper that captures the essence of what you wrote:

> "The rapid advance of computers has helped dramatize this point, because computers and people are very different. For instance, when Appel and Haken completed a proof of the 4-color map theorem using a massive automatic computation, it evoked much controversy. I interpret the controversy as having little to do with doubt people had as to the veracity of the theorem or the correctness of the proof. Rather, it reflected a continuing desire for human understanding of a proof, in addition to knowledge that the theorem is true."

Incidentally, I've also a similar problem when reviewing HCI and computer systems papers. Ok sure, this deep learning neural net worked better, but what did we as a community actually learn that others can build on?

The Four Color Theorem is a great example! I think this story is often misrepresented as one where mathematicians didn't believe the computer-aided proof. Thurston gets the story right: I think basically everyone in the field took it as resolving the truth of the Four Color Theorem --- although I don't think this was really in serious doubt --- but in an incredibly unsatisfying way. They wanted to know what underlying pattern in planar graphs forces them all to be 4-colorable, and "well, we reduced the question to these tens of thousands of possible counterexamples and they all turned out to be 4-colorable" leaves a lot to be desired as an answer to that question. (This is especially true because the Five Color Theorem does have a very beautiful proof. I reach at a math enrichment program for high schoolers on weekends, and the result was simple enough that we could get all the way through it in class.)

Is the proof of the Four Colour Theorem really that unsatisfying?

The Four Colour Theorem is true because there exists a finite set of unavoidable yet reducible configurations. QED.

To verify this computational fact one uses a (very) glorified pocket calculator.

I think you're missing the core component. We care __WHY__ the theorem is true. To be honest, the __IF__ part matters a lot less.

The thing is that the underlying reasoning (the logic) is what provides real insights. This is how we recognize other problems that are similar or even identical. The steps in between are just as important, and often more important.

I'll give an example from physics. (If you're unsatisfied with this one, pick another physics fact and I'll do my best. _Any_ will do.) Here's a "fact"[0]: The atoms with even number of electrons are more stable than those with an odd number. We knew this in the 1910's, and this is a fact that directly led to the Pauli Exclusion Principle, which led us to better understand chemical bonds. Asking why Pauli Exclusion happens furthers our understanding and leading us to a better understanding of the atomic model. It goes on and on like this.

It has always been about the why. The why is what leads us to new information. The why is what leads to generalization. The why is what leads to causality and predictive models. THe why is what makes the fact useful in the first place.

[0] Quotes are because truth is very very hard to derive. https://hermiene.net/essays-trans/relativity_of_wrong.html

I do think the why that the Four Colour Theorem is true is captured my statement. The reason why it is true is because there exists some finite unavoidable and reducible set of configurations.

I'm fairly sure that people are only getting hung up on the size of this finite set, for no good reason. I suspect that if the size of this finite set were 2, instead of 633, and you could draw these unavoidable configuration on the chalk board, and easily illustrate that both of them are reducible, everyone would be saying "ah yes, the four colour theorem, such an elegant proof!"

Yet, whether the finite set were of size 2 or size 633, the fundamental insight would be identical: there exists some finite unavoidable and reducible set of configurations.

> I'm fairly sure that people are only getting hung up on the size of this finite set, for no good reason.

I think that is exactly correct, except for the "no good reason" part. There aren't many (any?) practical situations where the 4-colour theory's provability matters. So the major reason for studying it is coming up with a pattern that can be used in future work.

Having a pattern with a small set (single digit numbers) means that it can be stored in the human brain. 633 objects can't be. That limits the proof.

To me, the four-color theorem is a very interesting proof of concept, perhaps the most interesting mathematical proof of the past 50 years. Perhaps the "pattern that can be used in future work" is the idea of having computers enumerate a large number of cases, which they then solve in individually straightforward ways.

But, I can understand if pure mathematicians don't feel this way. This might be only really an intriguing and beautiful concept to someone who is interested in scaling up algorithms and AI.

> So the major reason for studying it is coming up with a pattern that can be used in future work.

Surely, reducing the infinite way in which polygons can be placed on a plane to a finite set, no matter how large, must involve some pattern useful for future work?

But why stop at “some” pattern when you can find the most general pattern.

The nature does not care whether it fits in our brains.

That’s why we use math to describe nature in a way that fits in our brains.

That’s the whole point of math.

If it were size 2, we could more easily make sure that the answer is definitely mind-blowing.

Have programmers given up on wanting their mind blown by unbelievable simplicity?

> The reason why it is true is because there exists some finite unavoidable and reducible set of configurations.

OK but respectfully that's just restating the problem in an alternative form. We don't get any insight from it. Why does there exist this limit? What is it about this problem that makes this particular structure happen?

Perhaps the key insight is that there is no concise explanation that underlies this particular structure. Many mathematical statements are true for no concise reason. If you want to discover if these things are true or not, perhaps you need a computer-assisted search, and that's the intellectual lesson to be drawn here.

I really doubt this. I mean mathematicians spent decades trying to answer if the number 2 exists. People spend a lot of time on what seems fairly mundane and frankly, the results are quite beneficial. What's incredible or mind blowing is really just about your perspective, it is really just about your choice to wonder more. https://www.youtube.com/shorts/lcQAWEqPmeg

Mind-blowing result from a different attempt to prove four color theorem

https://blog.tanyakhovanova.com/2024/11/foams-made-out-of-fe...

> The Four Colour Theorem is true because there exists a finite set of unavoidable yet reducible configurations. QED.

You just summarised (nearly) everything a mathematician can get out of that computerised proof. That's unsatisfying. It doesn't give you any insight into any other areas of math, nor does it suggest interesting corollaries, nor does it tell you which pre-condition of the statement does what work.

That's rather underwhelming. That's less than you can get out of the 100th proof of Pythagoras.

Fwiw fairly recently posted to HN was progress towards a more satisfying (read, likely mind-blowing) proof:

https://blog.tanyakhovanova.com/2024/11/foams-made-out-of-fe...

This is also nice because only pre-1600 tech involved

Another example akin to the proof of the 4-color map theorem was the proof of the Kepler conjecture [1], i.e. "Grocers stack their oranges in the densest-possible way."

We "know" it's true, but only because a machine ground mechanically through lots of tedious cases. I'm sure most mathematicians would appreciate a simpler and more elegant proof.

[1] https://en.wikipedia.org/wiki/Kepler_conjecture

I understand the emotion and the sadness you mention from a different situation I experienced about a dozen years ago. At that time I was entering Kaggle machine learning competitions, and I did well enough to reach 59th on the global leaderboard. But the way I did it was by trying to understand the problem domain and make and test models based on that understanding.

However by the end of that period, it seemed to transition to a situation where the most important skill in achieving a good score was manipulating statistical machine learning tools (Random Forests was a popular one, I recall), rather than gaining deep understanding of the physics or sociology of the problem, and I started doing worse and I lost interest in Kaggle.

So be it. If you want to win, you use the best tools. But the part that brought joy to me was not fighting for the opportunity to win a few hundred bucks (which I never did), but for the intellectual pleasure and excitement of learning about an interesting problem in a new field that was amenable to mathematical analysis.

I witnessed the same bitter lesson on Kaggle, too. Late stage competitions were almost always won by a mixture of experts using the most recently successful models on that problem. Or a random forest of the same. It was a little frustrating/disappointing to the part of me that wanted to see insights, not just high scores.

Many years ago I heard a mathematician speaking about some open problem and he said, "Sure, it's possible that there is a simple solution to the problem using basic techniques that everyone has just missed so far. And if you find that solution, mathematics will pat you on the head and tell you to run off and play.

"Mathematics advances by solving problems using new techniques because those techniques open up new areas of mathematics."

That's the attitude of poor mathematicians who are insecure about their own faults.

What the hell is that quote? No, a simple proof is the absolute mathematical ideal!

Really? I've always had the impression that "elementary" proofs of hard problems are highly valued.

A proof of a long-open conjecture that uses only elementary techniques is typically long and convoluted.

Think of the problem as requiring spending a certain amount of complexity to solve. If you don't spend it on developing a new way of thinking then you spend it on long and tedious calculations that nobody can keep in working memory.

It's similar to how you can write an AI model in Pytorch or you can write down the logic gates that execute on the GPU. The logic gate representation uses only elementary techniques. But nobody wants to read or check it by hand.

That seems like a justification that is right on the knife's edge of being a self-licking ice cream cone.

TBH, I think you're worrying about a future that is likely to become much more fun than boring.

For actual research mathematics, there is no reason why an AI (maybe not current entirely statistical models) shouldn't be able to guide you through it exactly the way you prefer to. Then it's just a matter of becoming honest with your own desires.

But it'll also vastly blow up the field of recreational mathematics. Have the AI toss a problem your way you can solve in about a month. A problem involving some recent discoveries. A problem Franklin could have come up with. During a brothel visit. If he was on LSD.

I'm not a mathematician so please feel free to correct me...but wouldn't there still be an opportunity for humans to try to understand why a proof solved by a machine is true? Or are you afraid that the culture of mathematics will shift towards being impatient about this sorts of questions?

Well, it depends on exactly what future you were imagining. In a world where the model just spits out a totally impenetrable but formally verifiable Lean proof, then yes, absolutely, there's a lot for human mathematicians to do. But I don't see any particular reason things would have to stop there: why couldn't some model also spit out nice, beautiful explanations of why the result is true? We're certainly not there yet, but if we do get there, human mathematicians might not really be producing much of anything. What reason would there be to keep employing them all?

Like I said, I don't have any idea what's going to happen. The thing that makes me sad about these conversations is that the people I talk to sometimes don't seem to have any appreciation for the thing they say they want to dismantle. It might even be better for humanity on the whole to arrive in this future; I'm not arguing that one way or the other! Just that I think there's a chance it would involve losing something I really love, and that makes me sad.

