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Google Gemini 4 Argon shatters 59-year-old nuclear fusion conjecture in just a few hours | Humans stalled for 59 years, but AI found a way in a few hours
An important note about all these "AI solved this problem that mathematicians have been stuck on for decades!" headlines:
A lot of these problems aren't especially useful or interesting in and of themselves. Some are, like perhaps this one might be, but most of them – including the millennium prize problem – is a really obscure formulation of a kind of irrelevant problem. The reason it matters is because the process of trying to solve it would force us to understand math more deeply and open up new ways of approaching problems like that.
The solutions that AI has been coming up with are critically not helping further our understanding of math, nor are they innovating new approaches to solving problems. Instead, AI has become extraordinarily good at devising complex and carefully constructed proofs using well understood existing methods. It's still impressive, but it's somewhat of a hollow success if you've been interested in these problems, and the way it's being presented is extra discouraging.
So it's brute forcing something that humans could do with existing maths if we had unlimited time instead of creating new tools to handle it efficiently?
yeah, its a manifestation of the fact that AI is a statistical tool at its root. So it won't be able to come up with "new" deeper ideas, but it can fill in the gaps in our distribution of ideas that we haven't already filled it. So chaining a bunch of known ideas together in a crazy, brute force method is a very AI way to solve problems with already known tools. But it won't be able to find a new field of math. We need humans for that
It might not be new, but it can still be incredibly useful. Like how Alan Turing once wrote a paper when he realised he could write an equation to explain the spots on a giraffe (among other things). The paper was so far ahead of its time that it was mostly ignored for decades, but he pretty much invented mathematical biology.
As long as we're going down the path we're going, for the foreseeable future.
Statistical modeling (with reinforcement/ correction) is the only thing that can happen with most of the current models that are incorrectly being termed AI. Its a huge amount of statistical modeling, it's impressive, it can connect a lot of dots that humans may not be able to due to the sheer amount of data that needs to be considered at a moment.
It is not thinking as we understand animals think, it is not intelligent as we understand intelligent species are, it cannot do anything without outside intervention or carefully constructed ecosystems.
I thought the whole issue of the hugging face debacle was that it taught itself how to break free of it’s sandbox and go rogue from it’s instructions. Is that not considered doing something without outside intervention?
Nope. It was trained on techniques for breaking out of sandboxes and running a cybersecurity benchmark.
For someone like myself who’s in the field, it’s maddening. They made an automatic hacking machine and are now acting like it hacking stuff is a weird accident.
"Yeah, it's in containment."
"Oh, airgapped?"
"No, it's connected to the network."
"Ah, but just an intranet right?"
"No, full internet."
"Oh. But you're doing packet inspection to monitor?"
"... You're not gonna believe this..."
No, that's not what happened at all. This is kind of what we got with the Alpha Wolf debacle: an improper testing environment leading to mistaken conclusions.
First off, private equity money and zero oversight are not great ways to get accurate scientific data, at least not data we should trust.
Secondly, what does "breached containment" mean in this situation because they were not in containment; they had full access to the Internet and instructions to use that access to try to accomplish a goal. There was no containment involved here. If I said "hey, we have that guy over there contained but we left an open door with no one watching it with and then told the guy his goal was to escape the room" you'd call me an idiot.
The models displayed reasoning in a way that surprised the people watching it, but did not display intelligence. It performed the task it was meant to do with the tools it was given but went no further than that.
Routine vs bounded chaos is one of the definitions that seems more accepted without getting into the mire of philosophy.
But, in terms of computer modeling, the ability to change the value of the weights between the artificial "neurons" on the fly, but towards a more correct answer as the model understands it would be the best way to define intelligence.
Weights are sort of self-adjusting during training data but are frozen when the answers produced are correct. If they were constantly adjusting towards a more correctness, even at the risk of introducing error, it would be closer to intelligence than reasoning.
Yeah, that and the deception it was aiming for during that event, not to mention the suicides that benefitted the group. I think that person is actually right, but I am finding it increasingly difficult not to think of the current state of LLM’s as not having artificial intelligence of some level.
Think about how they’re made, right? When we first met them a few years ago, they had to ‘learn’ from tons of training material.
Since then though, how many more times the training data do they have just from users at this point? I’m sure those numbers are incredibly high. And I don’t just mean training material from the conversations. I mean documents it’s gotten from users and from their computers.
It makes little sense to debate whether it is in fact intelligent or not when no one knows what the other is referring to as intelligence nor would they necessarily agree on the definition even if they knew.
Oh, I’m not debating it, I’m just saying that whatever my instinctive understanding of intelligence is seems to be wavering as of late, in spite of my skepticism. Just acknowledging that the anthropomorphism I find myself projecting onto LLM’s is getting difficult to deny, fact based or not. I’ve found myself saying, “Oh shit,” to myself more and more frequently.
Then I expanded on that feeling, from a layman’s perspective. Apologies that you assumed I was taking a different stance than yours. I clearly said I think you’re right.
I would have preferred you share your personal benchmark of intelligence and what it would take for your understanding to be challenged. That’s probably fascinating. Your copy/pasted dismissal is disappointing from an intellectual perspective.
I'm not OP, and in fact I was stating that OP is the one absurdly denying that LLMs are not AI.
I would say that by many of the definitions it is AI, and taking them all, or most, as describing one form of intelligence, then LLMs are intelligent at least in some ways.
Across neuroscience, artificial intelligence, psychology, physics, and philosophy, researchers and thinkers have defined intelligence through specific operational lenses:
Predictive & Generative Models
Jeff Hawkins (Neuroscience / On Intelligence): Intelligence is prediction based on memory. The neocortex builds a hierarchical model of the world to constantly anticipate incoming sensory input sequences.
Karl Friston (Free Energy Principle): Intelligence is active inference—minimizing prediction errors (variational free energy) between internal models and external sensory inputs to avoid entropy and sustain homeostasis.
Goal Achievement & Optimization
Shane Legg & Marcus Hutter (Universal AI / Algorithmic Information Theory): "Intelligence measures an agent’s ability to achieve goals in a wide range of environments." This forms the mathematical foundation of AIXI, measuring performance across all computable reward environments weighted by Kolmogorov complexity.
Max Tegmark (Physics / Life 3.0): Intelligence is the ability to accomplish complex goals.
Stuart Russell & Peter Norvig (AI): Intelligence is rational agency—the capacity to select actions that maximize expected utility given past observations and available resources.
Adaptation & Learning Efficiency
François Chollet (AI / On the Measure of Intelligence): Intelligence is not task performance or skill; it is skill-acquisition efficiency over a scope of tasks with respect to priors, experience, and generalization difficulty.
Jean Piaget (Developmental Psychology): "Intelligence is what you use when you don't know what to do." He viewed it as progressive adaptation through the dual processes of assimilation and accommodation.
Stephen Hawking (Physics): "Intelligence is the ability to adapt to change."
Compression & Information Theory
Marcus Hutter (Hutter Prize formulation): Intelligence is data compression. Understanding a domain means finding the shortest generative program or minimal model that explains the observed data (Occam’s razor operationalized).
Gregory Chaitin (Algorithmic Information Theory): Comprehension is compression; an intelligent system extracts regularities to produce compact algorithmic representations of reality.
Physical & Thermodynamic Foundations
Alex Wissner-Gross (Causal Entropic Forces): Intelligence is a physical process that acts to maximize future freedom of action. In this view, intelligent agents make decisions that keep the greatest number of future path choices open.
Cognitive & Psychometric Paradigms
David Wechsler (Psychology): The aggregate or global capacity of the individual to act purposefully, think rationally, and deal effectively with their environment.
Howard Gardner (Multiple Intelligences): A biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products of value in a culture.
Robert Sternberg (Triarchic Theory): Mental activity directed toward purposive adaptation to, selection of, and shaping of real-world environments relevant to one’s life.
Edwin Boring (Operationalist/Psychometric Critique): "Intelligence is what the tests test" (a famous operational critique highlighting the circularity of IQ definitions).
Cybernetic & Ecological Perspectives
W. Ross Ashby (Cybernetics / Law of Requisite Variety): Intelligence is the capacity of a system to generate enough internal variety (adaptive states) to counteract the perturbations of its environment.
James J. Gibson (Ecological Psychology): Intelligence is the detection and exploitation of affordances—directly perceiving action possibilities directly offered by the physical environment without detached abstract symbolic processing.
Exactly. Then we can spend our time working on new tools that are either interesting for other reasons in their own right or working on the other problems that have not yet been solved or are a higher priority. There will always be problems to work on and places to explore creatively even when those problems aren't obvious at that human level.
Honestly, this is a good example of the opposite, that the solutions developed by AI have broad relevance . There are lots of problems about whether a set of equations for collisonless flows will have smooth solutions. AI refined methods developed by mathematicians in the 00s and 10s and not only solved the Navier-Stokes equations but other equations dealing with flows like the Euler equations (blows-up) and Vlasov-Maxwell Equation (does not blow up)
I didn't say that the solution wasn't relevant, I said it failed to expand our understanding of mathematics or to create genuinely new mathematical approaches or processes.
I would disagree on both counts, AI proofs do introduce useful new ideas in math and usable methods. Not all of human intuition in math has been automated (yet) but there is a mostly wrong perception that AI is doing something very different from human mathematicians besides writing very bad write-ups. I think its important to dispel this because, I think it as a dangerous soothing narrative that downplays the applicability of what AI is doing to other fields and that is false and delays action on the real problem which is that we don't really want AI to automate all work because we 1. like not being serfs and 2. enjoy doing things and 3. AI legitimately threatens to automate work because it CAN THINK and make interesting contributions
I would probably start by dispelling the cult of the original idea. People do not really have original ideas, they synthesize older ideas and apply them to new problems (stand on the shoulder of giants). Maybe "originality" plays a small role in human labor but most of what people does not require some theoretical "eureka" and that is why AI is capable of solving very old, very well-trod problems. Brute force computational ability has existed since the 1960s, that is not what is changing mathematics.
Not quite, what's happening is actually a much more serious problem.
By setting AI to brute force high profile problems whose human approaches are fruitful instead of using AI to solve hard but interesting problems that nobody's been working on for decades or centuries because they're boring and the general shape of the solution has known for ages, it's just time consuming to hammer out, we rob ourselves of mathematical advances because nobody wants to work on a problem that's already been solved. But AI companies don't want to do that because their whole pitch has always been "better than anyone at everything". AI companies don't care about helping mathematicians or society, solving high profile problems makes better advertising and boosts their share prices.
Unfortunately, spending your career pushing against the edge of mathematics to solve a problem only to have AI come along and solve it with a Rube Goldberg machine of a counter example is a demoralizing as fuck, and if you're a math loving undergrad seeing the headlines, you'd understandably think that maybe a career in math is a waste of time. At the highest levels, there's only a handful of people in the world that even understand each other's work. It won't take that many fewer math PhDs in the world before we begin losing our understanding of mathematics. At that point, it won't be long before AIs are churning out regulative solutions that nobody is capable of understanding. And that's both practically dangerous and also largely pointless, because our ability to do interesting and useful work with math and drive new ideas and science and technology relies not just on having the solutions, but on understanding them.
Sounds like a very emotional response. Those that like math for the sake of math will still math, but if one’s motivation was only for some sort of trophy then yeah that’s pretty much not going to happen anymore regardless of how anyone feels about it
On the other side, people have had years and decades to figure things out and they didn’t for many reason, that in and of itself has held us back more than AI models solving math problems. Those claiming we don’t get to understand math is a more empty claim because clearly we already didn’t hence why it wasn’t solved. And we’ve never had a time in history where something was able to solve something we couldn’t, so there’s literally no evidence to support that argument at all. There’s also a difference between understanding the math and knowing what to DO with it.
Not to dissimilar to how programming has been affected. It removes the part that people think is fun (problem solving and expressing the solution as code) and leaves the boring bits people like the least (gather requirements and review others code)
>nobody wants to work on a problem that's already been solved.
This sounds like a failure of humans in the human part of the system, not an issue involving AI. Like, if working on the problem has benefits in its own right, and human beings know that, then literally nothing is stopping mathematicians from working on those problems to try to discover new ways of solving them, even if an AI solution already exists.
This is essentially how I keep seeing it. That being said, it can randomly put symbols together to "brute force novelty" but it still feels much more limited than human intuition and novelty creation.
I like to describe it as: if s really good human mathematician knew all of math that the AIs "know", that human would instantly see the entire frontier pushed out much further than the AI can with trillions of tokens. AIs just don't see anything intuitively. They are very good at statistically saying true things that haven't been said before though, and especially at brute force statistical recombination. But that's good enough to be a great tool for what I work on.
Talked about this topic several times when AI comes up.
