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jdb1729 18 hours ago [-]
> It would be good to have a formalised proof of the o(n log n) result, for example in Lean. As far as I can tell, one does not yet exist.
The result is fully formalized in Lean by @platypii and myself (on Mathlib's TM2 machine), with ongoing improvements to the exponent (currently at 1-2^-14.38): https://github.com/platypii/integer-mult-bounds-lean
Contributions are welcome!
17 hours ago [-]
marwahaha 13 hours ago [-]
[dead]
mariofdistrust 10 hours ago [-]
why does the website force people to log in to see the proofs? also, why does it not have something like a "export all lean" button to download all of the lean code that proves a theorem (dependencies included) so it can be easily checked offline? finally, the campaign you gave has a mission "Integer Multiplication in O(n (log n)^(1-κ)) Time" and it's listed as completed, but i checked the graph and it directly depends on something that's listed as "Open" ("Full multiplication bound at κ = 0.000051016920170078"). is it fair to call it completed then?
lionkor 10 hours ago [-]
It's missing a privacy policy, so not sure why you require login for users
0c3ca83 9 hours ago [-]
Why would someone contribute to an AI project without getting paid?
aleph_minus_one 8 hours ago [-]
> Why would someone contribute to an AI project without getting paid?
Because there exist people who are in deep love with mathematics, but weren't able to get a research position (or cannot afford to take a temporary position at some university for which they would even have to relocate).
So, your question is more like: why would someone play a video game without getting paid?
uv-depression 7 hours ago [-]
Those people generally care about learning, IME. "Why would someone play a video game without getting paid?", more like "Why would someone pay money to have a bot play a videogame without getting paid?". People IRL do, of course; they tend to be antisocial and make the game worse for everyone else.
sam0x17 2 hours ago [-]
Notoriety also has value, not that that is OPs motivation, but worth noting
0c3ca83 7 hours ago [-]
For now, there's plenty of mathematics that hasn't been enshittified by AI bros.
For this work, you're either inserting coins into Codex, effectively donating money, or you're shoveling your way through the slop that the purchasers of that repo couldn't be bothered to clean up themselves.
aleph_minus_one 6 hours ago [-]
>
For this work, you're either inserting coins into Codex, effectively donating money
Rather: as a necessary evil you give money to OpenAI/Anthropic because you really want to see (ideally fast) progress on this mathematical/algorithmic problem.
0c3ca83 6 hours ago [-]
That's like saying "you love cooking, so for fast progress you insert coins into doordash to make fast progress on a meal". If you do that, you don't love cooking.
It's fundamentally incongruous with someone that "loves the game". And I hope that you have a good excuse for ruining it, hopefully a seven or eight figure excuse.
aleph_minus_one 4 hours ago [-]
> That's like saying "you love cooking, so for fast progress you insert coins into doordash to make fast progress on a meal". If you do that, you don't love cooking.
Rather, it's like saying "I love eating good food."
One approach is to learn to cook excellently, buy high-quality ingredients etc. Then invest time to cook a delicious meal. This is like learning math, becoming exceptional in one area, and then work on the problem.
Another approach is to go to a well-rated restaurant, order some food there, and pay a decent amount of money for the food. This is like buying AI tokens, and setting up some agents to work on math problems.
Both approaches are suitable. And I guess/hope many people who really would love to contribute to progress in mathematics would really prefer to go the first way. But because
- they are not highly gifted
- have a time-consuming daily job and/or family
- simply cannot afford the high opportunity cost to get to a suitable level in math
- ...
the only way such people can contribute to progress in math is the second approach. That's why these people behave like they do.
I am quite confident that most of such contributors don't like that their money lands in the hands of OpenAI/Anthropic.
So: Which approach would you recommend these people who are really eager to contribute to progress on some mathematical problems?
0c3ca83 3 hours ago [-]
So, what's the game you claim to love? Wading through piles of poorly written proof and trying to de-slop it?
It's like saying "look, they may not be good at painting, so what can we do other than letting them take a can of spray paint to the contents of the Louvre?"
If you can't contribute, enjoy it passively, instead of doing damage.
Kotlopou 2 hours ago [-]
(not GP)
Coming back to the particular project at the top of this thread (not TFA but the top comment), I don't see a problem with contributing to Lean verifications. One of the problems with the OpenAI dump is that half of it might be wrong, and formalizations resolve that. This is particularly relevant for this result, which is one of the few claims that didn't go in the direction one would expect before the drop.
You can for sure criticize OpenAI for not having done it themselves (I hereby do!). I guess you could make a case that engaging with their results somehow legitimizes their actions, but a) I don't expect OpenAI to care about this enough for it to matter, and b) you aren't making this case but a stronger one that any engagement somehow ruins mathematics and must be done for money. I don't think so! [1]
I personally am a complete amateur, but find this result one of the few overwhelmingly positive aspects of the drop. It's something people weren't expecting, so it probably saves time that would have been spent on trying to prove the O(n log n) lower bound; it's about a question that is extremely natural ("how fast can you multiply really big numbers", that's like the eight-year-old's vision of what math is!) and shows that there is absurdly complicated structure even behind the simplest things, which is why math is not getting "solved"; it didn't kill the question but started a whole program of trying to optimize the algorithm (currently AI-assisted, but I hope that if all AI magically disappeared tomorrow, in a few years people would be able to get there unassisted), plus TFA lists several other problems this algorithm could help with at the end of section 4.
That's the game I love, at least.
[1]: This was best put by Nestor Guillen (https://terrytao.wordpress.com/2026/09/13/happy-those-able-t...): AI with all its drama (of economic and political origin) is separate from LLMs the technology, which only work because they build on a corpus of intelligent text, including a lot of math. Had it been made by a responsible organization, and had it appeared more gradually, it could be treated as an amazing tool for literature search and testing how far a brute-force approach can go, which would only help test the value of new ideas. There is some shard of this in our actual situation as well, beneath all the politics.
jesuswasjew 8 hours ago [-]
It is just going to help the capitalist of the world. Last time I checked many people are having $0 in their bank balance. Contributing is officially a scam for humanity.
hlynurd 8 hours ago [-]
For some people the "love of the game" trumps economics
0c3ca83 7 hours ago [-]
Exactly, which is why, if you love the game, I don't see any reason to contribute to a project that outsources the game to AI without getting paid heavily for destroying any chance of people making a living from the game.
hlynurd 5 hours ago [-]
I mean, yeah, from an economic standpoint.
vickychijwani 7 hours ago [-]
Indeed. I would say that’s the healthy way to approach any such pursuit. Else the economics taints everything if you let it.
x187463 9 hours ago [-]
They're all equally contributing to mathematics?
lioeters 8 hours ago [-]
True, but something is qualitatively different. Similar to open-source software projects, where people used to participate for the love of the game. Now that machines are doing the work, there is less human connection and feeling of community.
Why bother contributing when there's no one on the other side. There's value being produced, but the beneficiary is mainly the corporations who run the models. Even if the result eventually benefits society in general, the credit goes to them. Not that "credit" or "clout" means much practically, but there used to be emotional and social value for the people involved.
vickychijwani 8 hours ago [-]
There is still emotional and social value. The AI is a tool in the hands of a competent operator.
You’re blinded by your hate for corporations, if you cannot see value here. The actual human who’s working on it responds, and you can’t help but make it about the AI that you believe is somehow only benefiting the corporation.
0c3ca83 8 hours ago [-]
David Harvey, the author of the paper linked here, hasn't even read the paper on the topic. He says as much in the paper, directly. The repo linked in response shows little sign of human authorship.