> The thing that makes me sad about these conversations is that the people I talk to sometimes don't seem to have any appreciation for the thing they say they want to dismantle

Yes! This is what frustrates my about the pursuit of AI for the arts too.

This seems obviously untrue: why would they be replicating it if they didn’t want it?

I see both cases as people who aren’t well served by the artisanal version attempting to acquire a better-than-commoditized version because they want more of that thing to exist. We regularly have both things in furniture and don’t have any great moral crisis that chairs are produced mechanistically by machines. To me, both things sound like “how dare you buy IKEA furniture — you have no appreciation of woodwork!”

Maybe artisanal math proofs are more beautiful or some other aesthetic concern — but what I’d like is proofs that business models are stable and not full of holes constructed each time a new ML pipeline deploys; which is the sort of boring, rote work that most mathematicians are “too good” to work on. But they’re what’s needed to prevent, eg, the Amazon 2018 hiring freeze.

That’s the need that, eg, automated theorem proving truly solves — and mathematicians are being ignored (much like artist) by people they turn up their noses at.

> why would they be replicating it if they didn’t want it?

Who is "they"?

Most AI for math work is being done by AI researchers that are not themselves academic mathematicians (obviously there exceptions). Similarly, most AI for music and AI for visual art is being done by AI researchers that themselves are not professional musicians or artists (again, there are exceptions). This model can work fine if the AI researchers collaborate with mathematicians or artists to understand that the use of AI is actually useful in the workflow of those fields, but often that doesn't happen and there is a savior-like arrogance where AI researchers think they'll just automate those fields. Same thing happens in AI for medicine. So the reason many of those AI researchers want to do this is for the usual incentives - money and publications.

Clearly, there are commercial use cases for AI in all these fields and those may involve removing humans entirely. But in the case of art, and I (and Hardy) would argue academic math, there's a human aspect that can't be removed. Both of those approaches can exist in the world and have value but AI can't replace Van Gogh entirely. It'll automate the process of creating mass produced artwork or become a tool that human artists can use. Both of those require understanding the application domain intimately, so my point stands I think.

> This model can work fine if the AI researchers collaborate with mathematicians or artists to understand that the use of AI is actually useful in the workflow of those fields, but often that doesn't happen and there is a savior-like arrogance where AI researchers think they'll just automate those fields.

In my experience, the vast majority is people who are hobbyists or amateurs in those fields, who are looking to innovate in approaches — eg, the overwhelming majority of AI music is hobbyists using models to experiment. Similarly, the overwhelming majority of people using AI graphics tools are making memes or pictures to share with friends.

Those people are poorly served by the artisanal approach and are looking to create more art — they’re not engaging in “savior-like arrogance” but trying to satisfy unmet desire for new music and art. You’re merely being snooty.

> But in the case of art, and I (and Hardy) would argue academic math, there's a human aspect that can't be removed.

This is the pretentiousness I called out (and you completely failed to address):

> That’s the need that, eg, automated theorem proving truly solves — and mathematicians are being ignored (much like artist) by people they turn up their noses at.

Nobody is stopping you from your artisanal proofs — have at it. You’re refusing to do the ugly work people actually want, so they’re solving their problems with a tool that doesn’t involve you.

I think we're actually in agreement with respect to the needs of AI in music and graphics. When I say "AI researchers" I'm talking about people in industrial and academic labs. I consider the hobbyists you describe to be the users (who obviously can also be researchers). It's the desires/needs of the latter that should drive the research agenda of the former.

I actually don't understand your position here or what you think I'm arguing for. My point is that the real musicians, artists and mathematicians (whether they're hobbyists, academics or professionals in industry) are not well served by detached AI researchers just trying to automate their work for them. They need AI researchers to understand their workflows and build tools that elevate them, i.e. bicycles for the mind (or hands?).

Again, I do recognize there may be new fully automated workflows that can come out of AI research too but I maintain that the actual artists, musicians and mathematicians today have a valuable role in guiding that development too.

I don’t think the advent of superintelligence will lead to increased leisure time and increased well-being / easier lives. However, if it did I wouldn’t mind redundantly learning the mathematics with the help of the AI. It’s intrinsically interesting and ultimately I don’t care to impress anybody, except to the extent it’s necessary to be employable.

I would love that too. In fact, I already spend a good amount of my free time redundantly learning the mathematics that was produced by humans, and I have fun doing it. The thing that makes me sad to imagine --- and again, this is not a prediction --- is the loss of the community of human mathematicians that we have right now.

>But I don't see any particular reason things would have to stop there: why couldn't some model also spit out nice, beautiful explanations of why the result is true?

Oh… I didnt anticipate this would bother you. Would it be fair to say that its not that you like understanding why its true, because you have that here, but that you like process of discovering why?

Perhaps thats what you meant originally. But my understanding was that you were primarily just concerned with understanding why, not being the one to discover why.

This is an interesting question! You're giving me a chance to reflect a little more than I did when I wrote that last comment.

I can only speak for myself, but it's not that I care a lot about me personally being the first one to discover some new piece of mathematics. (If I did, I'd probably still be doing research, which I'm not.) There is something very satisfying about solving a problem for yourself rather than being handed the answer, though, even if it's not an original problem. It's the same reason some people like doing sudokus, and why those people wouldn't respond well to being told that they could save a lot of time if they just used a sudoku solver or looked up the answer in the back of the book.

But that's not really what I'm getting at in the sentence you're quoting --- people are still free to solve sudokus even though sudoku solvers exist, and the same would presumably be true of proving theorems in the world we're considering. The thing I'd be most worried about is the destruction of the community of mathematicians. If math were just a fun but useless hobby, like, I don't know, whittling or something, I think there would be way fewer people doing it. And there would be even fewer people doing it as deeply and intensely as they are now when it's their full-time job. And as someone who likes math a lot, I don't love the idea of that happening.

CNCs and other technology haven’t destroyed woodworking. There’s whole communities on YouTube — with a spectrum from casual to hobbyist to artisanal to industrial.

Why would mathematics be different than woodworking?

Do you believe there’s a limited demand for mathematics? — my experience is quite the opposite, that we’re limited by the production capacity.

HN has this very unique and strange type of reasoning. You’re actually asking why would mathematics be any different than woodworking because CNC machines? It’s like aby issue can be reduced to the most mundane observations and simplicity because we have to justify all technology. Professional mathematics requires years of intense and usually, i.e. almost always, in graduate schools and the entire machinery of that. You’re comparing something many people do as a hobby to the life’s work and f others. of course you can have wave all this away with some argument but I’m not sure this type of reasoning is going to save the technocrats when it he majority of people realize what this app portends for society.

No Im not — I’m comparing two fields that range from hobby to professional, both of which I’ve worked in professionally. But for which automation occurred at different times.

> You’re comparing something many people do as a hobby to the life’s work and f others.

You’re denigrating the talents and educational efforts of artisanal woodworkers to make a shallow dismissal of my point.

This is actually a metaphor I've used myself. I do think the woodworking community is both smaller and less professionalized than it would be in a world where industrial furniture production didn't exist. (This is a bizarre counterfactual, because it's basically impossible for me to imagine a world where industrial furniture production doesn't exist but YouTube does, but like pretend with me here for a moment.) I don't know that this is necessarily a bad thing, but it's definitely different, and I can imagine that if I were a woodworker who lived through the transition from one world to the other I would find it pretty upsetting! As I said above, I'm not claiming it's not worth making the transition anyway, but it does come with a cost.

One place I think the analogy breaks down, though, is that I think you're pretty severely underestimating the time and effort it takes to be productive at math research. I think my path is pretty typical, so I'll describe it. I went to college for four years and took math classes the whole time, after which I was nowhere near prepared to do independent research. Then I went to graduate school, where I received a small stipend to teach calculus to undergrads while I learned even more math, and at the end of four and a half years of that --- including lots of one-on-one mentorship from my advisor --- I just barely able to kinda sorta produce some publishable-but-not-earthshattering research. If I wanted to produce research I was actually proud of, it probably would have taken several more years of putting in reps on less impressive stuff, but I left the field before reaching that point.

Imagine a world where any research I could have produced at the end of those eight and a half years would be inferior to something an LLM could spit out in an afternoon, and where a different LLM is a better calculus instructor than a 22-year-old nicf. (Not a high bar!) How many people are going to spend all those years learning all those skills? More importantly, why would they expect to be paid to do that while producing nothing the whole time?

That’s no different than high-end artisanal woodworking, which I would argue is comparable to self-directed, cutting-edge research:

- apprenticing

- journeyman phase

- only finally achieving mastery

CNC never replaced those people, rather, it scaled the whole field — by creating much higher demand for furniture. People who never made that full journey instead work at factories where their output is scaled. What was displaced was mediocre talent in average homes, eg, building your own table from a magazine design.

You still haven’t answered why you think mathematics will follow a different trajectory — and the only substantial displacement will, eg, be business analysts no longer checking convexity of models and outsourcing that to AI-scaled math experts at the company.

Woodworking is very far from my world, so I don't really have any grounds to judge how comparable the two things actually are. I'll say two things instead.

First, right now presumably the reason a few people still become master woodworkers is that their work is actually better than the mass-produced furniture that you can get for much less money. Imagine a world where instead it was possible to cheaply and automatically produce furniture that is literally indistinguishable from, or maybe even noticeably superior to, anything a human woodworker could ever make. Do you really think the same number of people would still spend years and years developing those skills?

Second, you've talked about business logic and "math experts at the company" a few times now, which makes me wonder if we're just referring to different things with the word "mathematics". I'm talking about a specific subset, what's sometimes called "pure math," the kind of research that mostly only exists within academia and is focused on proving theorems with the goal of improving human understanding of mathematical patterns with no particular eye on solving any practical problems. It sounds like you're focused on the sort of mathematical work that gets done in industry, where you're using mathematical tools, but the goal is to solve a practical problem for a business.