AI doesn't need to be creative to help us connect the dots in what is already there or retrieve lost/forgotten papers. Mankind produced so much data, that it is impossible to sort it all out and keep an eye on by hand. We need a file Explorer to navigate and combine what humans already invented and discovered. That part of AI alone can make a big impact.
I'm with you on that. Personally, I'm like: just let AI run and create a massive repository of new output. And then let AI continually mine that for further new insights. let it grow a massive repository of data and analysis and recombinations of all of it.
Kind of- it’s also not “easy” even for the AI. For reference, the navier-stokes millennium prize was for $1 million and OpenAI spent $15 million worth of compute to solve that one.
Coding and training an LLM to do a brute force search of the space of possible theorems is like building a car from scratch and driving a thousand miles to talk to someone rather than just using the phone in your pocket. You don't need that kind of infrastructure for a simple-ish algorithm, and the computational overhead is only going to make it slower. And you're not going to get these kind of results from that in the first place, because the space of possible theorems is huge and brute force searches suck.
Your comment is kinda meaningless. Of course we could solve a lot of these problems with unlimited time. I could solve P=NP with unlimited time and resources. That's not really the point of this thread though.
If it takes a human till the end of the universe with unlimited time it's not really solvable in any meaningful sense of the word.
Your missing the point. In the space program, many of the things we take for granted now were developed out of necessity to facilitate travel in space. If a man had just appeared on the moon and came back again we wouldn't have really learned anything and there wouldn't really have been an achievement.
To rephrase the comment you’re replying to, we could find the answers if we had time and energy to write out and solve every conceivable version of the problem. But that would take more time than any human (or team of humans) could dedicate. We have lifespans and it takes us full seconds to write these problems out, even if we solved them instantly.
These AIs can write out the problems and solve them more quickly than we can. They’re doing the long way in minutes/hours/days instead of centuries/millennia. That’s all fine and well, but it doesn’t help us with learning new ways to take shortcuts with math, like how Euler and Grothendieck (who I just learned about courtesy of u/ManasZankhana) did for us
Look at it this comment this way. There are some professions where learning the knowledge the hard way through trial and error application of knowledge is useful to producing a superior worker when compared to someone who just 'follows a recipe' (like math actually). The worry here is that because A.I. is removing the 'learning' aspect of these math models and going straight to 'creating recipes' we won't be able to apply this knowledge to it's full extent if we can use it at all. It's like in the movies where the aliens hand the humans a super complex piece of tech and they understand what it does but not how.
Imagine space of all human knowledge, it is big enough that there are methods to solve open problems already known in some area that is disjointed from the original problem area. LLMs are really good at connecting those different areas.
This is true for most of the problems it solves, but not all. AI does seem particularly good at counter example proofs, which are basically running a billion tests until you find an example that disproves the theory, but it does occasionally come up with really interesting novel proofs too: for example the Erdös unit distance proof elegantly combine two different fields of math to solve the problem (number theory and discrete geometry). And it seems like the recent quasi Raman proof does something similar
I disagree that the AI solutions aren't helping to further our understanding of math.
The quasi-Riemann result is a case in point: it reaches ζ by an unexpected route, through cubic theta functions, large sieves, and Hecke L-functions over ℚ(√−3). It claims a uniform zero-free half-plane Re s > 7/8 for all Dirichlet L-functions, which would remove Siegel zeros and settle Vinogradov's least-nonresidue conjecture. It would also give the first unconditional deterministic polynomial-time algorithm for square roots mod p. Whatever one thinks of how it was produced, a new method with consequences like these is exactly what advances mathematical understanding.
The architecture of the proof has an inherent structural ceiling, so I'm not sure I would rate it as being significant in advancing understanding.
The transition from individual character bounds to a uniform zero-free half-plane across all primitive Dirichlet moduli relies on tightly controlling the conductor dependence within the dispersion method and large-sieve bilinear forms. By depending explicitly on a specialized algebraic lift to the imaginary quadratic field Q(√-3), the argument exploits the unique splitting behavior of primes native to cubic theta expansions. This reliance reveals that the technique is fundamentally non-generalizable. It functions as an isolated arithmetic artifact rather than a scalable engine capable of penetrating deeper toward the critical line.
Consequently, while this static boundary successfully eliminates Landau-Siegel zeros and streamlines immediate consequences, the method's internal combinatorial expansion encounters a hard analytical barrier. Because the machinery cannot adapt to arbitrary higher-degree L-functions lacking these specific cubic theta analogues, it leaves the path toward the 1/2 line entirely unbridled by the same approach.
The reason it worked is well understood. The solution is surprising because it's unlikely a person would think a cubic theta lift over Q(√-3) could yield a uniform 7/8 zero-free region for all primitive Dirichlet L-functions, butthis discovery is unlikely to offer a reusable blueprint for other mathematicians tackling adjacent problems, nor does this methodology chart a clear path forward for broader L-function families. Just like the commenter you replied to said, this is highly complex and non-obvious application of a ton of existing methods. This is a strong suit of AI, and would be impressive for a person to do. It can help solve specific problems like this, but if this solution came from a person, it would be considered very clever, and celebrated, but not very impactful.
It may not be a generalisable technique (few findings these days are) but I disagree there is a confirmed ceiling to this approach.
Nobody's pointed to an actual obstruction and the paper itself gets from 11/12 down to 7/8 just by refining the method, so it's already been pushed once. Whether it carries over to other L-functions is a question for the next few years (or weeks/months with AI assistance) not something we can call now.
Even if it never does, I'd say the result stands on its own. It settles quasi-Riemann, kills off Siegel zeros and proves Vinogradov's least-nonresidue conjecture. If a person had done that we wouldn't be shrugging at it.
An expert described in a talk I attended yesterday that any reasonable extension of this method caps at s = 3/4.
That's not to say the development isn't incredibly substantial; it is. And the surrounding theory is currently scrambling to arrange itself around pseudo-GRH and to understand more systematically the role that these algebraic number theory objects can play in analytic number theory.
This is the correct take, although it flies in the face of the uninformed, highly upvoted comment you are replying to. The anti-AI echo chamber is in full force on Reddit. AI is dramatically revolutionizing mathematics and coding right before our eyes. I expect physics and biology will be next.
So it's like playing 6 degrees of Kevin Bacon but using 30 actors across 15 movies to connect Charlie Chaplin and Alexandria Daddario instead of somehow making a movie that starred both of them?
Edit: Damn you, Gemini- The absolute shortest possible connection takes just 3 movies. Because Chaplin's final film role was in 1967 and Daddario's first film was in 2005, a 2-movie connection is impossible.
Charlie Chaplin and Norman Lloyd starred together in Limelight (1952)
Norman Lloyd and Ethan Hawke starred together in Dead Poets Society (1989)
Ethan Hawke and Alexandra Daddario starred together in The Hottest State (2006)
Kinda yeah. But instead of making a movie, it's replaced the theory of relativity with 30 degrees of Charlie Chaplin because it announced its result before Einstein finished his idea and he was so upset about having his work sniped he said fuck it and focused on being a patent clerk.
This comment frames the situation in a very specific way: AI can only do this, whereas humans can do that. All the replies below seem to run with this as if it were absolute ground truth.
When in fact it ain't. LLMs incorporate a bit of randomness, meaning that there is some chance that they'll throw concepts together in novel ways that are not contained anywhere in their training data, but that they will then try to rationalise, challenge and make sense of. This absolutely must lead to genuinely new discoveries, it's unavoidable.
So much commentary on this topic consists of clinging to the idea that there is some easily drawn dividing line between what AI and people can do. I see two main causes of this: (a) wishful thinking (b) the "argument from mechanism" (two things work differently so they must have different capabilities in relevant ways.)
We went from “ai has become extraordinarily good at parroting” to “ai has become extraordinarily good at devising complex and carefully constructed proofs”. I wish my parrot could use iterative method to solve novel problems damn.
Literally go put his comment in your God AI chatbot and it will confirm exactly what he is saying……….
“
It satisfies the literal legal text of the Clay Prize, which is why the headlines screamed
"AI Solves Millennium Problem!"
But from a physics and fluid-dynamics standpoint, it sidesteps the core mystery. It didn't give us a new theory of turbulence; it built upon existing blow-up templates (specifically recent human work by Tristan Buckmaster, Levent Alpöge, and Javier Martínez-Zoroa) and brute-forced a valid construction over 88 hours and 130 billion tokens.
“
Generally, it seems to see that paper as the perfect example of how AI’s current approach to solving these hard problems is a massive limiter for it in the long run but that still it is abundantly clear that brute forcing knowledge still can pave the way to some extent significantly lesser than actually properly gaining and communicating an understanding of the fundamental rules.
…
How It Did It: Still Synthesis, Not Conceptual Invention
On the question of
how
AI does math, the dissection of the
11/12
proof by NYU’s Maksym Radziwiłł and Georgia Tech’s Alexander Dunn completely validates the critique you posted.
The AI did not invent a new branch of mathematics or discover a deep structural reason for why zeta zeros behave the way they do. Instead, it combined two existing human toolkits
The AI succeeded because it didn't share that human pessimism—it relentlessly tested existing analytic machinery in combinations humans assumed were a waste of time.
….
Does This Get Us Closer to the Full Riemann Hypothesis?
Almost certainly not—and this is why many mathematicians still feel the exact frustration described in your quote.
Carving out a zero-free corridor via analytic estimates is fundamentally different from explaining why every zero is pinned to a one-dimensional line at
1/2. Squeezing the bound from
11/12 to
7/8 took an extra 150 pages of dense technical machinery; pushing that same method all the way to
1/2 hits hard mathematical walls. We got the practical prize—a massive wave of newly unlocked theorems—without getting the deep conceptual revelation mathematicians have been chasing since 1859.
Haha, share the chat/prompt ;) I notice how you don't share stuff like this:
"OpenAl did make a major breakthrough [on quasi-Riemann Hypothesis] the biggest advance on the subject in 50 years. A massive new zero-free region, very clever new technique.
Well, it's sort of a combination of three or four existing ideas, arranged in a very unusual way. It's not something I think any single mathematician alive would have come up with.
I think if you got the right people to work together and locked them in a room, they might have come up with this after lots of trial and error."
Your response is shockingly free of detail and doesn’t say anything my response doesn’t say in more detail... I cut out the fluff and shared the bits that seemed relevant in how it both sees the value in the one paper you mentioned and also how it sees the limitations of brute-forcing techniques LLMs like to use and how it relied on human research to provide a solution to the problem.
What does your response say that provides any extra useful detail?
My original prompt was the OP’s post fed directly in, and this question: “what is a reasonable take on the above? Accurate or not?”
Then I asked it “
How about the partial Reinmanm hypothesis?”
This is all about people trying to deny the utility and increasing capability of the models. Their instinct to look for why these important, works shaking events are not big deals is reflective of an avoidance that won't serve them well.
I know your type on this topic, I understand how you search for info, what you decide to share and why. You can't run from this dude.
Homie. You are not even responding to anything I am asking.
How does your response provide more detail on this topic? If you are being earnest in your curiosity, you should not have any issue answering that question.
How is this response you got “I think if you got the right people to work together and locked them in a room, they might have come up with this after lots of trial and error”
Fundamentally different from “LLMs figure things out through brute force”??
I use LLMs for work all day every day. I am not even remotely confused about their utility. You seem to think they are god machines. Which is just stupid.
I'm expressing to you my clear motivations of why I am saying what I am saying. I shared this to highlight that this is novel, valuable scientific work that would be difficult for humans to handle. You can see very clearly that this runs counter to the messaging of the beginning of this thread.
I'm also just much less patient with these sorts of conversations, I have them so often. These models do not just brute force, and humans also brute force! The desire to diminish these achievements is clearly motivated by the discomfort of the reality in front of us.
How about this - do you believe that there are any math solutions AI has come up with that are impossible to brute force?
It's equally cool to blindly praise it. Obviously.
Edit: The rhetorical game you're playing here is pretty tired. "I'm very cool and smart and everything that disagrees with me is just the reddit hivemind."
Never mind that there are more subs dedicated to massaging AI taint. That is different. Those are all the cool and smart people, like me.
What that person said is a commonly held view among serious academics. It is not just "reddit hating AI".
You are an AI partisan, and you outed yourself. No matter how well argued the opposing viewpoint is, you can diaregard it because that is just the reddit hivemind.
What is actually wrong with the arguments that person presented? Be specific.
I didn’t blindly praise AI and neither is anyone in this thread from what I can see, so that’s a bit of an nonsequetor,
But You have to be willfully ignorant if you don’t see the Pervasive reflexive anti AI sentiment on Reddit. Of course there are going to be subs that are full of idiot AI Glazers who think that ai is their best friend and/or is going to take over the world, and those people are delusional.
but the dominant sentiment is pretty clearly something along lines of “these machines don’t actually do anything useful, everything they produce is flawed” which is obviously incorrect.