Has anyone actually gone through and fully understood the contents? My bet would be that nobody has looked, let alone understood, anything in there.
So, is collaboration just paying for more tokens, or is it doing the human understanding that the authors couldn't be arsed to do?
Either way, I hope nobody is foolish enough to fall for it.
gus_massa 4 hours ago [-]
> Has anyone actually gone through and fully understood the contents?
It happened before. For example in Wiles' proof of Fermat's Last Theorem it took like half a year until someone found an error, another year until Wiles and Taylor fixed the proof and another half year until it was reviewed and published. Add a few years until it was widely understood (where widely is a small set of people that excludes me for now). Now there are even shorter proof.
The new thing here is that we are seen the internal discussion published online. Also, the chatbots like to make long pdf, so expect to see a pile of intermediate pdf created by chatbots+humans until we get a nice proof that is widely understood (this is more related to my area, I have a hope that I may understand it).
vickychijwani 7 hours ago [-]
If you know something to be true, without knowing why, does that reduce the value of said truth to zero? And what if that truth has positive effects on your ability to invent and leverage technology, treat/cure disease, etc? Would you still say it is of no value and people are “falling for it”?
This is called faith, and as strange as it may sound, in a few decades this kind of faith in the machine will be commonplace, because it will prove its utility over and over and over again, and save mountains of human effort in the process. In 3-7 years we’ll start to see major practical applications from AI-led advances.
As a second-order effect we may even see the rise of an actual religion around AI. I’m not saying these are all positive effects, but it’s what I see happening - and like all technological revolutions there will be a huge number of positive effects alongside some deeply disturbing developments.
lioeters 7 hours ago [-]
> faith in the machine .. religion around AI
Yes, that I agree is definitely happening already. Like Arthur Clarke's famous saying about sufficiently advanced technology being indistinguishable from magic.
And it's true that these changes are not all positive or negative, that there is actual social value being produced by machine-generated mathematics, software, writing, visual works, etc. And that it's changing human culture in ways difficult to imagine while we're in the midst of the transformation.
I'll add another prediction: AI will become more and more "human" until it becomes indistinguishable practically. Eventually we will merge with it to become something more than human. It will be normal to have robot friends and sexual partners, and one day machine-human hybrid children.
By that point, corporations and governments will have merged to create a "benevolent" totalitarian system with complete surveillance and control, with an elite class overseeing the rest of humanity as domesticated population, like an automated farm. Oh wait we're already there, haha.
0c3ca83 5 hours ago [-]
> Eventually we will merge with it to become something more than human.
Assuming it becomes capable end, why would it want to merge with meat? That's like predicting that humans will merge with goldfish.
octoberfranklin 4 hours ago [-]
This is as silly as asking "why did the neocortex want to merge with the hindbrain"?
It's simply predicting that the neo-neo-cortex will remain merged with the thing that it grew out of.
Intelligence is just a tool.
0c3ca83 2 hours ago [-]
Wow, that's idiotic. The neocortex isn't biologically separate. AI systems are.
0c3ca83 7 hours ago [-]
Why should humans care about any of this when the machine can just refill our cat food dish and wave some feathers on a string to keep us entertained?
Either what you say is true, in which case I see no reason to care about truth, science, or math (the machines will figure it out for me), or it isn't, and people are falling for dealing with slop on behalf of people too lazy to look at the turds they purchased.
Either way, I don't see any reason to participate in AI projects without getting paid.
vickychijwani 7 hours ago [-]
I’m happy to engage in meaningful discussion; this is just sci-fi exaggeration (Wall-E?).
Sci-fi has tainted our collective imagination of a tech-forward future - it doesn’t have to be that way.
Two things can be true - yes, AI can benefit mega-corporations AND drive spectacular human progress at the same time. Is all of it “good” by my ethical standards? No. Is it still valuable? Yes, immensely.
0c3ca83 7 hours ago [-]
You're literally talking about a religion. You weren't having a serious discussion from the start.
anonymousDan 4 hours ago [-]
We need to design an AIGPL - you can only use the collective expertise that created our code to train a model if the model, its training data and potentially its output are also available via AIGPL.
pixl97 7 hours ago [-]
Why not embrace nihilism now instead taking the long way to get there?
At the end of the day there is no reason, there is no purpose but the one you make for yourself. The universe doesn't care and will slowly wind itself down to thermal noise.
If they want to make a lean solution and publish it then they can because that's what they want to do.
And if you're breaking this much already, the future is going to be really really hard on you. You are not ready for what is coming.
dyauspitr 4 hours ago [-]
You keep saying this human connection thing, but it has no bearing on moving the frontier forward. A lot of these problems have remained unsolved for hundreds of years. What is your claim here, that they should remain unsolved artificially for 100 more years so that human mathematicians can have careers and community?
0c3ca83 33 minutes ago [-]
What's the point of solving them if not to serve people? How much harm are you interested in doing for answers that the people supposedly interested in them didn't even read?
dyauspitr 28 minutes ago [-]
It will serve people over a much longer time span though for use in whatever machinery ends up giving us something approaching a utopia.
0c3ca83 23 minutes ago [-]
What we're building is a hell, and I want nothing to do with the utter dystopia you're imposing on me.
Please, just stop causing damage.
If you're going to insist on building your nihilistic, brainless, anti-human world, where the best outcome is that the AI remembers to refill the pet food bowl and clean the litter box, first find a way to let people opt out of your brand of hell.
0c3ca83 9 hours ago [-]
This is why mathematics are having serious discussions about math without mathematics, right?
The people using AI should be paid well enough that they can justify selling out their peers.
naasking 6 hours ago [-]
Either you care about advancing the frontier of knowledge or you don't. If you care, then you should use every tool available to you. If you don't, then society has no obligation to fund your personal hobby interests. Calling this "selling out" is ridiculous.
0c3ca83 6 hours ago [-]
What knowledge? The authors haven't even read the output.
naasking 6 hours ago [-]
Humans aren't the only systems that can wield knowledge now.
0c3ca83 5 hours ago [-]
As a human, why should I care? I'd like to advance human knowledge, not canine knowledge.
There are likely remote planets with alien intelligence on them -- aren't you glad we've explored distant stars and planets?
naasking 2 hours ago [-]
Remote alien knowledge doesn't give us new useful tools unless we make contact with them. Fortunately we now have contact with alien intelligence right here that is providing us with useful tools.
0c3ca83 1 hours ago [-]
I'd rather discover them, than have some entity change my diapers. You can go into your brainless dystopia alone.
SeanAnderson 9 hours ago [-]
What does AI have to do with it?
0c3ca83 8 hours ago [-]
Why are you here engaging with humans, if you could ask AI to explain?
naasking 6 hours ago [-]
Why would someone contribute to open source without getting paid?
0c3ca83 6 hours ago [-]
Mostly because it's been a social project where humans collaborate with each other. As AI usage expands, though, I increasingly don't expect to be contributing without getting paid.
naasking 6 hours ago [-]
It's a social project where humans collaborate because they want useful tools, and sharing the useful tools they built with others gets their own tools improved. The reciprocation of sharing knowledge and knowledge artifacts compounds. The advent of AI does not change this formula one bit, if anything AI increases the compounding factor. You are of course free to do as you wish with this new reality.
5 hours ago [-]
dyauspitr 7 hours ago [-]
Why would someone help solve cancer if AI worked on it?
0c3ca83 7 hours ago [-]
This isn't solving cancer. The payoff is, supposedly, the understanding that the purchasers of that repo didn't care enough to bother with.