These are actually quite different activities --- the same individuals who are good at one stand a decent chance of being good at the other, but that's most of what they have in common, and even there I know many people who are much more skilled at one than the other. I'm not really asking anyone who doesn't care about pure math to start caring about it, but when I'm talking about the effect of AI on the future of the field, I'm referring specifically to pure math research.

It would be like having the machine code to something amazing but lacking the ability to adequately explain it or modify it - the machine code is too big and complicated to follow, so unless you can express it or understand it in a better way, it can only be used exactly how it is already.

In mathematics it is just as (if not moreso) important to be able to apply techniques used to solve novel proofs as it is to have the knowledge that the theorem itself is true. Not only might those techniques be used to solve similar problems that the theorem alone cannot, but it might even uncover wholly new mathematical concepts that lead you to mathematics that you previously could not even conceive of.

Machine proofs in their current form are basically huge searches/brute forces from some initial statements to the theorem being proved, by way of logical inference. Mathematics is in some ways the opposite of this: it's about understanding why something is true, not solely whether it is true. Machine proofs give you a path from A to B but that path could be understandable-but-not-generalizable (a brute force), not-generalizable-but-understandable (finding some simple application of existing theorems to get the result that mathematicians simply missed), or neither understandable-nor-generalizable (imagine gigabytes of pure propositional logic on variables with names like n098fne09 and awbnkdujai).

Interestingly, some mathematicians like Terry Tao are starting to experiment with combining LLMs with automated theorem proving, because it might help in both guiding the theorem-prover and explaining its results. I find that philosophically fascinating because LLMs rely on some practices which are not fully understood, hence the article, and may validate combining formal logic with informal intuition as a way of understanding the world (both in mathematics, and generally the way our own minds combine logical reasoning with imprecise language and feelings).

That is kind of hard to do. Human reasoning and computer reasoning is very different, enough so that we can't really grasp it. Take chess, for example. Humans tend to reason in terms of positions and tactics, but computers just brute force it (I'm ignoring stuff like Alpha Zero because computers were way better than us even before that). There isn't much to learn there, so GMs just memorize the computer moves for so and so situation and then go back to their past heuristics after n moves

> so GMs just memorize the computer moves for so and so situation and then go back to their past heuristics after n moves

I think they also adjust their heuristics, based on looking at thousands of computer moves.

It’s kind of like knowing the answer to the ultimate question of life and not knowing the question itself ;)

"I can imagine a future where some future model is better at proving theorems than any human mathematician" Please do not overestimate the power of the algorithm that is predicting next "token" (e.g. word) in a sequence of previously passed words (tokens).

This algorithm will happily predict whatever it was fed with, just ask Chat GPT to write the review of non-existing camera, car or washing machine, you will receive nicely written list of advantages of such item, so what it does not exist.

Luckily, we have ways of verifying mathematical results, and using that to improve our AI systems: https://deepmind.google/discover/blog/ai-solves-imo-problems...

I can also write you a review of a non-existent camera or washing machine. Or anything else you want a fake review of! Does that mean I’m not capable of reasoning?

If you are not capable of distinguishing between truth and lie, and not capable of reflection which is the drive behind learning from past mistakes - then yes.

> what it is that mathematicians want from math: that the primary aim isn't really to find out whether a result is true but why it's true.

I really wish that had been my experience taking undergrad math courses.

Instead, I remember linear algebra where the professor would prove a result by introducing an equation pulled out of thin air, plugging it in, showing that the result was true, and that was that. OK sure, the symbol manipulation proved it was true, but zero understanding of why. And when I'd ask professors about the why, I'd encounter outright hostility -- all that mattered was whether it was proven, and asking "why" was positively amateurish and unserious. It was irrelevant to the truth of a result. The same attitude prevailed when it got to quantum mechanics -- "shut up and calculate".

I know there are mathematicians who care deeply about the why, and I have to assume it's what motivates many of them. But my actual experience studying math was the polar opposite. And so I find it very surprising to hear the idea of math being described as being more interested in why than what. The way I was taught didn't just not care about the why, but seemed actively contemptuous of it.

Math-for-engineering and math-for-math courses are very different in emphasis and enthusiasm. Many engineering students tend to not care too much about proofs, so the "get it working" approach might be fine for them. Also, the math profs teaching the "math-for-engineering" courses tend to view them as a chore (which it kind of is; pure math doesn't get a lot of funding, so they have to pick up these engineering-oriented courses to grab a slice of that engineering money).

I guess, what university and what level of math was that?

I majored in math at MIT, and even at the undergraduate level it was more like what OP is describing and less like what you're saying. I actually took linear algebra twice since my first major was Economics before deciding to add on a math major, and the version of linear algebra for your average engineer or economist (i.e.: a bunch of plug and chug matrices-type stuff), which is what I assume you're referring to, was very different. Linear algebra for mathematicians was all about vector spaces and bases and such, and was very interesting and full of proofs. I don't think actually concretely multiplying matrices was even a topic!

So I guess linear algebra is one of those topics where the math side is interesting and very much what all the mathematicians here are describing, but where it turned out to be so useful for everything, that there's a non-mathematician version of it which is more like what it sounds like you experienced.

in poker AI solvers tell you what the optimal play is and it's your job to reverse engineer the principles behind it. It cuts a lot of the guess work out but there's still plenty of hard work left in understanding the why and ultimately that's where the skill comes in. I wonder if we'll see the same in math

As Heidegger pointed out in _"The question concerning technology"_ the driving mindset behind industrial technology is to turn everything into a (fungible) standing resource -- instrumentalizing it and robbing it of any intrinsic meaning.

Maybe because CS is more engineering than science (at least as far as what drives the sociology), a lot of people approach AI from the same industrial perspective -- be it applications to math, science, art, coding, and whatever else. Ideas like _the bitter lesson_ only reinforce the zeitgeist.

If the shortest proof for some theorem is several thousand pages long and beyond the ability of any biological mind to comprehend, would mathematicians not care about it?

Which is to say, if you only concern yourself with theorems which have short, understandable proofs, aren't you cutting yourself off from vast swathes of math space?

Hm, good question. It depends on what you mean. If you're asking about restricting which theorems we try to prove, then we definitely are cutting ourselves off from vast swathes of math space, and we're doing it on purpose! The article we're responding to talks about mathematicians developing "taste" and "intuition", and this is what I think the author meant --- different people have different tastes, of course, but most conceivable true mathematical statements are ones that everyone would agree are completely uninteresting; they're things like "if you construct these 55 totally unmotivated mathematical objects that no one has ever cared about according to these 18 random made-up rules, then none of the following 301 arrangements are possible."

If you're talking about questions that are well-motivated but whose answers are ugly and incomprehensible, then a milder version of this actually happens fairly often --- some major conjecture gets solved by a proof that everyone agrees is right but which also doesn't shed much light on why the thing is true. In this situation, I think it's fair to describe the usual reaction as, like, I'm definitely happy to have the confirmation that the thing is true, but I would much rather have a nicer argument. Whoever proved the thing in the ugly way definitely earns themselves lots of math points, but if someone else comes along later and proves it in a clearer way then they've done something worth celebrating too.

Does that answer your question?

So Godel proved that there are true theorems that are unprovable. My hunch is that there is a fine grained version of this result <-- that there is a some distribution on the length of the proof for any given conjecture. If true that would mean that we better get used to dealing with long nasty proofs because they are a necessary part of mathematics...perhaps even, in some kind of Kolmogorov complexity-esque fashion, the almost-always bulk of it

Agree that something like this does seem likely. And this line of thought also highlights the work of Chaitin, and the fact that the current discussion around AI is just the latest version of early-2000s quasi-empiricism[1] stuff that never really got resolved. Things like the 4-color theorem would seem to be just the puny top of really big iceberg, and it's probably not going away.

The new spin on these older unresolved issues IHMO is really the black-box aspect of our statistical approaches. Lots of mathematicians that are fine with proof systems like Lean and some million-step process that can in principle be followed are also happy with more open-ended automated search and exploration of model spaces, proof spaces, etc. But can they ever be really be happy with a million gigabyte network of weighted nodes masquerading as some kind of "explanation" though? Not a mathematician but I sympathize. Given the difficulty of building/writing/running it, that looks more like a product than like "knowledge" to me (compare this to how Lean can prove Godel on your laptop).

Maybe it's easier to swallow the bitter pill of poor quality explanations though after the technology itself is a little easier to actually handle. People hate ugly things less if they are practical, and actually something you can build pretty stuff on top on.

https://en.wikipedia.org/wiki/Quasi-empiricism_in_mathematic...

I’m not sure that’s quite true. Say the proof of proposition P requires a minimum of N symbols. You could prove it in one paper that’s N symbols long and nobody can read, or you can publish k readable papers, with an average length on the order of N/k symbols, and develop a theory that people can use.

I think even if N is quite large, that just means it may take decades or millennia to publish and understand all k necessary papers, but maybe it’s still worth the effort even if we can get the length-N paper right away. What are you going to do with a mathematical proof that no one can understand anyway?

> If the shortest proof for some theorem is several thousand pages long and beyond the ability of any biological mind to comprehend, would mathematicians not care about it?

Care or not, what are they supposed to do with it?

Sure, they can now assume the theorem to be true, but nothing stopped them from doing that before.

  > the primary aim isn't really to find out whether a result is true but why it's true.

But being over in the AI/ML world now, this is my NUMBER ONE gripe. Very few are trying to understand why things are working. I'd argue that the biggest reason machines are black boxes are because no one is bothering to look inside of them. You can't solve things like hallucinations and errors without understanding these machines (and there's a lot we already do understand). There's a strong pushback against mathematics and I really don't understand why. It has so many tools that can help us move forward, but yes, it takes a lot of work. It's bad enough I know people who have gotten PhDs from top CS schools (top 3!) and don't understand things like probability distributions.

Unfortunately doing great things takes great work and great effort. I really do want to see the birth of AI, I wouldn't be doing this if I didn't, but I think it'd be naive to believe that this grand challenge can entirely be solved by one field and something so simple as throwing more compute (data, hardware, parameters, or however you want to reframe the Bitter Lesson this year).