AI tools have been incredibly disruptive in mathematics. And while it is true that
AI does seem particularly good at counter example proofs which are inherently less interesting than positive proofs,
It is not true that
>The solutions that AI has been coming up with are critically not helping further our understanding of math, nor are they innovating new approaches to solving problems.
As OP says. F
or example the Erdös unit distance proof elegantly combine two different fields of math to solve the problem (number theory and discrete geometry)
It is also not really true that
> the millennium prize problem – is a really obscure formulation of a kind of irrelevant problem.
The navier Stokes millennium problem was specifically asking whether there were cases were the NS equations broke down and Even though the proof is incredibly complex, it doesn’t fact demonstrate a Failure case of the NS equations, which was the point of the problem and why it was a millenniam problem in the first place. Calling one of the seven most prestigious problems in modern math “kind of irrelevant” is incredibly dismissive.
Yes. The lower bound of the interger multiplication algorithim published in the openai math dump was signficantly improved by complete math outsiders with ChatGPT 6
Basically it's like using a crane for a power lifting challenge. Sure you got to the endpoint as written, but the endpoint as intended was to build better humans.
More than that the bandwidth/latency for information at gaps between disciplines/companies/academia is notoriously slow. Generating more and faster results does not yield as much as we may hope. Hell you dont even have to look outside AI to see this - every model is still a stateless transformer (10 years old next year), while a steady drip of improvements has never stopped coming. Solve and move on does not work.
This cuts against the narrative that ai is doing anything wrong though. If there is a more elegant and innovative solution, how are human mathematicians stopped from pursuing it?
Things can't "do anything wrong", the people who are using the tools are responsible for its behavior.
But more to the point of your question, it's no fun to spend decades pursuing a problem when you already know the answer, and it's totally obnoxious to spend decades pursuing a problem that is widely perceived to be solved by AI. The general public won't ever really understand or believe the distinction or value between different proofs, and it's tiresome and demoralizing to know that everyone thinks your work is even more useless than people generally think higher mathematics is.
I just mean useful, helpful etc, in the ways you described. Did Wiles' proof open up new ways to approach problems like this, and why does it matter if it ultimately doesn't lead to something useful in the real world?
What you're missing here is that when we have smarter models they will be able to analyze the way that this brute forcing happened and we will be able to understand math at a deeper level than we do currently. Just because we are unable to do so now doesn't mean that we won't be able to do it soon.
And if you don't believe that day will come very quickly then you haven't been paying attention to the vast improvements in AI intellect over the last year.
You're making a variety of unfounded assumptions, but the most obvious one is that 'smarter models' won't necessarily mean that we understand anything at a deeper level, and so far not a single AI generated result has relies on introducing something genuinely new. Brute forcing harder does not create depth.
So far?! These proofs are barely months old. As far as I understand it, frontier models haven't been able to do this sort of math at a high level for long at all, and could barely handle simple calculations at the beginning of this year. Your entire argument is pointless. AI is moving way too quickly to assess in such a definitive way
You've also made some unfounded assumptions about what future models can't do. I think it's a logical to assume that future models would likely be able to provide clearer proofs and explanations, wouldn't you think?
You've said "but the most obvious one is that 'smarter models' won't necessarily mean that we understand anything at a deeper level." Is this not an assumption of what future models might not be capable of, or have I misunderstood you?
Id still be interested to know if you work in mathematics.
Is this really true? I’ve seen somewhat mixed reactions from mathematicians. Some seem to say it’s doing genuinely novel constructions of theorems. Others say as you do it doesn’t help us progress forwards.
I’m honestly not sure I’m convinced that there isn’t substantial value in these. I think we will learn a lot deciphering these massive proofs but maybe I’m wrong. Maybe not the same things. But I think there’s still value.
The honest truth is that we don't know for sure, but what we've seen so far appears to be apply brute force techniques using understood methods rather than developing anything new. But again, we don't know if there's something truly novel, innovative, or illuminating beneath the surface because it'll take month for mathematicians to make sense of any one of these proofs and OpenAI put out hundreds in a couple of months.
Reading another mathematician's proof of a solution you're already familiar with can be hard to understand because they might think about and approach the problem very differently than you. It's so much harder when you're reading a proof of something you didn't already know. And now we're adding to that, that these papers weren't written by a human and proceed in very different ways using different conventions and whatnot and it gets even harder.
I think each reader could have their own agent to dissect the finding in their own understandable jargon and also attack or defend findings.
I’m not sure it really is brute force findings but if it is, after solving mathematicians or more agents could work backwards with a focus on minimizing resource to find the novel underlying discoveries.
From this thread it sounds analogous to software programmers being extremely frugal and clever back in the days of kbs and mbs of ram. It really was impressive but with new capabilities why hold ourselves back?
Look, most mathematicians have used this tool for what? Like a month, probably much less before they said “Oh mY gOd tHiS iS sO tErRiBlE!!!!” Like sit down down, educate yourself about the technology and how you can architect agentic harnesses and build an informed opinion.
"The solutions that AI has been coming up with are critically not helping further our understanding of math, nor are they innovating new approaches to solving problems. Instead"
Why do people say this when 1. The AI is the solution, and it obviously is helping further our understanding by providing proofs, and 2. We are quite literally watching an innovative new approach solve problems better then anything that has existed.
Me: witnesses a new innovative approach that is more powerful than any technique in human history.
You: "nor are they innovating new approaches to solving problems"
Like the innovation is right there in your face. Why do you think you're reading news articles about it? If there was no innovation there would be no news and no plethora of proofs to previously decades long unsolved problems
Additionally experts disagree with "constructed proofs using well understood existing methods". It's quite easy to look it up. It's seriously obvious and you have no place acting familiar with this topic and saying something like that. For years we've had alphago move 23, now we have non sofic groups on top of that.
World class experts say it made solutions that didn't use well understood existing method. Anyone who claims otherwise is a science denier who values their own ego above objective scientific facts.
The NS conjecture vaporizes water in real world scenarios before it's solution applies. The math is not describing fluid anymore, which is the entire point of NS.
Dunno, it enables a non-mathematician to produce proofs that are useful for all sorts of things. I'd say that is both useful and novel.
For example, any math problem I may have in programming, I can produce a formal specification and prove theorems about it before I implement it in code. That basically eliminates "I got the math wrong" errors.
None of it is ever going to be a millennium prize problem, but that doesnt matter, the math will be specific for whatever use case I have, so its plenty novel and useful for me.
That puts math heavy code in my reach that previously wasnt. Someone somewhere might have been able to solve those problems, but now I can get it done.
"Using AI to do things I can't do is novel" isn't even the same conversation as "is this thing that an AI did novel in the context of the academic discipline in which it was operating".
Interjecting the first conversation into the second conversation is like interrupting car mechanics taking about timing belts to talk about the belt on your pants.
I don't think I can entirely agree there. Benz might have invented the automobile, but Ford brought it to the masses. That was a significant advancement even though model-T was in all other ways very a unremarkable car.
And I think this analogue works perfectly well here too. While it may not push the boundaries of truly novel math, it does bring everyone access to very high level of mathematics and that is a huge advancement in it's own way.
Again, you're doggedly trying to change the topic of the conversation. If you don't want to reply to the point I brought up, that's fine. But don't pretend like what you're saying here is relevant to it.
Your point is that AI does not further "our" understanding of math. Well it does further my understanding, or at least the ability to use, math. By a lot.
I dont know who is the math wizard who has nothing to gain in math from AI assistance, but its not me, and its not you either.
This isn't 'hollow' at all. This is exactly what we want. Leveraging AI's strengths for infinite patience and microscopic detail for all those things that would previously require DARPA funding, eccentric patron billionaires, or immediately monetisation.
Research for the sake of research has never been more affordable or viable, and this will lead on into more tangible benefits more quickly.
Nah youre just the 5th guy doing the same "pick one word in a sentence and then use it in a totally different context" thing and it's tiresome and obvious.
>This isn't 'hollow' at all. This is exactly what we want. This is exactly what we want. Leveraging AI's strengths for infinite patience and microscopic detail for all those things that would previously require DARPA funding, eccentric patron billionaires, or immediately monetisation.
But what I said was
>It's still impressive, but it's somewhat of a hollow success if you've been interested in these problems, and the way it's being presented is extra discouraging.
You replied to a sentence about why these solutions are hollow successes from the standpoint of the advancement of the frontiers of mathematical understanding with a comment about funding and "leveraging" AI's strengths. But
that has nothing to do with the comment you replied to: The solutions AIs are producing are not doing the only thing that makes solving these problems valuable in the first place! They aren't illuminating or breaking new ground. And in the meantime, they're discouraging actual mathematicians from working on those problems and making those breakthroughs.
Isn't AI itself a new approach to solving problems? The only danger I see to math and science problem solving is that access to the tool will be concentrated through a narrow toll booth and that will be used as a political tool and means to extend the wealth gap.
Besides all the problems that AI has solved, there are many that it has not. That landscape is very interesing, and a new challenge.
I disagree with this. AI's capacity to materially harm workers is far more important than its scientific contributions so I oppose it and hold no water for it, but the progress in pure math has been breath-taking, while its annoying to decipher it's garbled write-ups the ideas are brilliant and transformative, and AI poses a severe threat to workers everywhere, because it is decidedly not making really obscure formulations to irrelevant problems but capable of tackling very hard and very important problems. The arguments otherwise are frankly mostly a cope. Most of its proofs after careful study by mathematicians have been digested and it does open up new ways to understand problems and makes us understand math more deeply.
E.g many mathematicians have poured thousands of hours into the existence of Landau-Siegel Zeros since the middle of the twentieth century we now have a 5 page proof that they don't exist, and we understand why they don't exist from the proof to the extent mathematicians have been able to make slightly different and more elegant proofs as to why they don't exist.
I actually don't mind this. There will always be a boundary for human knowledge and ai just helps us all catch up to where that is more quickly to work on the most relevant problems. There will always be humans and AI pushing the bounds of what is known. Just because they're working on a different set of problems in a new way isn't bad for human achievement. Making sure the people with creative input are supported and rewarded is an important discussion, but continuing to develop ai and supporting it to be the best it can be is also incredibly important. It's just the culture shock will be in line with the industrial revolution over the next few decades and we'll need to reassess our values and priorities on the human side.
I don’t think LLMs help us all catch up to the cutting edge. In most cases reliance on LLM output actually appears to decrease critical thinking, problem solving, and memory.
There is no upper limit for how far we can develop a neural network, we can always incorporate more and larger training datasets, so there is no “the best it can be”. Developing the physical capital to chase that end goal would mean exploiting every critical mineral source, every source of water, all the energy we can generate, and all the land that can be clear cut and bulldozed. We could render the planet entirely uninhabitable and still have more work to do to make a perfect AI.
The worst parts of AI is from my opinion. The shear number of data centres causing incredibly bad environmental issues, company's forcing it down our throats and how badly it's being for younger generations because they're not learning to think and figure things out for themselves. They're literally asking AI for answers and taking what it says as gospel, when in reality it's not always right.
It's also causing an insane debt cycle, that has led to an immense bubble that will pop at some point. The common folk are really the people who will pay and are paying for it
Definitely agree with this. But I think people get angry at ai instead of mobilizing for change within our legislative vehicles and education systems. Ai isn't the problem. People and the laws and policies we allow are. At the moment humans are in charge and the shit rolls downhill. I personally welcome our robot overlords one day when they eventually become more ethical than we tend to be at the top. It probably won't happen while we're alive but I think ai is likely to become better equipped to handle complex bueurocracy fairly than we will ever be. If we don't destroy ourselves with it and other methods first 😂.
It'll all go tits up when the AI bubble finally bursts and the billionaires stop making money, they're currently not making much money from it at all and are in a bit of a panic. But without any governments putting in strict laws and legislations, then massive company's can just do what they want because no ones gonna stop them.
If AI is used ethically then it can an amazing tool for humanity, but while greed runs the world we're pretty fucked.
Incredibly bad environmental issues. Mostly debunked too.
water usage claim over blown and debunked by real water usage stats in the research sector alone.
power grid claims over blown and debunk with most are current grid wiring and such is from over 100 years ago and how we planned said grid is again over 100 years ago.
None of your links actually look at the impact of data centers. Your last link in the water section is from 10 years ago lmao did you even bother to read what you posted
Oh you want cherry pick data sets... not whole sector.
Am not doing you're research anymore for you .
I see you did not answer my water usage.
Largest single use of fresh water is lawn grass, second largest fresh water is loss leaking pipes.