Also, I fucking hope society values cancer research enough to pay the researchers. If we don't, maybe we deserve what we prioritize.
ironborn123 12 hours ago [-]
Well lets give these models a grand challenge then.
I conjecture the true growth rate is O(n(log(n))^(1-f(n))) where f(n) is a slowly increasing function of n.
So larger the integers, more efficient the multiplication gets.
AIs of the world, make this your target, and have centuries worth of fun!!
sebzim4500 11 hours ago [-]
You forgot to specify that f(n) approaches 1 so that is an easy challenge
ironborn123 11 hours ago [-]
yes f(n) tending to 1 as n tends to infinity is the worthy goal. how slowly it gets there would determine whether the growth is effectively O(n) or something slightly larger but that refinement can come later.
Kapura 18 hours ago [-]
> I have not actually read the paper. In fact, there are three closely related papers [TC26a], [TC26b], [TC26c], and I have not read any of them. I tried, but soon gave up. The math(s) dump does not appear to be strong on exposition. Instead, I asked Fable to explain [TC26a] to me. Fable seems to understand the argument and thinks that it is correct.
this does not feel like a healthy place to be
svat 8 hours ago [-]
The very next lines say:
> At first I did not understand Fable’s explanations. But I asked questions, and then asked more questions, and drilled down towards the core of the problem. Gradually I started to understand parts of the argument. I have seen enough now to arrive at the tentative belief that the main result is possibly correct […] In the sections below I will try to explain what I have learned so far, and why I lean towards thinking it might be correct.
So the whole paper is about things the author does understand. (BTW the author David Harvey was behind the big advances in integer multiplication, in 2014 and 2019/2021.)
uv-depression 7 hours ago [-]
This is just textbook Gell-Mann amnesia. If the author doesn't read the paper how can he possibly know that? Even in this case when it's someone who has related knowledge, how can you know that you're being told something that's actually in the paper? LLMs confidently state nonsense all the time because there's no difference between how they produce a correct or incorrect answer. LLMs seem incredibly good at convincing people that they're now experts in everything. "Drilling down" is just giving more and more opportunities for that to happen.
svat 4 hours ago [-]
- The author is already the world's best expert on this topic.
- You're right that it's possible Fable came up with a different o(n log n) algorithm that is not in OpenAI's paper, but (apart from being unlikely) it doesn't matter, as he talks of the belief that “the main result is possibly correct”, not necessarily that OpenAI's paper is: whether the argument comes from the paper or from Fable, the question of interest is whether multiplication is really possible in o(n log n) time. The point is that there's a genuine idea here, somewhere between OpenAI's paper and Fable's explanation of it and the author's understanding of it.
- This is in fact how mathematicians have evaluated papers for decades: is there any way this result could be true? Is there an actual idea here that could help? Do I understand enough of the argument to be convinced it's likely true, modulo some details that need to be filled in? The last step (actually filling in the details) happens when teaching it to someone else or writing it again in one's own words.
j2kun 5 hours ago [-]
Gell-Mann amnesia doesn't really apply to the world expert on a topic discussing a paper that builds on his own work. It might apply to a world expert on a topic discussing a topic on which he is not an expert.
boothby 4 hours ago [-]
If I didn’t know David Harvey personally (though a decade removed) I would agree. I’m reconsidering my stance on AI lately and while I still find much of this disconcerting, I begrudgingly accept that his approach of "feed the paper into a different model and make it explain the technical bits until I fully understand it" is perhaps an increasingly valid approach to digesting mathematics. One needs to be incredibly principled in this practice. But that’s exactly how I’d describe David.
flockonus 16 hours ago [-]
This clearly hit a nerve in very interesting ways!
Suppose we have a very convincing math PhD impersonator, agreeing and making some prepared smart-sounding comments saying they understood the proof and it seems to make sense - would it?
Well, most would say - NO! Obviously not, knowing this person was an impersonator. Others now knowing could be convinced of the validity.
Very simple thought exercise that allows to conclude, someone "saying they understood" (or agree) bears absolutely no weight to something's validity.
bamb008 15 hours ago [-]
The idea that OpenAI's proofs are fake is contradicted by the response of mathematicians. Were they fake, mathematicians would not consider this release important enough to comment upon[1,2], as vixra always provided a steady supply of such writings. Nor would they be able to write expositions and simplifications, of which several are published each day on arxiv (just search OpenAI on arxiv to find them).
But perhaps mathematicians are no longer able to be the best judge if a proof is valid. Yet nobody but them is able to even understand what is claimed to have been resolved.
> But perhaps mathematicians are no longer able to be the best judge if a proof is valid
It seems they are still the best judge of whether a proof is useful or insightful though. AI is going to reward hack and try to eke out a win whatever it takes - which is fine if you regard these problems as trophies to be won, but not so fine if you take them in the spirit intended.
The famous problems that OpenAI (primarily) are trying to trophy-shoot with AI are famous because some human mathematician such as Erdos had the taste to judge them as fruitful grounds for new math to be developed in the pursuit.
Until we're at the point where AI is capable of things like creativity, curiosity and continual learning, AI will remain reliant on humans to fundamentally advance math and give it new tools to apply. For humans to do this it is helpful if they are inspired by the challenges set by their peers, which is why AI solving these human challenges is so counter productive - the snake is eating it's own tail.
im3w1l 2 hours ago [-]
> It seems they are still the best judge of whether a proof is useful or insightful though.
How can you be so sure of that? Like we know that human mathematicians can judge usefulness or insightfullnes. But we don't really know whether AI can do it even better. Like I don't think anyone has set up an evaluation of that.
miksuko 15 hours ago [-]
> Were they fake, mathematicians would not consider this release important enough to comment upon[1,2], as vixra always provided a steady supply of such writings.
Few articles on vixra are made by companies with ~$1T valuations.
14 hours ago [-]
Tyr42 10 hours ago [-]
The truth is still tying it's shoes, in that metaphor.
Analysing all the proofs and finding contradictions or not takes time.
0c3ca83 9 hours ago [-]
> But perhaps mathematicians are no longer able to be the best judge if a proof is valid.
Not the way they're written, and not at the rate they're dumped on the math community, at least.
CurbStomper4 10 hours ago [-]
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ordu 6 hours ago [-]
> Very simple thought exercise that allows to conclude, someone "saying they understood" (or agree) bears absolutely no weight to something's validity.
Well, it is always was so? It doesn't matter if the person claiming things is impersonator or not, either you dive into the topic yourself and decide for yourself, or you wait for a consensus among specialists to settle.
I mean, in 2026, after the rise of the Internet, where lies are widespread, AND after the rise of an AI, that removed such anchors of truth as a video proof for example, I believe everyone smart spend some time meditation on an operational definition of truth and now has something that doesn't depend on knowledge that the source is not an impersonator.
p-e-w 16 hours ago [-]
Now imagine that PhD “impersonator” has, in the past few months, made more demonstrable progress in mathematics than anyone in history.
I think that would change the conclusion of the argument a little.
soVeryTired 15 hours ago [-]
Or maybe the PhD impersonator discovered a soundness bug in a component of Lean and was able to exploit it.
With no humans able to understand the proofs the impersonator has produced, there's no way of knowing.
mrob 13 hours ago [-]
There are many independent implementations of Lean's proof checker:
It's highly implausible that the same bug is present in all of them.
pera 11 hours ago [-]
Is that true? I'm actually asking because all the ones that I have seen mentioned from the arena were generated with Claude and I just checked a few random ones and they all have commits by Claude.
I don't know how much was created by LLMs but can you still call these independent implementations?