Maybe I'm biased because I come from physics where we only care about causal relationships. The "_why_" is the damn Chimichanga. And I should mention, we're very comfortable in physics working with non-deterministic systems and that doesn't mean you can't form causal relationships. That's what the last hundred and some odd years have been all about.[1]

[0] Undergrad in physics, moved to work as engineer, then went to grad school to do CS because I was interested in AI and specifically in the mathematics of it. Boy did I become disappointment years later...

[1] I think there is a bias in CS. I notice there is a lot of test driven development, despite that being well known to be full of pitfalls. You unfortunately can't test your way into a proof. Any mathematician or physicist can tell you. Just because your thing does well on some tests doesn't mean there is proof of anything. Evidence, yes, but that's far from proof. Don't make the mistake Dyson did: https://www.youtube.com/watch?v=hV41QEKiMlM

> I'd argue that the biggest reason machines are black boxes are because no one is bothering to look inside of them.

People do look, but it's extremely hard. Take a look at how hard the mechanistic interpretability people have to work for even small insights. Neel Nanda[1] has some very nice writeups if you haven't already seen them.

[1]: https://www.neelnanda.io/mechanistic-interpretability

  > People do look

  > Very few are trying to understand why things are working

Personal, I believe that if you aren't trying to interpret results and ask the why then you're not actually doing science. Which is fine. There's plenty of good things that come from outside science. I just think it's weird to call something science if you aren't going to do hypothesis testing and finding out why things are the way they are

The problem is that mechanistic interpretability is a lot like neuroscience or molecular biology, i.e. you're trying to understand huge complexity from relatively crude point measurements (no offense intended to neuroscientists and biologists). But AI wants publishable results yesterday. I often wonder whether the current AI systems will stay around long enough for anyone to remain interested in understanding why they ever worked.

People will always be interested in why things work. At least one will as long as I'm alive, but I really don't think I'm that special. Wondering why things are the way they are is really at the core of science. Sure, there are plenty of physicists, mathematicians, neuroscientists, biologists, and others who just want answers, but this is a very narrow part of science.

I would really encourage others to read works that go through the history of the topic they are studying. If you're interested in quantum mechanics, the one I'd recommend is "The Quantum Physicists" by William Cropper[0]. It won't replace Griffiths[1] but it is a good addition.

The reason that getting information like this is VERY helpful is that it teaches you how to solve problems and actually go into the unknown. It is easy to learn things from a book because someone is there telling you all the answers, but texts like these instead put yourself in the shoes of the people in those times, and focus on seeing what and why certain questions are being asked. This is the hard thing when you're at the "end". When you can't just read new knowledge from a book, because there is no one that knows! Or the issue Thomas Wolf describes here[2] and why he struggled.

[0] https://www.amazon.com/Quantum-Physicists-Introduction-Their...

[1] https://www.amazon.com/Introduction-Quantum-Mechanics-David-...

[2] https://thomwolf.io/blog/scientific-ai.html

taking a helicopter to the top of a mountain is not the same thing as climbing it

True. Taking a helicopter is way more impressive. The Everest was climbed in 1953 and the first helicopter to go there was in 2005. It is way harder thing to do.

No, in your analogy building a helicopter capable of going there is impressive. (Though I dispute the idea that it’s more impressive simply because helicopters were invented more recently than mountain climbing.) In any case, riding in a helicopter remains passive and in no sense impressive.

Understanding why something is true - that's the beauty of it

I’ve worked in tech my entire adult life and boy do I feel this deep in my soul. I have slowly withdrawn from the higher-level tech designs and decision making. I usually disagree with all of it. Useless pursuits made only for resume fodder. Tech decisions made based on the bonus the CTO gets from the vendors (Superbowl tickets anyone?) not based on the suitability of the tech.

But absolutely worst of all is the arrogance. The hubris. The thinking that because some human somewhere has figured a thing out that its then just implicitly known by these types. The casual disregard for their fellow humans. The lack of true care for anything and anyone they touch.

Move fast and break things!! Even when its the society you live in.

That arrogance and/or hubris is just another type of stupidity.

I'm sure that many of us sympathize, but can you please express your views without fulminating? It makes a big difference to discussion quality, which is why this is in the site guidelines: https://news.ycombinator.com/newsguidelines.html.

It's not just that comments that vent denunciatory feelings are lower-quality themselves, though usually they are. It's that they exert a degrading influence on the rest of the thread, for a couple reasons: (1) people tend to respond in kind, and (2) these comments always veer towards the generic (e.g. "lack of true care for anything and anyone", "just another type of stupidity"), which is bad for curious conversation. Generic stuff is repetitive, and indignant-generic stuff doubly so.

By the time we get further downthread, the original topic is completely gone and we're into "glorification of management over ICs" (https://news.ycombinator.com/item?id=43346257). Veering offtopic can be ok when the tangent is even more interesting (or whimsical) than the starting point, but most tangents aren't like that—mostly what they do is replace a more-interesting-and-in-the-key-of-curiosity thing with a more-repetitive-and-in-the-key-of-indignation thing, which is a losing trade for HN.

> Move fast and break things!! Even when its the society you live in.

This is the part I don't get honestly

Are people just very shortsighted and don't see how these changes are potentially going to cause upheaval?

Do they think the upheaval is simply going to be worth it?

Do they think they will simply be wealthy enough that it won't affect them much, they will be insulated from it?

Do they just never think about consequences at all?

I am trying not to be extremely negative about all of this, but the speed of which things are moving makes me think we'll hit the cliff before even realizing it is in front of us

That's the part I find unnerving

> Do they think they will simply be wealthy enough that it won't affect them much, they will be insulated from it?

Yes, partly that. Mostly they only care about their rank. Many people would burn down the country if it meant they could be king of the ashes. Even purely self-interested people should welcome a better society for all, because a rising tide lifts all boats. But not only are they selfish, they're also very stupid, at least in this aspect. They can't see the world as anything but zero sum, and themselves as either winning or losing, so they must win at all costs. And those costs are huge.

Reminds me of the Paradise Lost quote, "Better to rule in Hell, than serve in Heaven", such an insightful book for understanding a certain type of person from Milton. Beautiful imagery throughout too, highly recommend.

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> Do they just never think about consequences at all?

Yes, I think this is it. Frequently using social media and being “online” leads to less critical thought, less thinking overall, smaller window that you allow yourself to think in, thoughts that are merely sound bites not fully fleshed out thoughts, and so on. Ones thoughts can easily become a milieu of memes and falsehoods. A person whose mind is in the state will do whatever anyone suggests for that next dopamine hit!

I am guilty of it all myself which is how I can make this claim. I too fear for humanity’s future.

> Are people just very shortsighted and don't see how these changes are potentially going to cause upheaval?

> Do they think the upheaval is simply going to be worth it?

All technology causes upheaval. We've benefited from many of these upheavals to the point where it's impossible for most to imagine a world without the proliferation of movable type, the internal combustion engine, the computer, or the internet. All of your criticisms could have easily been made word for word by the Catholic Church during the medieval era. The "society" of today is no more of a sacred cow than its antecedent incarnations were half a millenium ago. As history has shown, it must either adapt, disperse, or die.

> The "society" of today is no more of a sacred cow than its antecedent incarnations were half a millenium ago. As history has shown, it must either adapt, disperse, or die

I am not concerned about some kind of "sacred cow" that I want to preserve

I am concerned about a future where those with power no longer need 90% of the population so they deploy autonomous weaponry that grinds most of the population into fertilizer

And I'm concerned there are a bunch of short sighted idiots gleefully building autonomous weaponry for them, thinking they will either be spared from mulching, or be the mulchers

Edit: The thing about appealing to history is that it also shows that when upper classes get too powerful they start to lose touch with everyone else, and this often leads to turmoil that affects the common folk most

As one of the common folk, I'm pretty against that

Exactly. It was described in Chesterton’s Fence:

There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, “I don’t see the use of this; let us clear it away.” To which the more intelligent type of reformer will do well to answer: “If you don’t see the use of it, I certainly won’t let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.”

I've called this out numerous times (and gotten downvoted regularly), with what I call the "Cult of Optimization"

aka optimization-for-its-own-sake, aka pathological optimization.

It's basically meatspace internalizing and adopting the paperclip problem as a "good thing" to pursue, screw externalities and consequences.

And, lo-and-behold, my read for why it gets downvoted here is that a lot of folks on HN ascribe to this mentality, as it is part of the HN ethos to optimize , often pathologically.

Love your point. "Lack of alignment" affects more than just AIs.

Humans like to solve problems and be at the top of the heap. Such is life, survival of the fittest after all. AI is a problem to solve, whoever gets to AGI first will be at the top of the heap. It's a hard drive to turn off.

In theory this is actually pretty easy to "turn off"

You flatten the heap

You decrease or eliminate the reward for being at the top

You decrease or eliminate the penalty for being at the bottom

The main problem is that we haven't figured out a good way to do this without creating a whole bunch of other problems

> But absolutely worst of all is the arrogance. The hubris. The thinking that because some human somewhere has figured a thing out that its then just implicitly known by these types.

I worked in an organization afflicted by this and still have friends there. In the case of that organization, it was caused by an exaggerated glorification of management over ICs. Managers truly did act according to the belief, and show every evidence of sincerely believing in it, that their understanding of every problem was superior to the sum of the knowledge and intelligence of every engineer under them in the org chart, not because they respected their engineers and worked to collect and understand information from them, but because managers are a higher form of humanity than ICs, and org chart hierarchy reflects natural superiority. Every conversation had to be couched in terms that didn't contradict those assumptions, so the culture had an extremely high tolerance for hand-waving and BS. Naturally this created cover for all kinds of selfish decisions based on politics, bonuses, and vendor perks. I'm very glad I got out of there.