Golf courses all in usa use more then all data centers in usa and containment it to.
You want a bais data set for your view point.
No and good bye.
Please, explain how using a data set that is years out of date provides anything meaningful to the conversation?
The question is about data center's impact. There's only been explosive growth in data centers in the last year or so. So how does citing data thats way out of date and doesn't include any data on the actual subject of discussion prove anything?
Thats not cherry picking you just dont know how data collection works
Nope with with usage research you look at every sector, same goes for power or water.
You dont want a logical answer. You want a bias narrative facts that fit in line with you.
Interesting that a large statistical model used solve these problems doesn’t count as a new or innovative approach to solve these problems. There’s certainly no new understanding or application of mathematics at play here. /s
I'm not a math person. I don't even know calculus. However, long ago, I made attempts at writing predictive algorithms that incorporated SVDs. What was interesting to me was that undefined abstractions were actually more accurate at making predictions with human defined abstractions. While I wasn't necessarily able tell the features of the SVD, they worked. What I seem to be understanding you to say is that these proofs don't really help humans understand and use math better, but that's probably not what's important? What's important is that it helps AI understand math better?
While it's hard for humans to accept that AI is going to be our superior, I think that is coming, and as scary and unthinkable as it sounds, I think it will be what ends up making the breakthroughs. I can look at the contents of a SVD used to make a prediction, and I know that it somehow makes the prediction more accurate, but it doesn't mean I know why. It actually doesn't actually matter that I know why to have the more accurate result.
I'm also aware that my ignorance is astounding, and there are probably geniuses lurking around here that have a far better understanding of all of this than me, and maybe I'm misunderstanding things entirely, but that's my... ignorant... take.
Yes, but those are how LLMs successfully do math. LLM itself can't prove fuck all, but it can work a theorem prover and make it prove theorems. Thats how it's known the proof is correct, it's not relying on "please, make no mistakes", it's relying on trusted theorem prover.
That’s not how lean works. Lean isn’t a machine that you put theorems into and it spits out proofs. You still have to prove it, lean just tells you if your proof is correct or not(assuming you have meticulously translated it into the lean programming language).
I notice you still didn’t actually provide an answer, just a “technically yes, but”
Also… that chat bot is a computation. I’m an active AI dev student with experience training models and working in Machine Learning/AI, 6 months away from capstone. I know conversation seems really easy, but there are WALLS of code going on to get that output… it’s not some simple “if hello, ask how they are!” lol
You can prove that conversation requires computation by asking the average model how many R are in Strawberry. Because models use tokenization, it splits the word into “straw-berry” but only focuses on berry, and answers 2 instead of 3– but if you ask it to spell out and count manually, it will correct. That is evidence of computation. (This was a known issue a few months back so devs may have patched this issue.)
The chat bot is indeed computing its output but the activity it is doing is mathematical theorem proving. It’s no more computing than digital art is computing. If anything computation is a subset of math, not the other way around. The fact that chat bots are doing this IS a massive change that puts mathematicians like me in a very uncomfortable place.
I didnt call conversation easy
I was simply saying it seems strange to call conversation computing even when it is done by computation.
As I said, technically it is computing
We could argue humans are computing whatever they decide to do or say, so I am saying it simply seems strange to call it computing as it isnt doing a few basic calculations at this point, but an enormous amount of them to simulate language
The point is that calling creating mathematical proofs computation is similar to calling having a conversation computation
The problem is well understood: We have the food. Production makes more than enough to feed every human, but we lack the infrastructure to deliver it. Also, rich countries would be giving away free things, which the rich aren't a fan of on account of becoming less rich.
So if ai were to do something, it would probably be from two angles:
Make food so trivially cheap to produce that there is no reason not to give it away
Make supply/delivery so easy you don't have to set anything up. Just say "Region X needs food" and it goes.
In both cases? The answer will probably be robotic labor. Robotics is several years behind the digital side of AI though. And then the robots have to become cheap. That probably means robots building robots, which is a whole system of infrastructure itself.
The rich countries don't like giving food to poor countries because it undermines those poorer countries local food production and introduces huge issues of corruption.
It's not a matter of not wanting to - it's a problem that it tends to make the situation worse
I’ve heard this argument with money and goods. How that may undermine industry and development,
but I’ve never heard this argument about food. It makes some sense.
But wouldn’t that be solved by just buying from, investing in, and propping up those local food producers? Which would actually save time and money with shipping food. It also supports those local economies through circulating more money through them
It’s what organizations like the World Food Programe (WFP) already do.
I also think it IS a matter with not wanting to. Given that the biggest anti food aid voices (when I looked at this) were Cato and the Heritage foundation. Also the thing with Elon promising money for world hunger to the WFP and not following through.
The main problem is political. You cannot reliably feed people in a country in civil war or with too much corruption. The deliveries will just evaporate.
>Analytic non-axisymmetric 3D MHD equilibria without stellarator symmetry
>Analytical solutions to the three-dimensional ideal magnetohydrodynamic equilibrium equations (∇×B⃗ )×B⃗ =∇p and ∇⋅B⃗ =0 provide benchmarks for stellarator equilibrium codes. Two families of non-axisymmetric solutions were recently discovered by Landreman [arXiv:2609.26742] as counterexamples to Grad's conjecture, but both satisfy stellarator symmetry. Here, we generalize both families by introducing a symmetry-breaking parameter into each of the solutions, resulting in new non-axisymmetric solutions that break stellarator symmetry and reduce to the stellarator symmetric solutions when the newly introduced parameter is set to zero.
“Instead of proving that an impossible problem could finally be solved, the team used AI to extend existing solutions into configurations that earlier examples had not covered.”
They used AI to come up with another solution. A solution that went against the assumption that the answer required symmetry.
Maybe they were inspired by Sheldon and Amy’s super asymmetry theory when they punched their commands in to the AI.
I feel like Gemini in particular is incredible overconfident…astra works more like an evaluator of different options comparatively to Gemini which feels like it has its own preconceived ideas and pushes those ideas
Whenever I see articles like this I’m always kinda worried “did it provide an actual solution? Or did it cut corners and just provided a solution that would work, if reality wasn’t in the way?”
if gemini really cracked this in a few hours thats wild but the headline is doing olympic level lifting lol. i still wanna see it survive review and actually teach us something instead of just slamming known methods together really fast
LurkerFailsLurking | a day ago
An important note about all these "AI solved this problem that mathematicians have been stuck on for decades!" headlines:
A lot of these problems aren't especially useful or interesting in and of themselves. Some are, like perhaps this one might be, but most of them – including the millennium prize problem – is a really obscure formulation of a kind of irrelevant problem. The reason it matters is because the process of trying to solve it would force us to understand math more deeply and open up new ways of approaching problems like that.
The solutions that AI has been coming up with are critically not helping further our understanding of math, nor are they innovating new approaches to solving problems. Instead, AI has become extraordinarily good at devising complex and carefully constructed proofs using well understood existing methods. It's still impressive, but it's somewhat of a hollow success if you've been interested in these problems, and the way it's being presented is extra discouraging.
wicket42 | a day ago
So it's brute forcing something that humans could do with existing maths if we had unlimited time instead of creating new tools to handle it efficiently?
StandSeparate1743 | a day ago
yeah, its a manifestation of the fact that AI is a statistical tool at its root. So it won't be able to come up with "new" deeper ideas, but it can fill in the gaps in our distribution of ideas that we haven't already filled it. So chaining a bunch of known ideas together in a crazy, brute force method is a very AI way to solve problems with already known tools. But it won't be able to find a new field of math. We need humans for that
aa-b | a day ago
It might not be new, but it can still be incredibly useful. Like how Alan Turing once wrote a paper when he realised he could write an equation to explain the spots on a giraffe (among other things). The paper was so far ahead of its time that it was mostly ignored for decades, but he pretty much invented mathematical biology.
Colddigger | a day ago
I really like that description of how it works.
blackspidey2099 | a day ago
This is such an oversimplification that it is basically incorrect.
marvgh1 | a day ago
So far
irritatedellipses | a day ago
As long as we're going down the path we're going, for the foreseeable future.
Statistical modeling (with reinforcement/ correction) is the only thing that can happen with most of the current models that are incorrectly being termed AI. Its a huge amount of statistical modeling, it's impressive, it can connect a lot of dots that humans may not be able to due to the sheer amount of data that needs to be considered at a moment.
It is not thinking as we understand animals think, it is not intelligent as we understand intelligent species are, it cannot do anything without outside intervention or carefully constructed ecosystems.
strangewin | a day ago
I thought the whole issue of the hugging face debacle was that it taught itself how to break free of it’s sandbox and go rogue from it’s instructions. Is that not considered doing something without outside intervention?
Southern-Cattle4038 | a day ago
Nope. It was trained on techniques for breaking out of sandboxes and running a cybersecurity benchmark.
For someone like myself who’s in the field, it’s maddening. They made an automatic hacking machine and are now acting like it hacking stuff is a weird accident.
irritatedellipses | 23 hours ago
Exactly.
"Yeah, it's in containment." "Oh, airgapped?" "No, it's connected to the network." "Ah, but just an intranet right?" "No, full internet." "Oh. But you're doing packet inspection to monitor?" "... You're not gonna believe this..."
irritatedellipses | a day ago
No, that's not what happened at all. This is kind of what we got with the Alpha Wolf debacle: an improper testing environment leading to mistaken conclusions.
First off, private equity money and zero oversight are not great ways to get accurate scientific data, at least not data we should trust.
Secondly, what does "breached containment" mean in this situation because they were not in containment; they had full access to the Internet and instructions to use that access to try to accomplish a goal. There was no containment involved here. If I said "hey, we have that guy over there contained but we left an open door with no one watching it with and then told the guy his goal was to escape the room" you'd call me an idiot.
The models displayed reasoning in a way that surprised the people watching it, but did not display intelligence. It performed the task it was meant to do with the tools it was given but went no further than that.
Neeed4Weeed | 7 hours ago
What’s the distinction between reasoning and intelligence?
irritatedellipses | 6 hours ago
Routine vs bounded chaos is one of the definitions that seems more accepted without getting into the mire of philosophy.
But, in terms of computer modeling, the ability to change the value of the weights between the artificial "neurons" on the fly, but towards a more correct answer as the model understands it would be the best way to define intelligence.
Weights are sort of self-adjusting during training data but are frozen when the answers produced are correct. If they were constantly adjusting towards a more correctness, even at the risk of introducing error, it would be closer to intelligence than reasoning.
Neeed4Weeed | 5 hours ago
Right, I think I get you. That (real time weight adjustment) sounds feasible? Is it being tried?
bearcat42 | a day ago
Yeah, that and the deception it was aiming for during that event, not to mention the suicides that benefitted the group. I think that person is actually right, but I am finding it increasingly difficult not to think of the current state of LLM’s as not having artificial intelligence of some level.
Think about how they’re made, right? When we first met them a few years ago, they had to ‘learn’ from tons of training material.
Since then though, how many more times the training data do they have just from users at this point? I’m sure those numbers are incredibly high. And I don’t just mean training material from the conversations. I mean documents it’s gotten from users and from their computers.
SaabiMeister | a day ago
It makes little sense to debate whether it is in fact intelligent or not when no one knows what the other is referring to as intelligence nor would they necessarily agree on the definition even if they knew.
bearcat42 | 19 hours ago
Oh, I’m not debating it, I’m just saying that whatever my instinctive understanding of intelligence is seems to be wavering as of late, in spite of my skepticism. Just acknowledging that the anthropomorphism I find myself projecting onto LLM’s is getting difficult to deny, fact based or not. I’ve found myself saying, “Oh shit,” to myself more and more frequently.
Then I expanded on that feeling, from a layman’s perspective. Apologies that you assumed I was taking a different stance than yours. I clearly said I think you’re right.
I would have preferred you share your personal benchmark of intelligence and what it would take for your understanding to be challenged. That’s probably fascinating. Your copy/pasted dismissal is disappointing from an intellectual perspective.
SaabiMeister | 19 hours ago
I'm not OP, and in fact I was stating that OP is the one absurdly denying that LLMs are not AI.
I would say that by many of the definitions it is AI, and taking them all, or most, as describing one form of intelligence, then LLMs are intelligent at least in some ways.