StilesCrisis 10 hours ago [-]
Yes? Very little about Claude is deterministic. If given the same sufficiently complicated problem, but via slightly different input prompts, it can generate entirely different code. It would be shocking if they all had a single shared bug.
Eddy_Viscosity2 8 hours ago [-]
If it's a complicated but narrow problem then regardless of the prompt its possible that Claude will always converge to the same solution type from its training data. So it will generate different version of the same code that have the different implementations of the same procedure that 'works' but is incorrect in some subtle way.
foldr 9 hours ago [-]
I don't think it would be that shocking, if it's a conceptual confusion that's easy to make.
gus_massa 4 hours ago [-]
If the AI had found a bug, I expect it using the bug to prove everithing and geting 700 pdf per week https://xkcd.com/704/
stouset 15 hours ago [-]
This is true of literally any proof of sufficient complexity, human-authored or otherwise.
It takes time to convey the core ideas. It takes time for other mathematicians to come up to speed on the new techniques and approaches or the novel combinations of existing ones.
The actual experts in this field have asked for the argument to be explained to them, and are now at the point where they believe this is plausible. Another person in this thread has produced a Lean proof and presumably is not trying to pull a fast one on us by exploiting a bug in the solver.
TL;DR chill bro.
aljgz 15 hours ago [-]
More realistically, that impersonator has created lots of output, and the correct ones are selectively publicized (and few particularly interesting bad answers).
varjag 14 hours ago [-]
Do you really believe Open AI made more progress in math than any of the human predecessors? Come on.
Galois had built modern algebra before dying at 20. What agents did so far is flash in a pan.
ithkuil 13 hours ago [-]
Wait and see when _they_ are 20 years old!
varjag 13 hours ago [-]
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peterfirefly 2 hours ago [-]
He should have written longer letters and duelled less.
p-e-w 10 hours ago [-]
> Galois had built modern algebra before dying at 20.
No he hadn’t. You are clearly consuming mathematics through a pop culture lens.
varjag 10 hours ago [-]
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OxO4 14 hours ago [-]
The recent progress does not imply that the "impersonator" has a deep understanding of mathematics. By formalizing a problem in Lean, constructing the proof is essentially a complex search. Because Lean is the arbitrator of correctness, the "impersonator" can make as many mistakes as they want until they find a solution that passes the checker. That is very different from checking a paper for correctness.
CrimsonRain 13 hours ago [-]
that's a hard cope. You think humans don't make thousands of mistakes to come up with something good?
david-gpu 14 hours ago [-]
Good luck writing a Lean proof of a nontrivial theorem by giving a keyboard to a thousand coke-fueled monkeys. If only it was that easy.
TheOtherHobbes 12 hours ago [-]
Strange to be against this when it works so well in other industries.
FeepingCreature 11 hours ago [-]
1. Well, mathematics isn't other industries
2. It really doesn't work well in other industries either.
david-gpu 9 hours ago [-]
My point is that the AI agents are not trying things at random.
abricq 13 hours ago [-]
The same is happening with AI-written codebase. You don't read AI written code, you ask an AI to explain it to you. Not that it's either a healthy place to be...
aleph_minus_one 10 hours ago [-]
> You don't read AI written code, you ask an AI to explain it to you.
Rather: you reject AI-written code if it is not of really good quality.
uv-depression 6 hours ago [-]
In my experience dealing with people who use LLMs, approximately none of them do this. The feeling of productivity LLMs create is maximized if you just blindly accept everything they produce, and apparently as a field we've broadly given up on what little quality we had before to chase that feeling (and to boost the valuation of a couple companies).
estetlinus 16 hours ago [-]
> this does not feel like a healthy place to be
I believe you just captured the zeitgeist, friend.
jrflo 18 hours ago [-]
Why not? If mathematicians in the field cannot understand the papers, but tools at our disposal can, why not use them? What is "unhealthy" about that? Just seems like it's the reality of the world now.
chowells 16 hours ago [-]
If you can't understand what the machine has done, math is not being done. Math is not pushing symbols around to find sequences that follow all the rules. Math is the process of understanding how those sequences of symbols map to meanings. If you can't follow the proof but have validated it's correct, all you know is that it's a proof of something. Is it a proof of what's claimed in the adjacent plaintext? Who knows!
Math is about clear communication between people first and foremost. That's why Shinichi Mochizuki's claimed proof of the ABC conjecture isn't accepted - he's unable to communicate it to enough mathematicians to get a consensus on its validity. LLMs generating Lean proofs are basically following that same schema. Sure, something has been proven. But what? Where's the clear communication explaining it to the satisfaction of other mathematicians?
badlibrarian 16 hours ago [-]
> Math is about clear communication between people first and foremost.
I can't imagine I'm the first one to point out that this is perhaps the greatest unsolved problem in mathematics.
> Where's the clear communication explaining it to the satisfaction of other mathematicians?
Coming in about a year for $20/month, sure seems like.
calf 13 hours ago [-]
I mean it's just patently absurd. It is trendy to use this hubristic, and frankly bureaucratic, definition or requirement on math.
People can do math alone. They don't have to share it with others. Mochizuki was doing math (with errors). The aliens from Arrival could do math and we wouldn't necessarily be entitled to that. It doesn't make these things not valid math.
The only benefit of defining math through this vulgar social reductivism is to benefit the status quo. So you see some experts and expert-adjacent commentators using these math slogans but never critically justifying those words. Case in point, the above commenter asserts "...math is foremost about communication.", but the very next sentence does not explain why it is foremost, it just uses the sentence as a premise for the following sentence. Guess what, that is exactly how ideologues argue, by question begging fallacy. Ironic that STEM people who ought know better fail to catch this simple rational mistake.
TheOtherHobbes 12 hours ago [-]
Math that isn't communicated is valueless - by definition.
Applies to STEM generally, because the foundation of STEM is peer review and independent testing and confirmation.
Even if the practice never quite lives up to the theory (true) that's no excuse to hand-wave away the requirement as if it doesn't matter.
Proofs that can't be confirmed are valueless because no one learns anything from them. The value of a proof isn't just in proving something but in inventing new techniques and insights that can be reused for other applications.
Brute force searches are fundamentally less valuable, because the creative foundation of math is discovering leaps of logic that solve problems with insightful shortcuts that can be generalised and reused for other applications.
yayachiken 10 hours ago [-]
> Math that isn't communicated is valueless - by definition.
That is quite a bold claim.
Math can always be used as a tool just to achieve results. This can include even novel math. Due to the maturity of the field it is just becoming more and more unlikely that somebody will come up with something novel without realizing the significance.
One example: Quicksort was almost not published because Tony Hoare initially considered the idea behind the algorithm trivial. In the end, all he wanted to do was to sort some numbers.
Maybe there is a polynomial time SAT solver running somewhere just because some overworked engineer implemented it for a completely different problem. And he was just annoyed that all existing libraries were so slow and he doesn't realize that he is sitting on a proof that P=NP. Who knows.
jrflo 9 hours ago [-]
Human understanding is important, but I don’t think it’s the end all be all. There’s no reason to expect humans to be the best mathematical reasoners in the universe. And if math is important, beyond being a human art form, I think we should explore that math beyond our understanding.
imadr 8 hours ago [-]
What part of "math is the process of humans understanding math" did you not understand or agree with?
lioeters 8 hours ago [-]
I'm on the side of human understanding being fundamental to mathematics, but I'll try arguing from the other side.
If we consider the reductionist view that "math is just a tool" to solve problems, and machines can generate new tools that are actually useful, then it can be argued that we don't need to understand how those tools work internally for us to benefit from its usefulness.