I wouldn't paint all of tech with the same brush, though. There are many companies that are better, much better. Not because they serve higher ideals, but because they can't afford to get so detached from reality, because they'd fail if they didn't respect technical considerations and respect their ICs.

I feel like this rumbling can be summarized as "Ai is engineering, not math" - and suddenly a lot of things make sense

Why Ai field is so secretive? Because it's all trade secrets - and maybe soon to become patents. You don't give away precisely how semiconductor fabs work, only base research level of "this direction is promising"

Why everyone is pushed to add Ai in? Because that's where the money is, that's where the product is.

Why Ai needs results fast? Because it's production line, and you create and design stuff

Even the core distinction mentioned - that Ai is about "speculation and possibility" - that's all about tool experimenting and prototyping. It's all about building and constructing. Aka Engineering/Technology letters of STEM

I guess next step is to ask "what to do next?". IMO, math and Ai fields should realise the divide and slowly diverge, leaving each other alone on an arm's length. Just as engineers and programmers (not computer scientists) already do

I had an aha moment recently. An excited AI researcher claimed, wow: claude could solve this IMO problem. Then, a mathematician pointed out a flaw which the AI researcher overlooked. The AI researcher then prompted the AI with the error and then the AI produced another proof he thought worked, but again was flawed. The AI played on the researcher's naivete.

Long story short, current AI is doing cargo-cult math - ie, going through the motions with mimicry. Experts can see through it, but excited AI hypesters are blind, and lap it up. Even alpha-geometry (with built-in theorem prover) is largely doing brute-force search of a limited axiomatized domain. This is not to say AI is not useful, just that the hype exceeds the actual.

Everything you’ve said is obvious, and yet really needs saying and isn’t said enough.

As Feynman once said [0]: "Physics is like sex. Sure, it may give some practical results, but that's not why we do it." I don't think it's any different for mathematics, programming, a lot of engineering, etc.

I can see a day might come when we (research mathematicians, math professors, etc) might not exist as a profession anymore, but there will continue to be mathematicians. What we'll do to make a living when that day comes, I have no idea. I suspect many others will also have to figure that out soon.

[0] I've seen this attributed to the Character of Physical Law but haven't confirmed it

I'd not heard that Feynman quote before, so thanks for sharing; I love it.

I'd include writing, art-, and music-making in that category.

In a way people don't do math just for its utility, they do it because it's beautiful, challenging, and deeply fulfilling

Back to gambling? Mathematics is a relatively new career. My understanding is that these guys used to gamble about solving proofs for a living.

Nice article. I didn't read every section in detail but I think it makes a good point that AI researchers maybe focus too much on the thought of creating new mathematics while being able to repdroduce, index or formalize existing mathematics is really they key goal imo. This will then also lead to new mathematics. I think the more you advance in mathematical maturity the bigger the "brush" becomes with which you make your strokes. As an undergrad a stroke can be a single argument in a proof, or a simple Lemma. As a professor it can be a good guess for a well-posedness strategy for a PDE. I think AI will help humans find new mathematics with much bigger brush strokes. If you need to generalize a specific inequality on the whole space to Lipschitz domains, perhaps AI will give you a dozen pages, perhaps even of formalized Lean, in a single stroke. If you are a scientist and consider an ODE model, perhaps AI can give you formally verified error and convergence bounds using your specific constants. You switch to a probabilistic setting? Do not worry. All of these are examples of not very deep but tedious and non-trivial mathematical busywork that can take days or weeks. The mathematical ability necessary to do this has in my opinion already been demonstrated by o3 in rare cases. It can not piece things together yet though. But GPT-4 could not piece together proofs to undergrad homework problems while o3 now can. So I believe improvement is quite possible.

> Throughout the conference, I noticed a subtle pressure on presenters to incorporate AI themes into their talks, regardless of relevance.

This is well-studied and not unique to AI, the USA in English, or even Western traditions. Here is what I mean: a book called Diffusion of Innovations by Rogers explains a history of technology introduction.. if the results are tallied in population, money or other prosperity, the civilizations and their language groups that have systematic ways to explore and apply new technology are "winners" in the global context.

AI is a powerful lever. The meta-conversation here might be around concepts of cancer, imbalance and chairs on the deck of the Titanic.. but this is getting off-topic for maths.

I think another way to think about this is that subtly trying to consider AI in your AI-unrelated research is just respecting the bitter lesson. You need to at least consider how a data-driven approach might work for your problem. It could totally wipe you out - make your approach pointless. That's the bitter lesson.

> Perhaps most telling was the sadness expressed by several mathematicians regarding the increasing secrecy in AI research. Mathematics has long prided itself on openness and transparency, with results freely shared and discussed. The closing off of research at major AI labs—and the inability of collaborating mathematicians to discuss their work—represents a significant cultural clash with mathematical traditions. This tension recalls Michael Atiyah's warning against secrecy in research: "Mathematics thrives on openness; secrecy is anathema to its progress" (Atiyah, 1984).

Engineering has always involved large amounts of both math and secrecy, what's different now?

AI is undergoing a transition from academic research to industry engineering.

(But the engineers want the benefits of academic research -- going to conferences to give talks, credibility, intellectual prestige -- without paying the costs, e.g. actually sharing new knowledge and information.)

It involves math at a research level, but from what I've observed, people in industry with engineering job titles make relatively little use of math. They will frequently tell you with that sheepish smile: "Oh, I'm not really a math person." Students are told with great confidence by older engineers that they'll never use their college math after they graduate.

Not exactly AI by today's standards, but a lot of the math that they need has been rolled into their software tools. And Excel is quite powerful.

[flagged]

I get the feeling you've never really talked to many academics.

Especially not mathematicians! No one goes into math academia for the money, and people with math Ph.D.'s are often very employable at much higher salaries if they jump ship to industry. The reason mathematicians stay in the field --- and I say this as someone who didn't stay, for a variety of reasons --- is because they love math and want to spend their time researching and teaching it.

I work with the ones that made the jump to industry, so no, I'm confronted with the divide day in and day out. The academics that either switch to industry or maintain close industry ties, typically do not seem to share these concerns, or at least, can contextualize them.

Academics that decided they didn't really want to be academics, but instead switched to industry. Seems like a significant sample bias, no?

They're still academics...

I miss when money people treated my motivations like a black box.

My take is a bit different. I only have a math undergrad and only worked as an AI trainer so I’m quite “low” on the totem pole.

I have listened to colin Mclarty talk about philosophy of math and there was a contingent of mathematicians who solely cared about solving problems via “algorithms”. The time period was just preceding the modern math since the late 1800s roughly, where the algorithmists, intuitivists, and logical oriented mathematicians coalesced into a combination that includes intuitive, algorithmic, and importance of logic, leading to the modern way we do proofs and focus on proofs.

These algorithmists didn’t care about the so called “meaningless” operations that got an answer, they just cared they got useful results.

I think the article mitigates this side of math, and is the side AI will be best or most useful at. Having read AI proofs, they are terrible in my opinion. But if AI can prove something useful even if the proof is grossly unappealing to the modern mathematician, there should be nothing to clamor about.

This is the talk I have in mind https://m.youtube.com/watch?v=-r-qNE0L-yI&pp=ygUlQ29saW4gbWN...

> One question generated particular concern: what would happen if an AI system produced a proof of a major conjecture like the Riemann Hypothesis, but the proof was too complex for humans to understand? Would such a result be satisfying? Would it advance mathematical understanding? The consensus seemed to be that while such a proof might technically resolve the conjecture, it would fail to deliver the deeper understanding that mathematicians truly seek.

I think this is an interesting question. In a hypothetical SciFi world where we somehow provably know that AI is infallible and the results are always correct, you could imagine mathematicians grudgingly accepting some conjecture as "proven by AI" even without understanding the why.

But for real-world AI, we know it can produce hallucinations and its reasoning chains can have massive logical errors. So if it came up with a proof that no one understands, how would we even be able to verify that the proof is indeed correct and not just gibberish?

Or more generally, how do you verify a proof that you don't understand?

Serious theorem-proving AIs always write the proof in a formal syntax where it is possible to check that the proof is correct without issue. The most popular such formal language is Lean, but there are many others. It's just like having a coding AI, it may write some function and you check if it compiles. If the AI writes a program/proof in Lean, it will only compile if the proof is correct. Checking the correctness of proofs is a much easier problem than coming up with the proof in the first place.

> Checking the correctness of proofs is a much easier problem than coming up with the proof in the first place.

Just so this isn't misunderstood, not so much cutting-edge math is presently possible to code in lean. The famous exceptions (such as the results by Clausen-Scholze and Gowers-Green-Manners-Tao) have special characteristics which make them much more ground-level and easier to code in lean.

What's true is that it's very easy to check if a lean-coded proof is correct. But it's hard and time-consuming to formulate most math as lean code. It's something many AI research groups are working on.

> The famous exceptions (such as the results by Clausen-Scholze and Gowers-Green-Manners-Tao) have special characteristics which make them much more ground-level and easier to code in lean.

"Special characteristics" is really overstating it. It's just a matter of getting all the prereqs formalized in Lean first. That's a bit of a grind to be sure, but the Mathlib effort for Lean has the bulk of the undergrad curriculum and some grad subjects formalized.

I don't think AI will be all that helpful wrt. this kind of effort, but it might help in some limited ways.

Yes I definitely concur, I have spent significant time with it.

The main bottleneck is having the libraries that define the theorems and objects you need to operate at those levels. Everything is founded on axiomatic foundations and you need to build all of maths on top of that. Projects like mathlib are getting us there but it is a massive undertaking.

It’s not just that it is a lot of maths to go through, it’s also that most maths has not really been proven to this degree of exactitude and there is much gap-filling to do when trying to translate existing proofs, or the reasoning style might be quite distant to how things are expressed in Lean. Some maths fields are also self-consistent islands that haven’t been yet connected to the common axiomatic foundations, and linking them is a serious research endeavor.