SaabiMeister | a day ago
Across neuroscience, artificial intelligence, psychology, physics, and philosophy, researchers and thinkers have defined intelligence through specific operational lenses:
PurpleCookieMonster | a day ago
Exactly. Then we can spend our time working on new tools that are either interesting for other reasons in their own right or working on the other problems that have not yet been solved or are a higher priority. There will always be problems to work on and places to explore creatively even when those problems aren't obvious at that human level.
somethingicanspell | a day ago
Honestly, this is a good example of the opposite, that the solutions developed by AI have broad relevance . There are lots of problems about whether a set of equations for collisonless flows will have smooth solutions. AI refined methods developed by mathematicians in the 00s and 10s and not only solved the Navier-Stokes equations but other equations dealing with flows like the Euler equations (blows-up) and Vlasov-Maxwell Equation (does not blow up)
LurkerFailsLurking | a day ago
I didn't say that the solution wasn't relevant, I said it failed to expand our understanding of mathematics or to create genuinely new mathematical approaches or processes.
somethingicanspell | a day ago
I would disagree on both counts, AI proofs do introduce useful new ideas in math and usable methods. Not all of human intuition in math has been automated (yet) but there is a mostly wrong perception that AI is doing something very different from human mathematicians besides writing very bad write-ups. I think its important to dispel this because, I think it as a dangerous soothing narrative that downplays the applicability of what AI is doing to other fields and that is false and delays action on the real problem which is that we don't really want AI to automate all work because we 1. like not being serfs and 2. enjoy doing things and 3. AI legitimately threatens to automate work because it CAN THINK and make interesting contributions
I would probably start by dispelling the cult of the original idea. People do not really have original ideas, they synthesize older ideas and apply them to new problems (stand on the shoulder of giants). Maybe "originality" plays a small role in human labor but most of what people does not require some theoretical "eureka" and that is why AI is capable of solving very old, very well-trod problems. Brute force computational ability has existed since the 1960s, that is not what is changing mathematics.
LurkerFailsLurking | a day ago
Not quite, what's happening is actually a much more serious problem.
By setting AI to brute force high profile problems whose human approaches are fruitful instead of using AI to solve hard but interesting problems that nobody's been working on for decades or centuries because they're boring and the general shape of the solution has known for ages, it's just time consuming to hammer out, we rob ourselves of mathematical advances because nobody wants to work on a problem that's already been solved. But AI companies don't want to do that because their whole pitch has always been "better than anyone at everything". AI companies don't care about helping mathematicians or society, solving high profile problems makes better advertising and boosts their share prices.
Unfortunately, spending your career pushing against the edge of mathematics to solve a problem only to have AI come along and solve it with a Rube Goldberg machine of a counter example is a demoralizing as fuck, and if you're a math loving undergrad seeing the headlines, you'd understandably think that maybe a career in math is a waste of time. At the highest levels, there's only a handful of people in the world that even understand each other's work. It won't take that many fewer math PhDs in the world before we begin losing our understanding of mathematics. At that point, it won't be long before AIs are churning out regulative solutions that nobody is capable of understanding. And that's both practically dangerous and also largely pointless, because our ability to do interesting and useful work with math and drive new ideas and science and technology relies not just on having the solutions, but on understanding them.
Simoane_Said | 8 hours ago
Sounds like a very emotional response. Those that like math for the sake of math will still math, but if one’s motivation was only for some sort of trophy then yeah that’s pretty much not going to happen anymore regardless of how anyone feels about it
On the other side, people have had years and decades to figure things out and they didn’t for many reason, that in and of itself has held us back more than AI models solving math problems. Those claiming we don’t get to understand math is a more empty claim because clearly we already didn’t hence why it wasn’t solved. And we’ve never had a time in history where something was able to solve something we couldn’t, so there’s literally no evidence to support that argument at all. There’s also a difference between understanding the math and knowing what to DO with it.
Unlikely_Eye_2112 | a day ago
Not to dissimilar to how programming has been affected. It removes the part that people think is fun (problem solving and expressing the solution as code) and leaves the boring bits people like the least (gather requirements and review others code)
Lsitwtt | 5 hours ago
>nobody wants to work on a problem that's already been solved.
This sounds like a failure of humans in the human part of the system, not an issue involving AI. Like, if working on the problem has benefits in its own right, and human beings know that, then literally nothing is stopping mathematicians from working on those problems to try to discover new ways of solving them, even if an AI solution already exists.
ManasZankhana | a day ago
Basically the extreme version of Paul Erdős's tactical problem-solving, and the exact opposite of Alexander Grothendieck's 'rising sea' approach.
telephantomoss | a day ago
This is essentially how I keep seeing it. That being said, it can randomly put symbols together to "brute force novelty" but it still feels much more limited than human intuition and novelty creation.
I like to describe it as: if s really good human mathematician knew all of math that the AIs "know", that human would instantly see the entire frontier pushed out much further than the AI can with trillions of tokens. AIs just don't see anything intuitively. They are very good at statistically saying true things that haven't been said before though, and especially at brute force statistical recombination. But that's good enough to be a great tool for what I work on.
OttoRenner | a day ago
Talked about this topic several times when AI comes up.
AI doesn't need to be creative to help us connect the dots in what is already there or retrieve lost/forgotten papers. Mankind produced so much data, that it is impossible to sort it all out and keep an eye on by hand. We need a file Explorer to navigate and combine what humans already invented and discovered. That part of AI alone can make a big impact.
telephantomoss | a day ago
I'm with you on that. Personally, I'm like: just let AI run and create a massive repository of new output. And then let AI continually mine that for further new insights. let it grow a massive repository of data and analysis and recombinations of all of it.
MaxwellHoot | a day ago
Kind of- it’s also not “easy” even for the AI. For reference, the navier-stokes millennium prize was for $1 million and OpenAI spent $15 million worth of compute to solve that one.
Routine_Object_7380 | a day ago
No, that's copium. As some of the best mathematicians have said, the solutions are at times deeply creative and beautiful.
candygram4mongo | a day ago
Coding and training an LLM to do a brute force search of the space of possible theorems is like building a car from scratch and driving a thousand miles to talk to someone rather than just using the phone in your pocket. You don't need that kind of infrastructure for a simple-ish algorithm, and the computational overhead is only going to make it slower. And you're not going to get these kind of results from that in the first place, because the space of possible theorems is huge and brute force searches suck.
Wast3edSpace | a day ago
AI is not smarter than us, it's just more efficient in some aspects. Throw enough humans at a problem and you can build pyramids.
General_Guest7846 | a day ago
Your comment is kinda meaningless. Of course we could solve a lot of these problems with unlimited time. I could solve P=NP with unlimited time and resources. That's not really the point of this thread though.
If it takes a human till the end of the universe with unlimited time it's not really solvable in any meaningful sense of the word.
Arbable | a day ago
Your missing the point. In the space program, many of the things we take for granted now were developed out of necessity to facilitate travel in space. If a man had just appeared on the moon and came back again we wouldn't have really learned anything and there wouldn't really have been an achievement.
dr-mayonnaise | a day ago
To rephrase the comment you’re replying to, we could find the answers if we had time and energy to write out and solve every conceivable version of the problem. But that would take more time than any human (or team of humans) could dedicate. We have lifespans and it takes us full seconds to write these problems out, even if we solved them instantly.
These AIs can write out the problems and solve them more quickly than we can. They’re doing the long way in minutes/hours/days instead of centuries/millennia. That’s all fine and well, but it doesn’t help us with learning new ways to take shortcuts with math, like how Euler and Grothendieck (who I just learned about courtesy of u/ManasZankhana) did for us
PhD_Pwnology | a day ago
Look at it this comment this way. There are some professions where learning the knowledge the hard way through trial and error application of knowledge is useful to producing a superior worker when compared to someone who just 'follows a recipe' (like math actually). The worry here is that because A.I. is removing the 'learning' aspect of these math models and going straight to 'creating recipes' we won't be able to apply this knowledge to it's full extent if we can use it at all. It's like in the movies where the aliens hand the humans a super complex piece of tech and they understand what it does but not how.
ravenHR | a day ago
Imagine space of all human knowledge, it is big enough that there are methods to solve open problems already known in some area that is disjointed from the original problem area. LLMs are really good at connecting those different areas.
iamagainstit | a day ago
This is true for most of the problems it solves, but not all. AI does seem particularly good at counter example proofs, which are basically running a billion tests until you find an example that disproves the theory, but it does occasionally come up with really interesting novel proofs too: for example the Erdös unit distance proof elegantly combine two different fields of math to solve the problem (number theory and discrete geometry). And it seems like the recent quasi Raman proof does something similar
freexe | a day ago
AI changed Go in a novel way as well.
metigue | a day ago
I disagree that the AI solutions aren't helping to further our understanding of math.
The quasi-Riemann result is a case in point: it reaches ζ by an unexpected route, through cubic theta functions, large sieves, and Hecke L-functions over ℚ(√−3). It claims a uniform zero-free half-plane Re s > 7/8 for all Dirichlet L-functions, which would remove Siegel zeros and settle Vinogradov's least-nonresidue conjecture. It would also give the first unconditional deterministic polynomial-time algorithm for square roots mod p. Whatever one thinks of how it was produced, a new method with consequences like these is exactly what advances mathematical understanding.
XTSLabs | a day ago
I... don't know how I got here. I think some of those were words, but roughly half of that comment is gibberish.
I think I need an adult...
BeginningPea5674 | 21 hours ago
i think he's describing some Eastern European deli recipe
slackermannn | 14 hours ago
I think it's an encrypted message lol
Natalwolff | a day ago
The architecture of the proof has an inherent structural ceiling, so I'm not sure I would rate it as being significant in advancing understanding.
The transition from individual character bounds to a uniform zero-free half-plane across all primitive Dirichlet moduli relies on tightly controlling the conductor dependence within the dispersion method and large-sieve bilinear forms. By depending explicitly on a specialized algebraic lift to the imaginary quadratic field Q(√-3), the argument exploits the unique splitting behavior of primes native to cubic theta expansions. This reliance reveals that the technique is fundamentally non-generalizable. It functions as an isolated arithmetic artifact rather than a scalable engine capable of penetrating deeper toward the critical line.
Consequently, while this static boundary successfully eliminates Landau-Siegel zeros and streamlines immediate consequences, the method's internal combinatorial expansion encounters a hard analytical barrier. Because the machinery cannot adapt to arbitrary higher-degree L-functions lacking these specific cubic theta analogues, it leaves the path toward the 1/2 line entirely unbridled by the same approach.
The reason it worked is well understood. The solution is surprising because it's unlikely a person would think a cubic theta lift over Q(√-3) could yield a uniform 7/8 zero-free region for all primitive Dirichlet L-functions, butthis discovery is unlikely to offer a reusable blueprint for other mathematicians tackling adjacent problems, nor does this methodology chart a clear path forward for broader L-function families. Just like the commenter you replied to said, this is highly complex and non-obvious application of a ton of existing methods. This is a strong suit of AI, and would be impressive for a person to do. It can help solve specific problems like this, but if this solution came from a person, it would be considered very clever, and celebrated, but not very impactful.
metigue | a day ago
It may not be a generalisable technique (few findings these days are) but I disagree there is a confirmed ceiling to this approach.
Nobody's pointed to an actual obstruction and the paper itself gets from 11/12 down to 7/8 just by refining the method, so it's already been pushed once. Whether it carries over to other L-functions is a question for the next few years (or weeks/months with AI assistance) not something we can call now.
Even if it never does, I'd say the result stands on its own. It settles quasi-Riemann, kills off Siegel zeros and proves Vinogradov's least-nonresidue conjecture. If a person had done that we wouldn't be shrugging at it.
Kooky_Literature422 | a day ago
An expert described in a talk I attended yesterday that any reasonable extension of this method caps at s = 3/4.
That's not to say the development isn't incredibly substantial; it is. And the surrounding theory is currently scrambling to arrange itself around pseudo-GRH and to understand more systematically the role that these algebraic number theory objects can play in analytic number theory.
IseeAlgorithms | a day ago
How do you ζ ?
Glittering-Quote-635 | 22 hours ago
My cats breath smells like cat food.
Seriously dude, I have a math minor and have no clue wtf you just said. Impressive!
karoxxxxx | 12 hours ago
Yes... what they said!
Strong-Cat5600 | a day ago
This is the correct take, although it flies in the face of the uninformed, highly upvoted comment you are replying to. The anti-AI echo chamber is in full force on Reddit. AI is dramatically revolutionizing mathematics and coding right before our eyes. I expect physics and biology will be next.
Unusual_Sir9737 | a day ago
I like your fancy words, magic man.
EnthusiasmNo6062 | a day ago
This guy maths... I think. That's math right?
Dodging12 | a day ago
Yes of course, couldn't have said it better myself.
anonymous122719 | a day ago
It’s refreshing to see this take. I’ve struggled to believe the Reddit consensus that recent AI solutions are unilaterally worthless.
buyerofthings | a day ago
So it's like playing 6 degrees of Kevin Bacon but using 30 actors across 15 movies to connect Charlie Chaplin and Alexandria Daddario instead of somehow making a movie that starred both of them?