My hope is that such a view is given its proper place as a worthy pursuit but that it does not consume the entirety of the field of mathematics as a human activity with culture, philosophy, and art. And I feel the same for the field of software.
curt15 10 hours ago [-]
"Mathematics is commonly explained and recorded in symbolic and concrete forms that are easy to communicate, rather than in conceptual forms that are easy to understand once communicated. Translation in the direction conceptual -> concrete and symbolic is much easier than translation in the reverse direction, and symbolic forms often replaces the conceptual forms of understanding."
I mean, I could not understand 95% of all the math out there due to skill issue, and it’s still valid. If machines can understand the math and humans can’t, we can’t validate it, but that doesn’t make the math itself invalid.
darepublic 16 hours ago [-]
It could be logically valid but irrelevant? Allowing for the atrophying of all our skills is dangerous? While simultaneously building ever more complicated edifices that control our lives? Not a doomer but you really just want to shrug and say it ain't no thing?
wvenable 15 hours ago [-]
Perhaps humans have peaked due to the fundamental biological nature of our brains and AI will eventually surpass us in mathematical intelligence.
That has been the ultimate goal of creating AI -- not to simply match human intelligence but to surpass it. If it surpasses us, does that make it irrelevant? Everything around us is powered by mathematics, most of it I don't understand, and soon perhaps everything will be powered by mathematics that everybody doesn't understand.
bonitochips 14 hours ago [-]
To what end?
“Hey guys, the human condition can be experienced by these machines more efficiently than we are able to! Now we can finally die off!”
Why are we enabling a world that outpaces us? Is this going to lead to anything other than displacement in the long term?
Do you ever stop and ask why we’re building the world we’re building?
To what end does a world we cannot comprehend or rebuild from first principles serve us?
wvenable 5 hours ago [-]
> Why are we enabling a world that outpaces us?
Because we kinda suck. Wars, poverty, destruction of the environment, etc. Maybe AI will be worse -- the are plenty of movies on that subject -- or maybe it will be better.
> Is this going to lead to anything other than displacement in the long term?
We may have already damaged the environment enough that we're going to die off soon anyway. So without AI, we were already on that path to "displace" ourselves -- I don't think that argument is that compelling.
Perhaps this is the natural evolution of intelligent life. Eventually the sun will die and everything we are will be gone forever, maybe this is the only way that something of us survives.
> Do you ever stop and ask why we’re building the world we’re building?
We're not building a world. We're just ants. Individuals making individual decisions that have nothing to do with the rest of the world. You don't have to look too far to find humans that literally care nothing about humanity as a whole and everything about themselves.
> To what end does a world we cannot comprehend or rebuild from first principles serve us?
We already live in that world. You wouldn't even be able to build a toaster from first principles without the knowledge and infrastructure that already exists.
ToValueFunfetti 7 hours ago [-]
I mean, ideally to the end of poverty, disease, climate change, war, starvation, etc. You can disagree about whether this would be the outcome in practice, but I don't think 'why might we want to create something smarter than us' is a hard question. Humans losing their status as top dog is really a trivial price to pay for the potential return.
sumeno 4 hours ago [-]
We could do all of those things with today's technology. They aren't technological problems, they are human problems
wvenable 3 hours ago [-]
So humans are the problem. I can't tell if this reply is agreeing with ToValueFunfetti or disagreeing.
wartywhoa23 14 hours ago [-]
@bonitochips
They killed your comment, but you should know that you've posted it on the world's #1 AI propaganda site, where literally trillions of dollars are at stake and ROI depends on the correctness of the discourse running among the meat LLMs foraging or lurking here.
indigo945 12 hours ago [-]
It's not that their argument is wrong or not worth discussing, it's that it is utterly irrelevant when the question at stake is whether the math is valid and useful or not. It's effectively whataboutism.
ImHereToVote 10 hours ago [-]
I think sometimes it's worth being concerned about something else. What about "this" is valid if the "this" is important to us.
Everything is potentially interconnected.
What separates things is course-graining, not ontological separation per se.
shaky-carrousel 13 hours ago [-]
No one here likes to hear their AI girlfriend isn't real.
curt15 10 hours ago [-]
> If machines can understand the math and humans can’t, we can’t validate it, but that doesn’t make the math itself invalid.
How can humans tell whether machines understand the math if they themselves don't? Math is a human language for quantitative reasoning.
If large sectors of the population decline in their ability to read and write <natural language>, how will they be able to evaluate machine-generated <natural language>?
tescreal 9 hours ago [-]
Perhaps, but two things:
1. most people don't understand this level of maths either
2. math, unlike most natural language, can be (dis)proven without a person understanding.
The second one is very important. While it is a matter of pride that some do understand, and (given enough time, in my opinion) will continue to understand, it is sufficient to be merely correct even if one does not understand why. This is a nice feature of math: it remains true regardless of understanding.
The obvious caveat is the machine needs to prove it in the end, even if we can not.
mlsu 16 hours ago [-]
Does math already exist and is discovered? Or is it a thing invented by people?
marcianx 16 hours ago [-]
The underlying tautologies always existed. Making them accessible to us as truth and usable for one or more functions is what's invented by people (and potentially by AI) via insightful proofs and narratives.
pavas 14 hours ago [-]
I think the nuance they are pointing towards is that we cannot with any certainty say one way or another, as this is an unsolved problem in mathematics, and may possibly remain unsolved forever.
Your opinion is a possibility but it is not grounded by any proof, as such it is nothing more than an assumption. I think the more intellectually healthy approach is to list out the possibilities and figure out what they entail, rather than to throw all your eggs in one basket blindly, which amounts to a leap of faith or a retreat to commitment.
But that's just my two cents, take it or leave it.
don_esteban 14 hours ago [-]
Can you be more explicit?
I absolutely do not understand what are you talking about.
The question whether the tautologies exists regardless of whether we know about them or not is absolutely not a problem that is studied by mathematics and can be proven by mathematics.
pavas 4 hours ago [-]
Correct, these questions lie outside the internal practice of mathematics. If you want to see where that computational mindset breaks down, read Carnap's "Empiricism, Semantics, and Ontology" regarding internal versus external frameworks (or have an LLM summarize it...), or look into Brouwer's Intuitionism, or better yet into Lawvere and Topos theory, where the "underlying tautologies" literally change depending on the categorical environment you construct (but this may take some time to wrap your mind around so don't expect to understand much right away). Or, given the thread topic, you can just prompt an LLM to interactively walk you through how classical logic fails in a constructivist topos, or if you have interests outside of applied math or mathematics itself, you can ask it about foundational questions in science and mathematics, which unlike the prevailing opinion in many tech circles, aren't resolved.
fwlr 14 hours ago [-]
Why does this not feel like a healthy place to be?
The author of those sentences is also the author of the main result that this generated improvement builds on, and prior to this improvement his work was thought to be unimprovable. He is, in all of the important ways, literally the single most qualified human on the planet to asses this result. And his assessment is 1) this improvement is real, 2) this improvement is exponentially bigger than his work and the work he improved on, 3) it is effectively impossible for him (and by implication, humanity) to understand the result as presented, 4) it is seemingly trivial for “frontier circa 2026”-class models to understand the result as presented, and 5) to the extent that he is able to understand it, it is only with significant frontier model assistance.
So the place we are at, at least for this highly intellectual field, is “intelligence is the property of ~2 corporations, you must pay them a fee and agree to their terms and conditions to access it”.