Although Lean does allow you to declare theorems as axioms. It is not common practice, but you can skip high up the abstraction ladder and set up a foundation up there if you are confident those theorems are correct. But still defining those mathematical objects can be quite hard on its own, even if you skip the proving.

Anyways, the complexity of the Lean language itself doesn’t help either. The mode of thinking you need to have to operate it is much closer to programming than maths, and for those that think that the Rust borrow-checker is a pain, this is an order of magnitude more complex.

Lean was a significant improvement in ergonomics compared to the previous generation (Coq, Isabelle, Agda…), but still I think there is a lot of work to be done to make it mathematician-friendly.

Most reinforcement-learning AI for maths right now is focused on olympiad problems, hard but quite low in the maths abstraction ladder. Often they don’t even create a proof, they just solve problems that end with an exact result and you just check that. Perhaps the reasoning was incorrect, but if you do it for enough problems you can be confident that it is not just guessing.

On the other side of the spectrum you have mathematicians like Tao just using ChatGPT for brainstorming. It might not be great at complex reasoning, but it has a much wider memory than you do and it can remind you of mathematical tools and techniques that could be useful.

> Anyways, the complexity of the Lean language itself doesn’t help either. The mode of thinking you need to have to operate it is much closer to programming than maths, and for those that think that the Rust borrow-checker is a pain, this is an order of magnitude more complex.

Could you elaborate on this? I'm interested to learn what the complexities are (beyond the mathematical concepts themselves).

Found something I wrote last year, see below, but off the top of my head:

Something like 5 different DSLs in the same language, most of it in a purist functional style that is neither familiar to most mathematicians nor most programmers, with type-checking an order of magnitude more strict and complex than any programming language (that's the point of it), with rather obscure errors most of the time.

It's really tedious to translate any non-trivial proofs to this model, so usually you end up proving it again almost from scratch within Lean, and then it's really hard to understand as it is written. Much of the information to understand a proof is hidden away as runtime data that is usually displayed via a complex VSCode extension. It's quite difficult to understand from the proof code itself, and usually it doesn't look anything like a traditional mathematical proof (even if they kind of try by naming keywords with a similar terminology as in normal proofs and sprinkling some unicode symbols).

I never-ever feel like I'm doing maths when I'm using Lean. I'm fighting with the syntax to figure out how to express mathematical concepts in the style that it likes, always having several different ways of achieving similar things (anti Zen of Python). And I'm fighting with the type-checker to transform this abstract expression into this other abstract expression (that's really what a proof is when it boils down to it), completely forgetting about the mathematical meaning, just moving puzzle pieces around with obscure tools.

And even after all of this, it is so much more ergonomic than the previous generation of proof-assistants :)

---

I think that the main reasons for Lean's complexity are:

- That it has a very purist functional style and syntax, literally reflecting the Curry-Howard Correspondence (function = proof), rather than trying to bridge the gap to more familiar maths notation.

- That it aims to be a proof assistant, it is fundamentally semi-automatic and interactive, this makes it a hard design challenge.

- A lot of the complexity is aimed at giving mathematicians the tools to express real research maths in it, on this it has been more successful than any alternative.

- Because of this it has at least 5 different languages embedded in it: functional expressions of theorems, forward proofs with expression transformations, backward proofs with tactics, the tactics metaprogramming macro language, and the language to define data-types (and at least 4 kinds of data-types with different syntax).

Ah, thanks for the clarification. Then the whole thing makes a lot more sense - though I'd say the outlook also becomes more optimistic.

I thought the rhetoric sounded somewhat like the AGI/accelerationist folks who postulate some sort of eventual "godlike" AI whose thought processes are somehow fundamentally inaccessible to humans. So if you had a proof that was only understandable to this sort if AIs, then mathematics as a discipline of understanding would be over for good.

But this sounds like it would at least theoretically let you tackle the proof? Like, it's imaginable that some AI generates a proof that is several TB (or EB) in size but still validates - which would of course be impossible to understand for human readers in the way you can understand a paper. But then "understanding" that proof would probably become a field of research of its own, sort of like the "BERTology" papers that try to understand the semantics of specific hidden states in BERT (or similar approaches for GPTs).

So I'd see an incomprehensible AI-generated proof not as the end of research in some conjecture, but more as a sort of guidance: Unlike before, you now know that the treasure chest exist and you even have its coordinates, you just don't have the route to that location. The task then becomes about figuring out that route.

In terms of having the proofs be understandable to humans, I'd say that's still a challenge. Not a fundamental challenge, but a real practical challenge still.

Lean makes the proofs verifiable, and sure to a degree understandable, but normal Lean proofs written by humans are already a challenge to understand without additional documentation, and, as with all code-generation, AI generated proofs are probably even harder to understand. There are many paths to arrive at a proof, the AI may take very messy routes that don't align with how humans create abstractions and assign meaning to break the process down into understandable steps.

In a sense all AI is understandable, nothing is hidden, you can see all the numbers inside a Transformer. Just being able to see the code might not necessarily help mathematicians too much in extracting deeper meaning and lessons from AI-generated proofs, at least not without significant additional work.

There's an argument to be made that incomprehensible proofs are still very useful.

I understand that mathematicians are all about understanding why a theorem is true, not just checking that it is, and then using that understanding to push forward other research. Same as most scientists care more about understanding how the world works rather than developing methods to control and manipulate it.

But proven theorems are mathematical tools that can be used to make progress, regardless of your understanding of how they work. I think there is a path forward for maths as an engineering discipline, possibly with much faster progress, just as computer science expanded into software engineering. AI is just the next step of many, software libraries, interfaces and abstractions are also black-boxes in a sense that do stuff for you without knowing how.

A good engineer learns to build on the shoulders of giants, mastering the understanding of what tools do and how they behave, without necessarily needing to know how they are built from scratch. It might be less satisfying to the curious, but you can get a lot more done.

Incomprehensible proofs are indeed still useful to some extent, and I don't think you'll find many mathematicians who would reject them as an answer to the binary question of whether the result is true.

But when you talk about "getting a lot more done," I want to ask, get a lot more done to what end? Despite what mathematicians sometimes write in their grant applications, resolving most of the big open problems in the field probably won't lead to new technologies or anything. To use the Riemann Hypothesis example again, most number theorists already think it's probably true, and there are a lot of papers being published already which prove things like "if the Generalized Riemann Hypothesis is true, then [my new result]".

No one is really waiting around just for the literal, one-bit answer to the question of whether RH is true; if we got that information and nothing else, I'm sure number theorists would be happy to know, but not a whole lot about the work being done in the field would change. It's not just being "satisfying to the curious"; virtually the entire reason we want a proof is to use the new ideas it would presumably contain to do more mathematics. This is exactly what's happened with the proof of the Poincare Conjecture, the only one of the Millennium Problems that's been resolved so far.

This is what I was lamenting in my comment earlier: the thing you're describing, where we set proof-finding models to work and they spit out verifiable but totally opaque proofs of big open problems in math, very well might happen someday, but it wouldn't actually be all that useful for anything, and it would also mean the end of the only thing about the whole enterprise that the people working in it actually care about.

Yeah, I imagine in those situations, an AI proof that the conjecture is false would probably be more interesting and useful than a proof that it is true.

A proof of the conjecture would essentially just move the situation from "we think there could be counterexamples, but so far we haven't found any" to "there really are no counterexamples anywhere, you can stop looking". The interesting thing here would be the explanation why there can be no counterexamples, which is exactly the thing that the proof wouldn't give you.

On the other hand, a counterproof would either directly present the community with a counterexample - and maybe reveal some interesting class of objects that was overlooked so far - or at least establish that there have to be counterexamples somewhere, which would probably give more motivation to efforts of finding one.

(Generally speaking here. I'm not a mathematician and I can't really say anything about the hypotheses in question)

Yeah, that's definitely right --- an explicit counterexample to the Riemann Hypothesis would be very surprising and interesting, and I think that would be equally true no matter whether it was found by a person or a computer! The situation that would be mostly unhelpful is a certificate that the result is true that communicates nothing about why.

> Or more generally, how do you verify a proof that you don't understand?

This is the big question! Computer-aided proof has been around forever. AI seems like just another tool from that box. Albeit one that has the potential to provide 'human-friendly' answers, rather than just a bunch of symbolic manipulation that must be interpreted.

oersted's answer basically covers it, so I'm mostly just agreeing with them: the answer is that you use a computer. Not another AI model, but a piece of regular, old-fashioned software that has much more in common with a compiler than an LLM. It's really pretty closely analogous to the question "How do you verify that some code typechecks if you don't understand it?"

In this hypothetical Riemann Hypothesis example, the only thing the human would have to check is that (a) the proof-verification software works correctly, and that (b) the statement of the Riemann Hypothesis at the very beginning is indeed a statement of the Riemann Hypothesis. This is orders of magnitude easier than proving the Riemann Hypothesis, or even than following someone else's proof!

> While mathematicians traditionally pursue understanding for its own sake, industry researchers must ultimately deliver products, features, or capabilities that create value for their organizations.

This really isn't about mathematics or AI, this is about the gap between academia and business. The academic wants to pursue knowledge for the sake of knowledge, while a business wants to make money.

Compare to computer science or engineering, where business has near completely pervaded the fields. I've never heard anybody lamenting their inability to "pursue understanding for its own sake" and when someone does advance the theory, there's also a conversation about how to make it profitable. The academic aspect isn't gone, but it's found a way to coexist with the business aspect, for better or worse.

Honestly it sounds like mathematicians have had things pretty good if this is one of their biggest complaints.

If AI can prove major theorems, it will likely by employing similar heuristics as the mathematical community employs when searching for proofs and understanding. Studying AI-generated proofs, with the help of AI to decipher contents will help humans build that 'understanding' if that is desired.