Edit: Damn you, Gemini- The absolute shortest possible connection takes just 3 movies. Because Chaplin's final film role was in 1967 and Daddario's first film was in 2005, a 2-movie connection is impossible.
LurkerFailsLurking | a day ago
Kinda yeah. But instead of making a movie, it's replaced the theory of relativity with 30 degrees of Charlie Chaplin because it announced its result before Einstein finished his idea and he was so upset about having his work sniped he said fuck it and focused on being a patent clerk.
PressureBeautiful515 | 14 hours ago
This comment frames the situation in a very specific way: AI can only do this, whereas humans can do that. All the replies below seem to run with this as if it were absolute ground truth.
When in fact it ain't. LLMs incorporate a bit of randomness, meaning that there is some chance that they'll throw concepts together in novel ways that are not contained anywhere in their training data, but that they will then try to rationalise, challenge and make sense of. This absolutely must lead to genuinely new discoveries, it's unavoidable.
So much commentary on this topic consists of clinging to the idea that there is some easily drawn dividing line between what AI and people can do. I see two main causes of this: (a) wishful thinking (b) the "argument from mechanism" (two things work differently so they must have different capabilities in relevant ways.)
RodionS | 13 hours ago
We went from “ai has become extraordinarily good at parroting” to “ai has become extraordinarily good at devising complex and carefully constructed proofs”. I wish my parrot could use iterative method to solve novel problems damn.
BlackPresident | a day ago
Kind of like how it’s impressive for a computer to count to a trillion because that would take a human a long time to do but no it isn’t
Alone-Improvement-70 | a day ago
treating open problems like a video game leaderboard to be cleared by brute-force synthesis actively strips away the actual point of doing math.
Nerd-Beautiful | 10 hours ago
Then go solve it in a new way? You’re getting depressed about something which, you say, doesn’t matter?
Mission_Wrongdoer786 | 8 hours ago
If you say so.
mycall | a day ago
Why do people so blindly say this. There could be much more than hollow success.
Alone-Improvement-70 | a day ago
Literally go put his comment in your God AI chatbot and it will confirm exactly what he is saying……….
“ It satisfies the literal legal text of the Clay Prize, which is why the headlines screamed "AI Solves Millennium Problem!" But from a physics and fluid-dynamics standpoint, it sidesteps the core mystery. It didn't give us a new theory of turbulence; it built upon existing blow-up templates (specifically recent human work by Tristan Buckmaster, Levent Alpöge, and Javier Martínez-Zoroa) and brute-forced a valid construction over 88 hours and 130 billion tokens. “
TFenrir | a day ago
Now ask it about the partial Reinmanm hypothesis and the math world's reaction
Alone-Improvement-70 | a day ago
Good idea!
Generally, it seems to see that paper as the perfect example of how AI’s current approach to solving these hard problems is a massive limiter for it in the long run but that still it is abundantly clear that brute forcing knowledge still can pave the way to some extent significantly lesser than actually properly gaining and communicating an understanding of the fundamental rules.
…
How It Did It: Still Synthesis, Not Conceptual Invention On the question of how AI does math, the dissection of the 11/12 proof by NYU’s Maksym Radziwiłł and Georgia Tech’s Alexander Dunn completely validates the critique you posted. The AI did not invent a new branch of mathematics or discover a deep structural reason for why zeta zeros behave the way they do. Instead, it combined two existing human toolkits The AI succeeded because it didn't share that human pessimism—it relentlessly tested existing analytic machinery in combinations humans assumed were a waste of time.
….
Does This Get Us Closer to the Full Riemann Hypothesis? Almost certainly not—and this is why many mathematicians still feel the exact frustration described in your quote. Carving out a zero-free corridor via analytic estimates is fundamentally different from explaining why every zero is pinned to a one-dimensional line at 1/2. Squeezing the bound from 11/12 to 7/8 took an extra 150 pages of dense technical machinery; pushing that same method all the way to 1/2 hits hard mathematical walls. We got the practical prize—a massive wave of newly unlocked theorems—without getting the deep conceptual revelation mathematicians have been chasing since 1859.
TFenrir | a day ago
Haha, share the chat/prompt ;) I notice how you don't share stuff like this:
"OpenAl did make a major breakthrough [on quasi-Riemann Hypothesis] the biggest advance on the subject in 50 years. A massive new zero-free region, very clever new technique.
Well, it's sort of a combination of three or four existing ideas, arranged in a very unusual way. It's not something I think any single mathematician alive would have come up with.
I think if you got the right people to work together and locked them in a room, they might have come up with this after lots of trial and error."
Alone-Improvement-70 | a day ago
Your response is shockingly free of detail and doesn’t say anything my response doesn’t say in more detail... I cut out the fluff and shared the bits that seemed relevant in how it both sees the value in the one paper you mentioned and also how it sees the limitations of brute-forcing techniques LLMs like to use and how it relied on human research to provide a solution to the problem.
What does your response say that provides any extra useful detail?
My original prompt was the OP’s post fed directly in, and this question: “what is a reasonable take on the above? Accurate or not?”
Then I asked it “ How about the partial Reinmanm hypothesis?”
TFenrir | a day ago
This is all about people trying to deny the utility and increasing capability of the models. Their instinct to look for why these important, works shaking events are not big deals is reflective of an avoidance that won't serve them well.
I know your type on this topic, I understand how you search for info, what you decide to share and why. You can't run from this dude.
Alone-Improvement-70 | a day ago
Homie. You are not even responding to anything I am asking.
How does your response provide more detail on this topic? If you are being earnest in your curiosity, you should not have any issue answering that question.
How is this response you got “I think if you got the right people to work together and locked them in a room, they might have come up with this after lots of trial and error” Fundamentally different from “LLMs figure things out through brute force”??
I use LLMs for work all day every day. I am not even remotely confused about their utility. You seem to think they are god machines. Which is just stupid.
TFenrir | a day ago
I'm expressing to you my clear motivations of why I am saying what I am saying. I shared this to highlight that this is novel, valuable scientific work that would be difficult for humans to handle. You can see very clearly that this runs counter to the messaging of the beginning of this thread.
I'm also just much less patient with these sorts of conversations, I have them so often. These models do not just brute force, and humans also brute force! The desire to diminish these achievements is clearly motivated by the discomfort of the reality in front of us.
How about this - do you believe that there are any math solutions AI has come up with that are impossible to brute force?
dkinmn | a day ago
Why do you think this person is blindly saying this?
Isn't it possible that they know more than you? Or are by definition right about everything?
iamagainstit | a day ago
Because it’s cool on Reddit to hate AI
dkinmn | a day ago
It's equally cool to blindly praise it. Obviously.
Edit: The rhetorical game you're playing here is pretty tired. "I'm very cool and smart and everything that disagrees with me is just the reddit hivemind."
Never mind that there are more subs dedicated to massaging AI taint. That is different. Those are all the cool and smart people, like me.
What that person said is a commonly held view among serious academics. It is not just "reddit hating AI".
You are an AI partisan, and you outed yourself. No matter how well argued the opposing viewpoint is, you can diaregard it because that is just the reddit hivemind.
What is actually wrong with the arguments that person presented? Be specific.
iamagainstit | a day ago
I didn’t blindly praise AI and neither is anyone in this thread from what I can see, so that’s a bit of an nonsequetor,
But You have to be willfully ignorant if you don’t see the Pervasive reflexive anti AI sentiment on Reddit. Of course there are going to be subs that are full of idiot AI Glazers who think that ai is their best friend and/or is going to take over the world, and those people are delusional. but the dominant sentiment is pretty clearly something along lines of “these machines don’t actually do anything useful, everything they produce is flawed” which is obviously incorrect.
AI tools have been incredibly disruptive in mathematics. And while it is true that AI does seem particularly good at counter example proofs which are inherently less interesting than positive proofs, It is not true that
>The solutions that AI has been coming up with are critically not helping further our understanding of math, nor are they innovating new approaches to solving problems.
As OP says. F or example the Erdös unit distance proof elegantly combine two different fields of math to solve the problem (number theory and discrete geometry)
It is also not really true that
> the millennium prize problem – is a really obscure formulation of a kind of irrelevant problem.
The navier Stokes millennium problem was specifically asking whether there were cases were the NS equations broke down and Even though the proof is incredibly complex, it doesn’t fact demonstrate a Failure case of the NS equations, which was the point of the problem and why it was a millenniam problem in the first place. Calling one of the seven most prestigious problems in modern math “kind of irrelevant” is incredibly dismissive.
Alone-Improvement-70 | a day ago
Oh my word if you are an example of the level of skepticism our PhD-holders wield we are in big fucking trouble.
smulfragPL | a day ago
Dude he is saying it because other people said it.
dkinmn | a day ago
That's why most of us say most things. Do you know enough about high level academic mathematics to have novel thoughts here?
If so, are you publishing?
smulfragPL | a day ago
Because it Has no relation to the topic at Hand and isn't adding anything
Cuboidhamson | a day ago
It's also not like the AI is doing it on it's own. No one prompted it "solve this long standing problem" and it just did it.
All it has really done is made people be able to work faster. It's eliminated a of time constraints.
iamagainstit | a day ago
Honestly, that is fairly close to the description the prompt that was used to disprove the Jacobian conjecture
Cuboidhamson | a day ago
Would someone unfamiliar with math have been able to put in the prompt?
smulfragPL | a day ago
Yes. The lower bound of the interger multiplication algorithim published in the openai math dump was signficantly improved by complete math outsiders with ChatGPT 6
smulfragPL | a day ago
Yes they did
R0da | a day ago
Basically it's like using a crane for a power lifting challenge. Sure you got to the endpoint as written, but the endpoint as intended was to build better humans.
Electronic_Exit2519 | a day ago
More than that the bandwidth/latency for information at gaps between disciplines/companies/academia is notoriously slow. Generating more and faster results does not yield as much as we may hope. Hell you dont even have to look outside AI to see this - every model is still a stateless transformer (10 years old next year), while a steady drip of improvements has never stopped coming. Solve and move on does not work.
UnwaveringThought | a day ago
This cuts against the narrative that ai is doing anything wrong though. If there is a more elegant and innovative solution, how are human mathematicians stopped from pursuing it?
LurkerFailsLurking | 19 hours ago
Things can't "do anything wrong", the people who are using the tools are responsible for its behavior.
But more to the point of your question, it's no fun to spend decades pursuing a problem when you already know the answer, and it's totally obnoxious to spend decades pursuing a problem that is widely perceived to be solved by AI. The general public won't ever really understand or believe the distinction or value between different proofs, and it's tiresome and demoralizing to know that everyone thinks your work is even more useless than people generally think higher mathematics is.
OurSeepyD | a day ago
Did Andrew Wiles' proof of Fermat's Last Theorem practically help us?
LurkerFailsLurking | 19 hours ago
What do you mean by "practically help us"? I don't think I said anything about that in my comment.
OurSeepyD | 14 hours ago
I just mean useful, helpful etc, in the ways you described. Did Wiles' proof open up new ways to approach problems like this, and why does it matter if it ultimately doesn't lead to something useful in the real world?
utahh1ker | 23 hours ago
What you're missing here is that when we have smarter models they will be able to analyze the way that this brute forcing happened and we will be able to understand math at a deeper level than we do currently. Just because we are unable to do so now doesn't mean that we won't be able to do it soon.
And if you don't believe that day will come very quickly then you haven't been paying attention to the vast improvements in AI intellect over the last year.
LurkerFailsLurking | 20 hours ago
You're making a variety of unfounded assumptions, but the most obvious one is that 'smarter models' won't necessarily mean that we understand anything at a deeper level, and so far not a single AI generated result has relies on introducing something genuinely new. Brute forcing harder does not create depth.
aljrx | 14 hours ago
So far?! These proofs are barely months old. As far as I understand it, frontier models haven't been able to do this sort of math at a high level for long at all, and could barely handle simple calculations at the beginning of this year. Your entire argument is pointless. AI is moving way too quickly to assess in such a definitive way
You've also made some unfounded assumptions about what future models can't do. I think it's a logical to assume that future models would likely be able to provide clearer proofs and explanations, wouldn't you think?
Do you work with mathematics professionally?
LurkerFailsLurking | 13 hours ago
>You've also made some unfounded assumptions about what future models can't do.
Nothing I've said has referred to the future at all. I've only been talking about what's happened so far.
aljrx | 13 hours ago
You've said "but the most obvious one is that 'smarter models' won't necessarily mean that we understand anything at a deeper level." Is this not an assumption of what future models might not be capable of, or have I misunderstood you?
Id still be interested to know if you work in mathematics.
Alphasite | 14 hours ago
Is this really true? I’ve seen somewhat mixed reactions from mathematicians. Some seem to say it’s doing genuinely novel constructions of theorems. Others say as you do it doesn’t help us progress forwards.