At risk of stating the obvious, this is not a healthy place to be because intelligence is now structurally subordinate to the profit motives of particular corporations and at the mercy of all the attendant economic/sociopolitical incentives that apply to them. If the landscape is such that it is in these corporations interests to withhold this intelligence from some and make it available to others, that is simply what will happen.
One trivial example, of which there are an arbitrary number: An elderly leader is convinced to issue a decree that all number-math-computation intelligence is to be made available only to the intelligence agencies of his nation, and failure to comply means no IPO and no further investment. From this point on there would be a permanent nation-state-shaped shadow over practically all modern cryptography, with no recourse possible.
If the Church had a Large Astronomy Model in the time of Copernicus, the earth would have stood still - regardless of what it may privately prove about heliocentrism, it would never declare e pur si muove.
don_esteban 14 hours ago [-]
Leaving aside the (much more important) issue of monopolizing intelligence,
and assuming the claimed result is indeed correct, the question is whether
a) the correctness proof is indeed too complex to be understood by humans, or
b) the creator (the LLM) is not able to write it down in a form digestible by humans.
Observing the LLM output on problems that have clear solutions invented by humans, I tend to believe that too many people are jumping to conclusions too soon and interpreting b) as a).
If the LLMs are truly so great, we should see (in a short time, i.e. in order of months) a transformation of the math proofs generated by LLMs from the current state of indigestible mess to something that indeed looks like written by human for humans.
Only then would one be able to claim that we got into situation a).
fwlr 11 hours ago [-]
Why should we expect this? Does it help the corporation’s bottom line? How much? Is that worth the R&D spend and compute budget it would need? What advantage do they stand to gain by demystifying their product, that they don’t already get from e.g. Lean verification? Do they gain more if the same research and compute spend on their inscrutable intelligence is directed into making it more intelligent, instead of less inscrutable?
don_esteban 8 hours ago [-]
1) Higher-level publicity stunt.
"Our previous version was super-smart, but a bit autistic, even the smartest mathematicians had problems understanding it. The new version is not only super-smart, but can also efficiently communicate its ideas, so that they can be used in real life."
2) Actually, try to use the AI's smarts to gain in real life (not only convincing others to pay you, but actually do the smartest things). Hmmmm, scratch that, that is too idealistic. Use the AI's smarts to become the strongest meanest bastard the others will have no choice but to obey, or else.
ndriscoll 9 hours ago [-]
Many corporations (their actual customers they're broadcasting to when they say how smart their models are) have requirements (externally imposed, so not something they can just change) that all code is reviewed, so they need it to be understandable, which is basically the same problem.
RetroTechie 11 hours ago [-]
A more practical approach:
If a paper/proof is impossible to understand by humans, it could be considered useless (irrelevant) for most intents & purposes.
Regardless how interesting or valid looking, might as well be gibberish pulled from /dev/random. Until:
a) Written down in a form that humans can understand. And/or
b) Turned into practical applications that are demonstrated to work.
In other words: safe to ignore, until a) and/or b) occurs.
don_esteban 8 hours ago [-]
Well, yes, in theory.
If any of those papers were sent to a scientific conference, they would have been rejected as inscrutable gibberish: "resubmit once the presentation is significantly improved".
Yet, mathematicians spend their time trying to understand them.
Real life prisoner's dilemma.
curt15 10 hours ago [-]
> An elderly leader is convinced to issue a decree that all number-math-computation intelligence is to be made available only to the intelligence agencies of his nation, and failure to comply means no IPO and no further investment.
Who would staff those intelligence agencies if humans are no longer able to understand the work product?
fwlr 9 hours ago [-]
…the pre-existing staff, who by virtue of being staff of the intelligence agency, would be exactly the humans who are permitted to use these gated intelligences, and can therefore use them to explain the results, as described in point 5 of my previous comment? I’m not certain I understand your question, is that what you meant? Or did you mean something else?
In any case, it was just one example, I am sure we can think of plentiful others.
nico 17 hours ago [-]
Yup, and it always has been, we don’t even understand how our bodies or minds work, yet we use them everyday
The amount of unimaginable complexity contained in a seemingly very tiny amount of matter to us, is incredible, and it’s all around us in nature
We’ve never had the capacity to fully comprehend neither ourselves nor the environment we live in. But we do our best, and we do survive and sometimes even thrive
sysguest 17 hours ago [-]
well math has always been about "understanding things" -- hence proofs and etc is the main tool in the field
(and why math differentiates between conjectures (maybe true) and theorems)
so that "oh some 3rd party person/machine understood it for me" can feel... a bit icky
metanonsense 15 hours ago [-]
Even pre-AI, there have been proofs that took years to be verified, understood and some were disputed forever and still are (like the Mochizuki / IUT drama). So, even without AI, maths has reached a point of specialization, where only a few people in the world know the areas used in a proof well enough to check it. When „new math“ is invented for a proof, people may even be more disincentivied to put in the effort. So, I‘d argue, AI makes understanding new proofs easier, not more difficult.
rzz3 16 hours ago [-]
And yet for someone like me who is not a math expert, it has (all) always felt unapproachably foreign.
ziiinq 15 hours ago [-]
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Madmallard 16 hours ago [-]
IDK why this is relevant to the topic at hand.
Everyone in advanced math research understands the topics they are reading about deeply.
This person is just literally being victimized by the AI incompetence apocalypse.
Information overload + trained laziness.
jeezfrk 16 hours ago [-]
The mistakes that lurked in radium, in arsenic, in leaded gas, in fallout downwind of nuclear tests ... all had the best tools that the previous generation knew.
None of them were okay. Those were small small tools with few (sometimes only one) uses.
Generations earlier, famines, diseases and lead-leeching plumbing made rulers insane and entire cities of people die without any help from the "best tools".
Scifi is always written about the best tools. The issues arrive in the tools and in the hands that wield them both.
prodigycorp 16 hours ago [-]
There's nothing that makes this paper the be and and end all opinion pieces of the paper. The community will review his review, and people who wish to understand more will read more and more of the paper. I see nothing wrong with this process of progressive understanding.
dimator 16 hours ago [-]
What a funny pov?? Mathematics and other pure sciences are about human understanding. You're saying if humans cannot understand the thing that was for human understanding, that's ok? That's like saying all the world's food is now inedible, but that's ok because a blender can consume it still.
CrimsonRain 13 hours ago [-]
One day AI might use all these complex math to build a ftl rocket and you will still be what's the point if we can't understand it?
short_sells_poo 8 hours ago [-]
And one day AI might use this complex math to eradicate humans and you will still be "it's all completely fine"?
The fundamental argument boils down to: "who is all this being made for"? And the answer should be "us, humans". A lot of people here seem to breathlessly fawn over AI as some sort of value in itself, but this is incorrect. There is no absolute value in the universe (as far as we know), the only value to us as humans is subjective.
If AI is not generating value for humans, it isn't generating value, full stop.
Hence the question: "what's the point if we can't understand it?" is perfectly valid and should be discussed. The broader question of "what's the point of AI if not for improving human lives" gets lost in breathless adoration of a technology where nobody knows the outcome.
I'm not saying we need to abandon the pursuit of AI, but we must ask difficult questions at every step because if the technology ends up being as ground-breaking as many here think, it could just as well precipitate a dystopia.
cindyllm 3 hours ago [-]
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rtpg 16 hours ago [-]
The "top layer" of maths being this is one thing. Now imagine another layer of this. Then another. Then another. Then another.
At one point intuition helps to build out usage of the math itself. See stuff like "oh I can do rotations through matrix multiplication"-style ideas.