An issue in these discussions is that mathematics is both an art, a sport, and a science. And the development of AI that can build 'useful' libraries of proven theorems means different things for each. The sport of mathematics will be basically over. The art of mathematics will thrive as it becomes easier to explore the mathematical world. For the science of mathematics, it's hard to say, it's been kind of shaky for ~50 years anyway, but it can only help.

Fundamentally, mathematics is about understanding why something is true or false.

Modern AI is about "well, it looks like it works, so we're golden".

I did a fair bit of applied mathematics at uni

What I think Mathematicians should remind themselves is a lot of prestigious mathematicians, the likes of Cantor or Erdos, often only employed a handful of “tricks”/heuristics for their proofs over their career. They repeatedly and successfully applied these strategies into unsolved problems

I argue would not take a tremendous jump in performance for an AI to begin their own journey similar in kind to the greats, the only thing standing in their way (as with all contemporary mathematicians) is the extreme specialisation required to reach the boundary of unsolved problems

AI need not be Euler to be an important tool and figure within mathematics

What I think Mathematicians should remind themselves is a lot of prestigious mathematicians, the likes of Cantor or Erdos, often only employed a handful of “tricks”/heuristics for their proofs over their career.

I know this claim is often made but it seems obvious that in this discussion, trick means something far wider and more subtle than any set computer program. In a lot of ways, "he just uses a few tricks" is akin to the way a mathematician will say "and the rest of the proof is elementary" (when it's still quite long and hard for anyone not versed in a given specialty). I mean, before category theory was formalized, the proofs that now are possible with it might classified as "all done with this trick" but grasping said trick was far from elementary matter.

I argue would not take a tremendous jump in performance for an AI to begin their own journey similar in kind to the greats, the only thing standing in their way (as with all contemporary mathematicians) is the extreme specialisation required to reach the boundary of unsolved problems.

Not that LLMs can't do some impressive things but your narrative seems to anthropomorphize them in a less than useful way.

As a mathematician, I can't help but simmer each time I find the profession's insistence on grasping the how's and why's of matters to be dismissed as pedantry. Actionable results are important but absent understanding, we will never have any grasp on downstream impact of such progress.

I fear AI is just going to lower our general epistemic standards as a society, and we forget essential truth verifying techniques in the technical (and other) realms all together. Needless to say the impact this has on our society's ethical and effectively legal foundations, because ultimately without clarity on how's and why's it will be near impossible to justly assign damages.

I find this cultural divide exists predominantly among mathematicians who consider existence proofs as real mathematics.

Mathematicians who practice constructive math and view existence proofs as mere intellectual artifacts tend to embrace AI, physics, engineering and even automated provers as worthy subjects.

I agree with the overt message of the post — AI-first folks tend to think about getting things working, whereas math-first people enjoy deeply understood theory. But I also think there's something missing.

In math, there's an urban legend that the first Greek who proved sqrt(2) is irrational (sometimes credited to Hippasus of Metapontum) was thrown overboard to drown at sea for his discovery. This is almost certainly false, but it does capture the spirit of a mission in pure math. The unspoken dream is this:

~ "Every beautiful question will one day have a beautiful answer."

At the same time, ever since the pure and abstract nature of Euclid's Elements, mathematics has gradually become a more diverse culture. We've accepted more and more kinds of "numbers:" negative, irrational, transcendental, complex, surreal, hyperreal, and beyond those into group theory and category theory. Math was once focused on measurement of shapes or distances, and went beyond that into things like graph theory and probabilities and algorithms.

In each of these evolutions, people are implicitly asking the question:

"What is math?"

Imagine the work of introducing the sqrt() symbol into ancient mathematics. It's strange because you're defining a symbol as answering a previously hard question (what x has x^2=something?). The same might be said of integration as the opposite of a derivative, or of sine defined in terms of geometric questions. Over and over again, new methods become part of the canon by proving to be both useful, and in having properties beyond their definition.

AI may one day fall into this broader scope of math (or may already be there, depending on your view). If an LLM can give you a verified but unreadable proof of a conjecture, it's still true. If it can give you a crazy counterexample, it's still false. I'm not saying math should change, but that there's already a nature of change and diversity within what math is, and that AI seems likely to feel like a branch of this in the future; or a close cousin the way computer science already is.

PS After I wrote my comment, I realized: of course, AI could one day get better at the things that make it not-perfect in pure math today:

* AI could get better at thinking intuitively about math concepts. * AI could get better at looking for solutions people can understand. * AI could get better at teaching people about ideas that at first seem abstruse. * AI could get better at understanding its own thought, so that progress is not only a result, but also a method for future progress.

> The last mathematicians considered to have a comprehensive view of the field were Hilbert and Poincaré, over a century ago.

Henri Cartan of the Bourbaki had not only a more comprehensive view, but a greater scope of the potential of mathematical modeling and description

I would also add Grothendieck to that list.

Grothendieck did not have a comprehensive view of mathematics, nor he ever claimed to. There are vast swathes of mathematics (e.g. PDE or probability) that never fell under Grothendieck's radar

> One striking feature of mathematical culture that came up was the norm of alphabetical authorship. […] There are some exceptions, like Adleman insisting on being last in the RSA paper.

lol, took me a second to get the plausible reason for that

Terence Tao recently gave a lecture on Machine Assisted Proofs that helped even common folk like me to understand on the upcoming massive changes to Math within the next decade. Especially, its fascinating to see how AI and especially Lean might provide an avenue for large scale collaboration in Math Research, to bring them on par with how research is done in other sciences

https://www.youtube.com/watch?v=5ZIIGLiQWNM

> Unlike many scientific fields, mathematics has no concept of "first author" or "senior author"; contributors are simply listed alphabetically.

I don't think this is (generally) true? Speaking as a math postdoc right now, at least in my field of computational mathematics there's definitely a notion of first author. Though, a note of individual contributions at the bottom of the paper is becoming more common.

I was an algebraic geometer when I was still doing research in the field, and it was definitely true in that corner of the world. Authors are alphabetical, and you usually cite the paper by listing them all, no "et al"'s. I think I didn't even know there was such a thing as "first author" until I worked in ML.

Computational mathematics may have adopted conventions from computer science, but I assure you that the alphabetical order convention is definitely the standard in most pure mathematics (with some exceptions, such as the Ascoli-Arzelà theorem)

> A revealing anecdote shared at one panel highlighted the cultural divide: when AI systems reproduced known mathematical results, mathematicians were excited, while AI researchers were disappointed

This seems very caricatural, one thing I've often heard in the AI community is that it'd be interesting to train models with an old data cutoff date (say 1900) and see whether the model is able to reinvent modern science

Is it really a culture divide or is it an economic incentives divide? Many AI researchers are mathematicians. Any theoretical AI research paper will typically be filled with eye-wateringly dense math. AI dissolves into math the closer you inspect it. It's math all the way down. What differs are the incentives. Math rewards openness because there's no real concept of a "competitive edge", you're incentivized to freely publish and share your results as that is how you get recognition and hopefully a chance to climb the academic ladder. (Maybe there might be a competitive spirit between individual mathematicians working on the same problems, but this is different than systemic market competition.) AI is split between being a scientific and capitalist pursuit; sharing advances can mean the difference between making a fortune or being outmaneuvered by competitors. It contaminates the motives. This is where the AI researcher's typical desire for "novel results" comes from as well, they are inheriting the values of industry to produce economic innovations. It's a tidier explanation to tie the culture differences to material motive.

> Many AI researchers are mathematicians. Any theoretical AI research paper will typically be filled with eye-wateringly dense math. AI dissolves into math the closer you inspect it. It's math all the way down.

There is a major caveat here. Most 'serious math' in AI papers is wrong and/or irrelevant!

It's even the case for famous papers. Each lemma in Kingma and Ba's ADAM optimization paper is wrong, the geometry in McInnes and Healy's UMAP paper is mostly gibberish, etc...

I think it's pretty clear that AI researchers (albeit surely with some exceptions) just don't know how to construct or evaluate a mathematical argument. Moreover the AI community (at large, again surely with individual exceptions) seems to just have pretty much no interest in promoting high intellectual standards.

I'd be interested to read about the gibberish in UMAP, I know the paper "An improvement of the convergence proof of the ADAM-Optimizer" for the lemma problem in the original ADAM but hadn't heard of the second one. Do you have any further info on it?

> Each lemma in Kingma and Ba's ADAM optimization paper is wrong

Wrong in the strict formal sense or do you mean even wrong in “spirit”?

Physicists are well-known for using “physicist math” that isn’t formally correct but can easily be made as such in a rigorous sense with the help of a mathematician. Are you saying the papers of the AI community aren’t even correct “in spirit”?

Much physicist math can't be made rigorous so easily! Which isn't to say that much of it doesn't still have great value.

However the math in AI papers is indeed different. For example, Kingma and Ba's paper self-presents as having a theorem with a rigorous proof via a couple of lemmas proved by a chain of inequalities. The key thing is that the mathematical details are purportedly all present, but are just wrong.

This isn't at all like what you see in physics papers, which might just openly lack detail, or might use mathematical objects whose existence or definition remain conjectural. There can be some legitimate problems with that, but at least in the best cases it can be very visionary. (Mirror symmetry is a standard example.) By contrast I'm not sure what 'spirit' is even possible in a detailed couple-page 'proof' that its authors probably don't even fully understand. In most cases, the 'theorem' isn't remotely interesting enough as pure mathematics and is also not of any serious relevance to the empirical problem at hand. It just adds an impressive-looking section to the paper.

I do think it's possible that in the future there will be very interesting pure mathematics inspired by AI. But it hasn't been found yet, and I'm very certain it won't come from reconsidering these kinds of badly-written theorems and proofs.

Amazing! I looked into your ADAM claim, and it checks out. Thanks! Now I'm curious. I you have the time, could you please follow up with the 'etc...'?

There's a related section about 'mathiness' in section 3.3 of the article "Troubling Trends in Machine Learning Scholarship" https://arxiv.org/abs/1807.03341. I would say the situation has only gotten worse since that paper was written (2018).