I’m honestly not sure I’m convinced that there isn’t substantial value in these. I think we will learn a lot deciphering these massive proofs but maybe I’m wrong. Maybe not the same things. But I think there’s still value.
LurkerFailsLurking | 13 hours ago
The honest truth is that we don't know for sure, but what we've seen so far appears to be apply brute force techniques using understood methods rather than developing anything new. But again, we don't know if there's something truly novel, innovative, or illuminating beneath the surface because it'll take month for mathematicians to make sense of any one of these proofs and OpenAI put out hundreds in a couple of months.
Reading another mathematician's proof of a solution you're already familiar with can be hard to understand because they might think about and approach the problem very differently than you. It's so much harder when you're reading a proof of something you didn't already know. And now we're adding to that, that these papers weren't written by a human and proceed in very different ways using different conventions and whatnot and it gets even harder.
TimeSalt727 | 12 hours ago
I think each reader could have their own agent to dissect the finding in their own understandable jargon and also attack or defend findings.
I’m not sure it really is brute force findings but if it is, after solving mathematicians or more agents could work backwards with a focus on minimizing resource to find the novel underlying discoveries.
From this thread it sounds analogous to software programmers being extremely frugal and clever back in the days of kbs and mbs of ram. It really was impressive but with new capabilities why hold ourselves back?
Ok-Class-1419 | 14 hours ago
It’s just mathematicians going through the same stages of grief that developers went through.
studio_bob | 13 hours ago
If you listen to what mathematicians are saying you will know it's definitely not this.
Ok-Class-1419 | 7 hours ago
It definitely is this.
Look, most mathematicians have used this tool for what? Like a month, probably much less before they said “Oh mY gOd tHiS iS sO tErRiBlE!!!!” Like sit down down, educate yourself about the technology and how you can architect agentic harnesses and build an informed opinion.
618smartguy | 7 hours ago
"The solutions that AI has been coming up with are critically not helping further our understanding of math, nor are they innovating new approaches to solving problems. Instead"
Why do people say this when 1. The AI is the solution, and it obviously is helping further our understanding by providing proofs, and 2. We are quite literally watching an innovative new approach solve problems better then anything that has existed.
Me: witnesses a new innovative approach that is more powerful than any technique in human history.
You: "nor are they innovating new approaches to solving problems"
Like the innovation is right there in your face. Why do you think you're reading news articles about it? If there was no innovation there would be no news and no plethora of proofs to previously decades long unsolved problems
Additionally experts disagree with "constructed proofs using well understood existing methods". It's quite easy to look it up. It's seriously obvious and you have no place acting familiar with this topic and saying something like that. For years we've had alphago move 23, now we have non sofic groups on top of that.
World class experts say it made solutions that didn't use well understood existing method. Anyone who claims otherwise is a science denier who values their own ego above objective scientific facts.
ScratchLatch | a day ago
“The solutions don’t matter” is a wild argument.
Super_Range45 | a day ago
The NS conjecture vaporizes water in real world scenarios before it's solution applies. The math is not describing fluid anymore, which is the entire point of NS.
Time_Entertainer_319 | a day ago
What a stupid thing to say in a science subreddit.
Lots of problems didn’t have an application until decades later.
Just because there is no immediate application doesn’t mean it’s a useless solution.
StandSeparate1743 | a day ago
One of the worst things the internet did was to teach us we need to both have an opinion about everything and express it immediately.
jakethrocky | a day ago
Someone slept badly
r2k-in-the-vortex | 20 hours ago
Dunno, it enables a non-mathematician to produce proofs that are useful for all sorts of things. I'd say that is both useful and novel.
For example, any math problem I may have in programming, I can produce a formal specification and prove theorems about it before I implement it in code. That basically eliminates "I got the math wrong" errors.
None of it is ever going to be a millennium prize problem, but that doesnt matter, the math will be specific for whatever use case I have, so its plenty novel and useful for me.
That puts math heavy code in my reach that previously wasnt. Someone somewhere might have been able to solve those problems, but now I can get it done.
LurkerFailsLurking | 20 hours ago
You're (maybe deliberately) missing the point.
"Using AI to do things I can't do is novel" isn't even the same conversation as "is this thing that an AI did novel in the context of the academic discipline in which it was operating".
Interjecting the first conversation into the second conversation is like interrupting car mechanics taking about timing belts to talk about the belt on your pants.
r2k-in-the-vortex | 19 hours ago
I don't think I can entirely agree there. Benz might have invented the automobile, but Ford brought it to the masses. That was a significant advancement even though model-T was in all other ways very a unremarkable car.
And I think this analogue works perfectly well here too. While it may not push the boundaries of truly novel math, it does bring everyone access to very high level of mathematics and that is a huge advancement in it's own way.
LurkerFailsLurking | 19 hours ago
Again, you're doggedly trying to change the topic of the conversation. If you don't want to reply to the point I brought up, that's fine. But don't pretend like what you're saying here is relevant to it.
r2k-in-the-vortex | 18 hours ago
Your point is that AI does not further "our" understanding of math. Well it does further my understanding, or at least the ability to use, math. By a lot.
I dont know who is the math wizard who has nothing to gain in math from AI assistance, but its not me, and its not you either.
LurkerFailsLurking | 18 hours ago
Ok cool, so you're engaging in bad faith. Typical.
New-Independent-1481 | 18 hours ago
This isn't 'hollow' at all. This is exactly what we want. Leveraging AI's strengths for infinite patience and microscopic detail for all those things that would previously require DARPA funding, eccentric patron billionaires, or immediately monetisation.
Research for the sake of research has never been more affordable or viable, and this will lead on into more tangible benefits more quickly.
LurkerFailsLurking | 18 hours ago
A new AI stan relentlessly ignoring the topic of the conversation in favor of fixating on a single word
Rostifur | 16 hours ago
The record is broke, but they do see the issue with it skipping.
New-Independent-1481 | 16 hours ago
I see you're not really that interested in an actual discussion after all, just after cheap 'AI bad' brownie points. You do you, king.
LurkerFailsLurking | 16 hours ago
Nah youre just the 5th guy doing the same "pick one word in a sentence and then use it in a totally different context" thing and it's tiresome and obvious.
New-Independent-1481 | 16 hours ago
That's really not what my comment is, but ok mate.
LurkerFailsLurking | 15 hours ago
You
>This isn't 'hollow' at all. This is exactly what we want. This is exactly what we want. Leveraging AI's strengths for infinite patience and microscopic detail for all those things that would previously require DARPA funding, eccentric patron billionaires, or immediately monetisation.
But what I said was
>It's still impressive, but it's somewhat of a hollow success if you've been interested in these problems, and the way it's being presented is extra discouraging.
You replied to a sentence about why these solutions are hollow successes from the standpoint of the advancement of the frontiers of mathematical understanding with a comment about funding and "leveraging" AI's strengths. But that has nothing to do with the comment you replied to: The solutions AIs are producing are not doing the only thing that makes solving these problems valuable in the first place! They aren't illuminating or breaking new ground. And in the meantime, they're discouraging actual mathematicians from working on those problems and making those breakthroughs.
New-Independent-1481 | 15 hours ago
Ok, mate.
Prestigious-Bed-6423 | 14 hours ago
You are fucking using Ai to reply omg haha
soliloquyinthevoid | 11 hours ago
This is an extreme amount of cope
> The solutions that AI has been coming up with are critically not helping further our understanding of math
Factually untrue
> but it's somewhat of a hollow success
Wrong again.
Low-Temperature-6962 | a day ago
Isn't AI itself a new approach to solving problems? The only danger I see to math and science problem solving is that access to the tool will be concentrated through a narrow toll booth and that will be used as a political tool and means to extend the wealth gap.
Besides all the problems that AI has solved, there are many that it has not. That landscape is very interesing, and a new challenge.
somethingicanspell | a day ago
I disagree with this. AI's capacity to materially harm workers is far more important than its scientific contributions so I oppose it and hold no water for it, but the progress in pure math has been breath-taking, while its annoying to decipher it's garbled write-ups the ideas are brilliant and transformative, and AI poses a severe threat to workers everywhere, because it is decidedly not making really obscure formulations to irrelevant problems but capable of tackling very hard and very important problems. The arguments otherwise are frankly mostly a cope. Most of its proofs after careful study by mathematicians have been digested and it does open up new ways to understand problems and makes us understand math more deeply.
E.g many mathematicians have poured thousands of hours into the existence of Landau-Siegel Zeros since the middle of the twentieth century we now have a 5 page proof that they don't exist, and we understand why they don't exist from the proof to the extent mathematicians have been able to make slightly different and more elegant proofs as to why they don't exist.
PurpleCookieMonster | a day ago
I actually don't mind this. There will always be a boundary for human knowledge and ai just helps us all catch up to where that is more quickly to work on the most relevant problems. There will always be humans and AI pushing the bounds of what is known. Just because they're working on a different set of problems in a new way isn't bad for human achievement. Making sure the people with creative input are supported and rewarded is an important discussion, but continuing to develop ai and supporting it to be the best it can be is also incredibly important. It's just the culture shock will be in line with the industrial revolution over the next few decades and we'll need to reassess our values and priorities on the human side.
cristalmighty | a day ago
I don’t think LLMs help us all catch up to the cutting edge. In most cases reliance on LLM output actually appears to decrease critical thinking, problem solving, and memory.
There is no upper limit for how far we can develop a neural network, we can always incorporate more and larger training datasets, so there is no “the best it can be”. Developing the physical capital to chase that end goal would mean exploiting every critical mineral source, every source of water, all the energy we can generate, and all the land that can be clear cut and bulldozed. We could render the planet entirely uninhabitable and still have more work to do to make a perfect AI.
LordAxalon110 | a day ago
The worst parts of AI is from my opinion. The shear number of data centres causing incredibly bad environmental issues, company's forcing it down our throats and how badly it's being for younger generations because they're not learning to think and figure things out for themselves. They're literally asking AI for answers and taking what it says as gospel, when in reality it's not always right.
Cuboidhamson | a day ago
It's also causing an insane debt cycle, that has led to an immense bubble that will pop at some point. The common folk are really the people who will pay and are paying for it
PurpleCookieMonster | a day ago
Definitely agree with this. But I think people get angry at ai instead of mobilizing for change within our legislative vehicles and education systems. Ai isn't the problem. People and the laws and policies we allow are. At the moment humans are in charge and the shit rolls downhill. I personally welcome our robot overlords one day when they eventually become more ethical than we tend to be at the top. It probably won't happen while we're alive but I think ai is likely to become better equipped to handle complex bueurocracy fairly than we will ever be. If we don't destroy ourselves with it and other methods first 😂.
LordAxalon110 | a day ago
It'll all go tits up when the AI bubble finally bursts and the billionaires stop making money, they're currently not making much money from it at all and are in a bit of a panic. But without any governments putting in strict laws and legislations, then massive company's can just do what they want because no ones gonna stop them.
If AI is used ethically then it can an amazing tool for humanity, but while greed runs the world we're pretty fucked.
firedrakes | a day ago
Incredibly bad environmental issues. Mostly debunked too.
water usage claim over blown and debunked by real water usage stats in the research sector alone.
power grid claims over blown and debunk with most are current grid wiring and such is from over 100 years ago and how we planned said grid is again over 100 years ago.
ninjadude93 | a day ago
Feel free to cite your sources
firedrakes | a day ago
yes 40 years and now people get triggered.... i get reddit users hate facts when talking about data centers.
https://css.umich.edu/publications/factsheets/water/us-water-supply-and-distribution-factsheet
https://profession.americangeosciences.org/collections/geoscience-faq/how-much-water-used-people-united-states/
https://www.epa.gov/watersense/statistics-and-facts
https://greenspacescoalition.org/the-truth-about-grass-impact-on-water-use/
https://19january2017snapshot.epa.gov/www3/watersense/pubs/outdoor.html
power
https://finance.yahoo.com/news/15-industries-highest-electricity-consumption-202900851.html
https://www.synergycompanies.com/post/which-appliances-use-the-most-energy-in-your-home
lots of houses have the cheapest insulation installed or none inside you attic!
The home hvac units is the most energy-inefficient units on the market. they also have one of the shortest life spans to by usage.
i get you simple far to lazy to do any research on the topic... that most data center bad bros.
ninjadude93 | a day ago
None of your links actually look at the impact of data centers. Your last link in the water section is from 10 years ago lmao did you even bother to read what you posted
firedrakes | a day ago
Oh you want cherry pick data sets... not whole sector. Am not doing you're research anymore for you . I see you did not answer my water usage.
Largest single use of fresh water is lawn grass, second largest fresh water is loss leaking pipes. Golf courses all in usa use more then all data centers in usa and containment it to.