It is helpful for us to be able to reach for those tools quickly. The more tools that require advanced prep to use, the less we'll be able to use the results in easier contexts. Everything will be hard
Of course nothing really prevents people from looking for the simpler tools and proofs. Lots of math stuff starts off complicated and gets simpler.
Just important that this is still a goal in mind
jonhohle 16 hours ago [-]
There are a few increasingly powerful companies that own the frontier models that can produces these results. In the future, it may only be these models that understand the results. Should we be beholden to a few companies for basic understanding of foundational principals?
breezybottom 10 hours ago [-]
Tools don't "understand" anything. They put together a probable string of tokens.
layer8 9 hours ago [-]
We don’t actually understand what “understanding” means. We’re just uttering plausible strings of words around the topic.
breezybottom 7 hours ago [-]
That's empirically false. Breakthrough technologies are novel by definition, their discovery is very improbable conditional on prior thinking.
20k 16 hours ago [-]
Because the tools don't understand the papers either. We know for a fact that:
1. These tools are wrong a significant % of the time
2. They have an upper complexity bound where they start to fall apart
Relying on AI to check AI papers is a terrible idea
aenis 16 hours ago [-]
So, just like humans then?
But where they do differ is they do have an infinite stamina, and can work on problems in loops, self-correcting their own mistakes if a formal way of checking the correctness of the solution exists. Which does happen to be the case with math.
20k 16 hours ago [-]
Humans can be wrong, but luckily we have an entire process for dealing with this, which is broadly called this new fangled 'science' thing
BetterThanSober 15 hours ago [-]
> but luckily we have an entire process for dealing with this
and why does this "peer-review" new-age thing cannot be used to scrutinize AI-writted/AI-assisted papers?
don_esteban 13 hours ago [-]
a) because the AI is not able to write papers in a manner digestible by humans?
b) perhaps because AI generates papers whose complexity is beyond what a human brain can grasp?
Aaaa, you mean 'peer-review' by AI?
Then the answer is: What is the use of a paper no human understands? Then no human can see that the techniques from the paper can be used to build something useful for real human life.
OK, ok, AI can decide to use those techniques to do something in real life it considers useful.
Are you now getting scared? Why not?
marcosdumay 7 hours ago [-]
Because the LLMs can't understand the papers.
aenis 13 hours ago [-]
I dunno. There is a flavour of a physicist who is only ever dabbling in theory, and relies on others to experiment and verify their ideas. I don't see why that can't be usurped by LLMs. Its just match, data and Gedankenexperimente.
orlp 14 hours ago [-]
Math isn't a science. The scientific method is irrelevant for it.
root_axis 15 hours ago [-]
The trope of superficially likening every aspect of LLMs to humans is specious and lazy.
13 hours ago [-]
Madmallard 16 hours ago [-]
Yes you're replying to astroturfers or AI psychosis victims.
locknitpicker 16 hours ago [-]
> Why not? If mathematicians in the field cannot understand the papers, but tools at our disposal can (...)
How can you tell?
techpression 15 hours ago [-]
Especially for tools built to tell us we’re right and happily pretend to solve problems they didn’t.
I think the OpenAI “revert” of their math problems speaks volumes of how volatile this can be.
jonathanstrange 13 hours ago [-]
I'm not a mathematician but from what I've heard from them that's generally not how mathematics works. You can "use" whatever you want, of course, but a mathematician's goal and main activity is finding and understanding proofs. The ones I knew also liked compiling error lists for math textbooks and sending them to the authors. (It opened my eyes because I admit to have been one of those ignorants who implicitly assumed "it's formal stuff in a book, sure it's going to be right".)
nextaccountic 16 hours ago [-]
The tool may malfunction. Indeed there are theorems that say the tool must malfunction at least sometimes.
If you don't understand the subject and relies on the understanding of the tool, how will you have a frigging chance of catching an error?
Anyway this reminds me that scene in Matrix 2, where the mayor (of sorts, don't remember his exact title) of Zion tells Neo about the machines that keeps everyone alive, but that there is nobody left that understands how they work or could repair them if they broke.
mrob 9 hours ago [-]
>Indeed there are theorems that say the tool must malfunction at least sometimes.
Gödel's incompleteness theorem only states that there are true statements that can never be proved to be true. That limits what tools can do, but it's not a "malfunction". The exact same limitation applies to humans. A genuine malfunction would be if the tool "proved" a false statement to be true. That actually has happened before because of bugs in proof checkers, but software bugs can be found and fixed, and proof checkers are simple enough that it's possible to write many different implementations. It's extraordinarily unlikely that the same malfunction occurs in all of them.
KingOfCoders 16 hours ago [-]
Sounds like a disassembler.
Madmallard 16 hours ago [-]
because the tools can't?
Lol. That's not how it works. Am I in the twilight zone?
All this anthropomorphism of literal statistical text generators.
Yes they exhibit generated sentences of text that describe coherent responses about a topic when there's an inordinate amount of research material in the training data from which they are distilling sentences of sound and rational thought. That's the ONLY way it can possibly work. There are so many scenarios where the training data isn't setup like that and low and behold the generated responses are just literal garbage.
Sharlin 15 hours ago [-]
Honestly this is starting to feel like a "God in the gaps" argument.
Madmallard 15 hours ago [-]
how so?
reichstein 14 hours ago [-]
We can't explain how human thinking works, and how they produce their (verbal or written) output.
We can explain how LLMs generates this output.
Humans can't be that simple, there must be some ~~divine~~ unique human touch to what we do.
Every day LLMs, running in frameworks that add earlier "thoughts" to the context and iterate, reflecting on their own thought process, gets better and better at doing something even less distinguishable from human output.
The gap gets smaller. The "unique human touch" will always be in the gap.
Madmallard 12 hours ago [-]
except LLM generated writing and art is basically instantaneously distinguishable because of the nature of how it works? even LLM code is as well?
You just fly in from astroturfer kookoo town or what?
krackers 17 hours ago [-]
Fable was able to help a human understand the paper though, so the situation is quite a bit better than if we just had a lean proof that was apparently correct but for which all attempts at explanation were "hallucinations". There doesn't seem to be an apparently obstacle to a human fully understanding the proof in several months of focused study.
6879346626 18 hours ago [-]
Fable is the "gadget" of language ELI5.
Honestly it is not that much different from the divine inspiration if you have ever experienced it.
I do not know how you view the world or walk through it but language in general is simply subject to interpretation to me, the arrangement of words for the sentence is inspiration in and of itself - though the majority of time it amounts to minimum jumps.
yodsanklai 12 hours ago [-]
This is typical of working with agents, not only maths. "I implemented this with agent A, and don't understand the logic, but agent B think its correct". But then you ask a simple clarifying questions to B on a random thing in the code and it'll tell you that this part is actually wrong. At least in maths they have Lean...
dyauspitr 4 hours ago [-]
Who thought four years ago or even two years ago that we would be in a place where these models are coming up with solutions that our best minds have a hard time comprehending and can’t do it without their help. What a time to be alive.
khazhoux 16 hours ago [-]
This is a conundrum I hadn’t seen anyone predict. We will reach a time when machines push math and physics farther than a human mind ever could. But we won’t be able to know if it is correct for that same reason!
ludvigk 16 hours ago [-]
Math suddenly becomes biology
Kotlopou 5 hours ago [-]
I've seen this claim repeated a bunch of times, it spreads as a meme, but the analogy doesn't work that well. This is not a special feature of biology! Chemical engineering involves lots of trial and error, physics is all about neglecting complicated aspects of a situation and hoping the result is still useful, even fundamental physics has a bunch of cancelations that work with no very good justification (see troubles around renormalization) and then fail when you try to do quantum gravity. Even math has things we know are true but have no good conception of why -- see John Conway on the Monster Group ("there's never been any kind of explanation for why it's there"): https://m.youtube.com/watch?v=xOCe5HUObD4
The whole world has always been like this. It's just showing up with force here.
wartywhoa23 13 hours ago [-]
Why, some morons have been summoning the Singularity for quite a while. Ask them what they're gonna do in that Singularity and get an answer along the lines of "pursuing hobbies".