However the discussion there is more about math which is unnecessary to a paper, not so much about the problem of math which is unintelligible or, if intelligible, then incorrect. I don't have other papers off the top of my head, although by now it's my default expectation when I see a math-centric AI paper. If you have any such papers in mind, I could tell you my thoughts on it.

> As Gauss famously said, there is "no royal road" to mathematical mastery.

This is not the point, but the saying "there is no royal road to geometry" is far older than Gauss! It goes back at least to Proclus, who attributes it to Euclid.

I never understood that quote until recently.

The story goes that the (royal) pharaoh of Egypt wanted to learn geometry, but didn't want to have to read Euclid. He wanted a faster route. But, "there is no royal road to geometry."

The last Egyptian pharaoh was Nectanebo II, who ruled from 358 to approximately 340 BC. Alexander founded Alexandria in 331 BC as the crown jewel of his empire where Euclid wrote his magnum opus, The Elements in 300 BC!

Unless the royal pharaoh of Egypt, refers to Ptolemy I Soter, Macedonian general who was the first Ptolemaic Kingdom ruler of Egypt after Alexander's death.

Yep, exactly. Here's a translation of Proclus:

"He [Euclid] lived in the time of Ptolemy the First, for Archimedes, who lived after the time of the first Ptolemy, mentions Euclid. It is also reported that Ptolemy once asked Euclid if there was not a shorter road to geometry that through the Elements, and Euclid replied that there was no royal road to geometry."

Source: http://aleph0.clarku.edu/~djoyce/java/elements/Euclid.html

I think a lot of this is not so much "math vs. AI" as "anyone who cares about anything other than making as much money as possible vs. anyone who only cares about making as much money as possible".

If AI-generated proofs become incomprehensible to humans, do they still count as -math- in the traditional sense?

We already have proofs by exhaustion that could only ever be verified using computers. Some people would argue they are not "elegant" but I don't think anyone would argue they are not math.

But I wonder if there's a distinction between a proof that is merely computationally intensive and one that is conceptually inaccessible

This is written in the first person, but there is no listed author and the website does not suggest an author...

It's in the usual location at the top of the page: "By Ralph Furman".

You can't fake influence

If you look closely at the history of mathematics you can see that it worked similarly to current AI in many respects (not so much the secrecy) - people were oftentimes just concerned with whether something worked rather than why it worked (eg so that they could build a building or compute something), and the full theoretical understanding of something sometimes came significantly later than the knowledge of whether something was true or useful.

In fact, the modern practice (the concept predates the practice of course, but was more of an opinion than a ritual) of mathematics as this ultimate understandable system of truth and elegance seemingly began in Ancient Greece with their practice of proofs and early development of mathematical "frameworks". It didn't reach its current level of rigor and sophistication until 100-150 years ago when Formalism became the dominant school of thought (https://en.wikipedia.org/wiki/Formalism_(philosophy_of_mathe...), spearheaded by a group of mathematicians who held even deeper beliefs that are often referred to as Mathematical Platonism (https://en.wikipedia.org/wiki/Mathematical_Platonism). (Note that these wikipedia articles are not amazing explanations of the concepts, how they relate to realism, or developed historically but they are adequate primers)

Of course, Godel proved that truths exists outside of these formal systems (only a couple decades after mathemticians had started building a secret religion around worshipping Logos. These beliefs were pervasive see eg Einsteins concept of God as a clockmaker or Erdos' references to "The Book"), which leaves us almost back where we started where we might need to consider there may be some empirical results and patterns which "work" but we do not fully understand - we may never understand them. Personally, I think this philosophically justifies not subjecting oneself to the burden of spending excess time understanding or proving things that have never been understood before - it may elude elegance (as the 4-color proof) or even knowability.

We can always look backwards and explain things later, and of course, it's a false dichotomy that some theorems or results must be fully understood and proven (or proven elegantly) before they can be considered true and used as a basis for further results. Perhaps it is unsatisfying to those who wish to truly understand the universe in terms of mathematical elegance, but that asshole used mathematical elegance to disprove mathematical elegance as a perfect tool for understanding the universe already, so take it up with him.

Personally, as someone who at one time heavily considered pursuing a life in mathematics in part because of its ability to answer deep truths, I think Godel set us free: to understand or know things, we cannot rely solely on mathematics. Formal mathematics itself tells us that there are things we can only understand by discovering them, building them, or experimenting with them. There are truths that Cuda Cowboys can uncover that LaTex Liturgy cannot

> This quest for deep understanding also explains a common experience for mathematics graduate students: asking an advisor a question, only to be told, "Read these books and come back in a few months."

With AI advisor I do not have this problem. It explains parts I need, in a way I understand. If I study some complicated topic, AI shortens it from months to days.

I was somehow mathematically gifted when younger, sadly I often reinvented my own math, because I did not even know this part of math existed. Watching how Deepseek thinks before answering, is REALLY beneficial. It gives me many hints and references. Human teachers are like black boxes while teaching.

Its not too late to hope for the current crop of LLMs to give rise to a benevolent, patient science based educator, like the "Young Ladies Illustrated Primer" of Neal Stephensons Diamond Age.

We clearly will soon have the technology for that .. but it requires a rich opinionated benefactor, or inspired government agency to fund the development .. or perhaps it can be done as an Open model variant through crowdsourcing.

An LLM personal assistant that detects my preferences and echoes my biases and massages my ego and avoids challenging me with facts and new ideas .. whose goal is to maximize screentime and credits for shareholder value .. seem to be where things are heading.

I guess this is an argument for having open models.

I suspect you probably don't understand it after that. You think you do.

I thought I understood calculus until I realised I didn't. And that took a bit thwack in the face really. I could use it but I didn't understand it.

I think you’re missing the point of what the advisor is saying.

No, I get it.

My point is human advisor does not have enough time, to answer questions and correctly explain the subject. I may get like 4 hours a week, if lucky. Books are just a cheap substitute for real dialog and reasoning with a teacher.

Most ancient philosophy papers were in form of dialog. It is much faster to explain things.

AI is a game changer. It shortens feedback loop from a week to hour! It makes mistakes (as humans do), but it is faster to find them. And it also develops cognitive skills while finding them.

It is like programming in low level C in notepad 40 years ago. Versus high level language with IDE, VCS, unit tests...

Or like farming resources in Rust. Booring repetitive grind...

Books aren't just a lower quality version of dialog with a person though. They operate entirely differently. With very few people can you think quietly for 30 minutes straight without talking, but with a book you can put it down and come back to it at will.

I don't think professional programmers were using notepad in 1985. Here's talk of IDEs from an article from 1985: https://dl.acm.org/doi/10.1145/800225.806843 It mentions Xerox Development Environment, from 1977 https://en.wikipedia.org/wiki/Xerox_Development_Environment

The feedback loop for programming / mathematics / other things I've studied was not a week in the year 2019. In that ancient time the feedback look was maybe 10% slower than with any of these LLMs since you had to look at Google search.

The point is that time and struggle are required for understanding. The advisor isn’t telling the student to go read these books because he doesn’t have time to explain.

He’s saying go read these books, wrestle with the ideas, go to bed, dream about them, think about them in the shower. Repeat that until you understand enough to understand the answer.

There’s no shortcut here. If you had unlimited time with the advisor he couldn’t just sit you down and make you understand in a few sessions.

Mathematics is, IMO, not the axioms, proofs, or theorems. It's the human process of organizing these things into conceptual taxonomies that appeal to what is ultimately an aesthetic sensibility (what "makes sense"), updating those taxonomies as human understanding and aesthetic preferences evolve, as well as practical considerations ('application'). Generating proofs of a statement is like a biologist identifying a new species, critical but also just the start of the work. It's the macropatterns connecting the organisms that lead to the really important science, not just the individual units of study alone.

And it's not that AI can't contribute to this effort. I can certainly see how a chatbot research partner could be super valuable for lit review, brainstorming, and even 'talking things through' (much like mathematicians get value from talking aloud). This doesn't even touch on the ability to generate potentially valid proofs, which I do think has a lot of merit. But the idea that we could totally outsource the work to a generative model seems impossible by definition. The point of the labor is develop human understanding, removing the human from the loop changes the nature of the endeavor entirely (basically to algorithm design).

Similar stuff holds about art (at a high level, and glossing over 'craft art'); IMO art is an expressive endeavor. One person communicating a hard-to-express feeling to an audience. GenAI can obviously create really cool pictures, and this can be grist for art, but without some kind of mind-to-mind connection and empathy the picture is ultimately just an artifact. The human context is what turns the artifact into art.

AI is young, and at the center of the industry spotlight, so it attracts a lot of people who are not in it to understand anything. It's like when the whole world got on the Internet, and the culture suddenly shifted. It's a good thing; you just have to dress up your work in the right language, and you can get funding, like when Richard Bellman coined the term "dynamic programming" to make it palatable to the Secretary of Defense, Charles Wilson.

AI has been around since at least the 1970s.

Not in any way that is relevant to the conversation about AI that has exploded this decade

Or 1949 if you consider the Turing Test, or 1912 if you consider Torres Quevedo's machine El Ajedrecista that plays rook endings. The illusion of AI dates back to 1770's The Turk.

"[The Analytical Engine] might act upon other things besides number, were objects found whose mutual fundamental relations could be expressed by those of the abstract science of operations, and which should be also susceptible of adaptations to the action of the operating notation and mechanism of the engine...Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent." - Ada Lovelace, 1842

Yes, and all of these dates would be considered "young" by most mathematicians!

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Imo mathematicians want to be very smart, when a lot of ai is actually easy to undertand with good abstract and logic thinking and linear algebra.

I've known a few mathematicians, and none of them cared about being perceived as smart. They wanted to figure stuff out, which, incidentally, tended to actually make them smart.

whaa ? have you read the article ?