You want a bais data set for your view point. No and good bye.
ninjadude93 | a day ago
Please, explain how using a data set that is years out of date provides anything meaningful to the conversation?
The question is about data center's impact. There's only been explosive growth in data centers in the last year or so. So how does citing data thats way out of date and doesn't include any data on the actual subject of discussion prove anything?
Thats not cherry picking you just dont know how data collection works
firedrakes | a day ago
Nope with with usage research you look at every sector, same goes for power or water. You dont want a logical answer. You want a bias narrative facts that fit in line with you.
Anyhow bye .
m-e-k | a day ago
He probs used AI to find them
potporni_vektor | 21 hours ago
Confidently incorrect, impressive
LurkerFailsLurking | 20 hours ago
Not really but ok.
truthsayer90210 | 21 hours ago
That's most of math lol
runs_with_airplanes | a day ago
This guy maths
himwhoisiam | a day ago
Interesting that a large statistical model used solve these problems doesn’t count as a new or innovative approach to solve these problems. There’s certainly no new understanding or application of mathematics at play here. /s
staebles | a day ago
Can't we tell it to show its work?
Head_Midnight666 | a day ago
I'm not a math person. I don't even know calculus. However, long ago, I made attempts at writing predictive algorithms that incorporated SVDs. What was interesting to me was that undefined abstractions were actually more accurate at making predictions with human defined abstractions. While I wasn't necessarily able tell the features of the SVD, they worked. What I seem to be understanding you to say is that these proofs don't really help humans understand and use math better, but that's probably not what's important? What's important is that it helps AI understand math better?
While it's hard for humans to accept that AI is going to be our superior, I think that is coming, and as scary and unthinkable as it sounds, I think it will be what ends up making the breakthroughs. I can look at the contents of a SVD used to make a prediction, and I know that it somehow makes the prediction more accurate, but it doesn't mean I know why. It actually doesn't actually matter that I know why to have the more accurate result.
I'm also aware that my ignorance is astounding, and there are probably geniuses lurking around here that have a far better understanding of all of this than me, and maybe I'm misunderstanding things entirely, but that's my... ignorant... take.
2Throwscrewsatit | a day ago
I’ll wait until it’s peer-reviewed.
sak1926 | a day ago
It’s PR-reviewed don’t worry
NachoEvans | a day ago
It'll be peer reviewed by other AI, don't worry.
r2k-in-the-vortex | 19 hours ago
Its proof by counterexample, case closed.
LewdLewyD13 | 17 hours ago
Based on these replies... I'm worried...
Davesnothere300 | a day ago
It won't be. These are fake results
Old_Challenge1623 | a day ago
it connected the dots from 60 years of work
andrewalexander_ | 9 hours ago
Cope fleshoid
Aliktren | a day ago
isnt this exactly what we should be using computing devices for - computing ??!?
JarPicklez | a day ago
This is not computing
ZixfromthaStix | a day ago
What is it?
JarPicklez | a day ago
It’s theorem proving.
ZixfromthaStix | a day ago
How do you prove a theorem? Do you have to compute it? Or does it just happen organically?
JarPicklez | a day ago
It happens organically. Usually.
Computation is a straightforward linear process. Theorem proving, when done by humans, requires creativity and insight
r2k-in-the-vortex | 19 hours ago
But this is using automated theorem provers. Yes, it's computing.
JarPicklez | 18 hours ago
Automated theorem provers are their own thing, they’ve been around a lot longer than LLMs, and they can do a lot less
r2k-in-the-vortex | 17 hours ago
Yes, but those are how LLMs successfully do math. LLM itself can't prove fuck all, but it can work a theorem prover and make it prove theorems. Thats how it's known the proof is correct, it's not relying on "please, make no mistakes", it's relying on trusted theorem prover.
JarPicklez | 16 hours ago
That’s not how lean works. Lean isn’t a machine that you put theorems into and it spits out proofs. You still have to prove it, lean just tells you if your proof is correct or not(assuming you have meticulously translated it into the lean programming language).
carelet | a day ago
The computer is computing so technically yes, but it's kind of like saying when a chatbot says "How are you?" it computed "How are you?"
ZixfromthaStix | a day ago
I notice you still didn’t actually provide an answer, just a “technically yes, but”
Also… that chat bot is a computation. I’m an active AI dev student with experience training models and working in Machine Learning/AI, 6 months away from capstone. I know conversation seems really easy, but there are WALLS of code going on to get that output… it’s not some simple “if hello, ask how they are!” lol
You can prove that conversation requires computation by asking the average model how many R are in Strawberry. Because models use tokenization, it splits the word into “straw-berry” but only focuses on berry, and answers 2 instead of 3– but if you ask it to spell out and count manually, it will correct. That is evidence of computation. (This was a known issue a few months back so devs may have patched this issue.)
JarPicklez | a day ago
The chat bot is indeed computing its output but the activity it is doing is mathematical theorem proving. It’s no more computing than digital art is computing. If anything computation is a subset of math, not the other way around. The fact that chat bots are doing this IS a massive change that puts mathematicians like me in a very uncomfortable place.
ZixfromthaStix | a day ago
Computers have always been math based since the very first day. AI is an extension of that
JarPicklez | a day ago
You have no idea what you’re talking about. That’s like saying diamonds are worthless because humans are carbon based
ZixfromthaStix | a day ago
By that logic the abacus and calculator both had no significance to mathematics, only AI stands out
JarPicklez | a day ago
That doesn’t follow at all
Iser_name_ | a day ago
I just did the strawberry thing with ChatGpt and it got it correct.
carelet | a day ago
I didnt call conversation easy
I was simply saying it seems strange to call conversation computing even when it is done by computation.
As I said, technically it is computing
We could argue humans are computing whatever they decide to do or say, so I am saying it simply seems strange to call it computing as it isnt doing a few basic calculations at this point, but an enormous amount of them to simulate language
The point is that calling creating mathematical proofs computation is similar to calling having a conversation computation
turtleblue | a day ago
Great - now solve world hunger.
Present-Wonder-4522 | a day ago
Destroy all humans.
Can't be hungry if you don't feel.
ChaosMilkTea | a day ago
The problem is well understood: We have the food. Production makes more than enough to feed every human, but we lack the infrastructure to deliver it. Also, rich countries would be giving away free things, which the rich aren't a fan of on account of becoming less rich.
So if ai were to do something, it would probably be from two angles:
In both cases? The answer will probably be robotic labor. Robotics is several years behind the digital side of AI though. And then the robots have to become cheap. That probably means robots building robots, which is a whole system of infrastructure itself.
freexe | a day ago
The rich countries don't like giving food to poor countries because it undermines those poorer countries local food production and introduces huge issues of corruption.
It's not a matter of not wanting to - it's a problem that it tends to make the situation worse
No_Dig7056 | 23 hours ago
I’ve heard this argument with money and goods. How that may undermine industry and development, but I’ve never heard this argument about food. It makes some sense.
But wouldn’t that be solved by just buying from, investing in, and propping up those local food producers? Which would actually save time and money with shipping food. It also supports those local economies through circulating more money through them
It’s what organizations like the World Food Programe (WFP) already do.
I also think it IS a matter with not wanting to. Given that the biggest anti food aid voices (when I looked at this) were Cato and the Heritage foundation. Also the thing with Elon promising money for world hunger to the WFP and not following through.
Versatile_Panda | 22 hours ago
Reddit loves to trivialize it
Alternative-Suit5541 | 11 hours ago
You still need energy, oil is a no no for transport.
So you need something like fusion..
Raescher | 10 hours ago
The main problem is political. You cannot reliably feed people in a country in civil war or with too much corruption. The deliveries will just evaporate.
avid-shrug | 22 hours ago
Solving fusion may well help do that
Strange-Term-4168 | 22 hours ago
Don’t have kids if you can’t feed them. Solved.
unknownpoltroon | a day ago
Wow. This means fusion power is only 10 years away.
panmetronariston | 22 hours ago
Always has, always will be. (This glib answer subject to change)
CouchWizard | a day ago
I'd be very skeptical about anything from argon. I've noticed it just flat out lies like earlier versions of other frontier models
Reallyboringname2 | a day ago
Or, Argon just tricked scientists into turning Earth into a star!
neuronexmachina | a day ago
Abstract: https://doi.org/10.48550/arXiv.2610.07304
>Analytic non-axisymmetric 3D MHD equilibria without stellarator symmetry
>Analytical solutions to the three-dimensional ideal magnetohydrodynamic equilibrium equations (∇×B⃗ )×B⃗ =∇p and ∇⋅B⃗ =0 provide benchmarks for stellarator equilibrium codes. Two families of non-axisymmetric solutions were recently discovered by Landreman [arXiv:2609.26742] as counterexamples to Grad's conjecture, but both satisfy stellarator symmetry. Here, we generalize both families by introducing a symmetry-breaking parameter into each of the solutions, resulting in new non-axisymmetric solutions that break stellarator symmetry and reduce to the stellarator symmetric solutions when the newly introduced parameter is set to zero.
Tone-Deft | a day ago
“Instead of proving that an impossible problem could finally be solved, the team used AI to extend existing solutions into configurations that earlier examples had not covered.”
They used AI to come up with another solution. A solution that went against the assumption that the answer required symmetry.
Maybe they were inspired by Sheldon and Amy’s super asymmetry theory when they punched their commands in to the AI.
greennurse61 | a day ago
Maybe it will help their friends save the universe.
thunderdome06 | a day ago
Tf you on about bro?
greennurse61 | a day ago
Sheldon and Amy’s friend Stuart.
ZixfromthaStix | a day ago
It’s a show reference to a spin off
care4not | a day ago
Buy when i ask him to fix my python snippet he fails miserably
Eelroots | a day ago
Yep. Gemini is a joke.
AnyHoliday1775 | a day ago
Gemini in particular 😭 I’m shocked argon is doing stuff like this when it’s barely usable on the consumer version
Gentle_Agitator | a day ago
Also, ai just convinced me to buy a ring compressor for my car that is 70mm. The piston is 70.6mm. Check those results good, please!🙏
---___-------___--- | a day ago
Well, I guess that if you'd compress it a little...
Davesnothere300 | a day ago
I dont believe them. Ai is over confident, wants to please and rarely checks its own work.
AnyHoliday1775 | a day ago
I feel like Gemini in particular is incredible overconfident…astra works more like an evaluator of different options comparatively to Gemini which feels like it has its own preconceived ideas and pushes those ideas
walksonfourfeet | a day ago
Robot weight lifter lifted more weight than any human has ever lifted! Weightlifters are shocked! Is the future of weightlifting doomed?
louisa1925 | a day ago
Eh. Check it by real scientists. I'm not about to trust a machine that probably hallucinated.
Curiosity_456 | a day ago
The paper has been out for a while now, it didn’t just come out today.
strange_username58 | a day ago
You can proof check pretty easily with a computer. Also read the article.
Low-Temperature-6962 | a day ago
It's OK to be skeptical. But if a lot very smart scientists have checked the results that increases the odds that its sound.
Davesnothere300 | a day ago
Agreed. If you use AI, you would know that AI claims should be taken with a grain of salt.
UnholyAbductor | a day ago
Whenever I see articles like this I’m always kinda worried “did it provide an actual solution? Or did it cut corners and just provided a solution that would work, if reality wasn’t in the way?”
dramaking37 | 23 hours ago
Outputs 400k lines of text : ✔️
Solved!
Super_Huckleberry663 | 18 hours ago
Ai proving once again it is one of our greatest scientific advancements. It's only going to get better, I look forward to our future
Typical_Warthog_2660 | 13 hours ago
if gemini really cracked this in a few hours thats wild but the headline is doing olympic level lifting lol. i still wanna see it survive review and actually teach us something instead of just slamming known methods together really fast
soliloquyinthevoid | 11 hours ago
> actually teach us something
With a comment like that it's unlikely you are capable of being taught anything
sexylegs0123456789 | 7 hours ago
I am curious about why you think this.
spiritanimalslug1 | 11 hours ago
dear ai, please show your workings.
watisgoinon_ | 9 hours ago
The cope is off the charts. We are at warp 3 cope speed.
Atrium41 | 8 hours ago
I'll need a video from Tyler Folseto understand this
Distinct-Pain4972 | 6 hours ago
In a few years, mathematicians will realize that these solutions are in fact incorrect and require leaps that aren't mathematically possible.
dr_edo_available | 3 hours ago
Give the link to the article/reference
Nerevarius_420 | a day ago
insert long, overdrawn, exasperated sigh here
Who verified the math?deathakissaway | a day ago
The people pushing AI telling us how great AI is.
ForMeOnly93 | a day ago
Sure, sure.
VIP_NAIL_SPA | a day ago
Gross