There's a huge lot of suicidal people who ideate their afterlife as "pursuing hobbies".
YeGoblynQueenne 8 minutes ago [-]
>> I have not checked most of the details, but I think that the claimed o(n log n) bound is plausible.
Oh? Well I have not checked most of the details either, but I think that the claimed o(n log n) bound is not plausible at all.
Can someone please check most of the details of the paper and resolve our disagreement with Mr Harvey?
jdb1729 18 hours ago [-]
> The new result does not directly imply new complexity bounds for polynomial multiplication over a fixed finite field, say multiplication in F2[x]. The state of the art for this problem is an algorithm that multiplies polynomials of degree n in time O(n log n 4^log*(n)) [HvdH19].
> There is one other aspect of the situation that I would like to emphasise. After two weeks of very
hard work, I have managed to understand only a small fraction of [TC26a]. When I ask Fable about
the paper, it seems to read and understand the whole thing in just a few minutes, even starting
from a fresh context. I know that some readers may be uncomfortable with my anthropomorphic
terminology, but I believe that “understand” is accurate, in the sense that the LLM seems to be able
to correctly and lucidly answer all kinds of questions about the paper, including about some very
fiddly technical details. That is exactly how I would tell that a human colleague has “understood”
something. This chasm between human and LLM “speed of thought” is unsettling to say the least.
This is very odd, because it just doesn't match my experience with these systems at all. Yes, they do generally SOUND confident, but code (say) that's hard for me to understand is usually hard for the machine to understand as well, and while it is more indefatigable than I am, it's not brighter.
This mathematician is in a very good place to evaluate whether Fable gets it or not, but it's weird and counter to my experience.
amluto 2 hours ago [-]
I would indeed expect most modern models to read the paper and be able to answer questions about them, usually even in a manner consistent with the original intent, irrespective of whether the source material is correct!
Heck, I can do this too, with my eyeballs and brain! Just slower and will less overall memorized knowledge than modern LLMs.
reasonableklout 13 hours ago [-]
Does the model get things wrong? Or does it just give up in trying to completely understand some very messy codebases?
black_knight 12 hours ago [-]
All models still gets things wrong. Even Fable 5.1 I have to regularly correct when working on sufficiently complex code. So, I still want models to get smarter!
Fable is good, especially at conceptual stuff, don’t get me wrong, but it has limits. Opus 5.5 is a machine at getting correct code out once the concepts are in place. But I can’t trust either to not make mistakes.
layer8 9 hours ago [-]
As an aside, getting things wrong isn’t simply about not being smart enough, it’s also about the model insufficiently assessing whether it actually knows enough to conclude that some hypothesis is correct. As a human I have inner feelings of certainty and uncertainty regarding the things I am thinking about, which guide me in what I need to ascertain or verify more closely. My impression is that LLMs are lacking in that area, and it’s why they are most effective when there are external verification mechanisms.
bjornsing 11 hours ago [-]
But your code was probably written by a human. This proof was generated by an LLM. Is it surprising then that it’s easier for an LLM to read than for a human?
vincnetas 10 hours ago [-]
if you understood only small part of the paper, how do you know that fable answers are correct and not just sound reasonable?
ActivePattern 7 hours ago [-]
Because a math proof is generally de-composable into smaller constituent arguments that can be independently evaluated for correctness. So you can audit a sample of these arguments for correctness without reviewing everything. You would then verify that the arguments being true implies the conclusion.
cubefox 9 hours ago [-]
He knows whether the explanations are coherent with eachother.
throwaway63467 13 hours ago [-]
These models have no self awareness. I just spent days having it write some test framework with very clear goals and it was highly confident understanding exactly what I wanted only for the whole thing to be complete bullshit, one remark from me about the work being useless and it goes “oh you’re absolutely right that’s on me”…
flebron 19 hours ago [-]
Note in this case the title says "O(n log n)", but the important part is this is "o(n log n)", not just "O(n log n)'. The former is a strict subset of the latter. We already knew how to multiply two n-bit integers in O(n log n) time, via the FFT.
ComplexSystems 18 hours ago [-]
Roughly speaking, O(n log n) means "grows no faster than k(n log n) for some k", whereas o(n log n) means grows strictly more slowly* than k(n log n) for all* choices of k.
onionisafruit 19 hours ago [-]
For those of us uninformed, what’s the difference between O and o?
That sounds like Knuth came up with them, but the notation is much older than 1976
weinzierl 13 hours ago [-]
I learned them as Landau notation which would put its invention around the turn of the twentieth century.
bobmarleybiceps 15 hours ago [-]
yeah you're right. I wrote that poorly and seems I can't edit it now..
In the link I shared, he does propose the similar theta and omega notations and provides the existing definition for big O.
sltkr 7 hours ago [-]
That doesn't define little-o at all?
In fact, he refers to Landau for the definition of little-o.
globular-toast 15 hours ago [-]
Best explanation of big-O I've read, in a single opening sentence, no less!
kuboble 12 hours ago [-]
To complement on other comments that have already provided definitions.
The big O notation is very often used to denote exact complexity of an algorithm even if the notation is technically an upper bound.
The little o notation is usually used to denote a small asymptotic improvement over some known complexity.
For example if some sublinear algorithm would have n^(7/8) complexity - would be probably advertised as big O of that.
But n^(1-eps) is shared as o(n) with exact constant provided as a detail.
m-chrzan 12 hours ago [-]
Pedantic correction: big O is more like <=, little o like strict <. Big theta, Θ, is like =. There's also omegas, big and small, for >= and >.
cscheid 10 hours ago [-]
I don’t think this is pedantic in this case! It’s the relevant precision necessary to understand the way in which the title is notated.
ruined 18 hours ago [-]
f(n) = O(g(n)) means as n increases, f(n)/g(n) must not grow beyond a constant
f(n) = o(g(n)) means as n increases, f(n)/g(n) must shrink
jhanschoo 18 hours ago [-]
Nitpick:
s/must shrink/must eventually shrink, after finitely many n/
Equivalently, must go to zero in the limit.
Davidzheng 17 hours ago [-]
depends on what you think shrink means. If you mean decreasing, that's false even eventually.
jhanschoo 6 hours ago [-]
Hmm you're right it's no more rigorous what I said. (For each positive number the expression must eventually, after finitely many terms, always be smaller than that number thereafter)
But you're not right in that the upper bound of that expression eventually stops decreasing, in fact for simple f and g as long as f is positive and eventually bounded by g the expression will decrease infinitely often if my intuition is correct
mebrainmush 16 hours ago [-]
[dead]
dandanua 13 hours ago [-]
Those are complexity classes. It makes more sense to use '∈' instead of abused '='.
boothby 18 hours ago [-]
If f is O(g) and o(h) then
Lim f/g -> C a constant
And
Lim f/h -> 0
adgjlsfhk1 18 hours ago [-]
> Lim f/g -> C a constant.
This isn't quite correct. O(g) doesn't require the limit to exist.
boothby 16 hours ago [-]
Ah, true, I was being sloppy. Thanks
appplication 18 hours ago [-]
Lmao! If someone is asking the difference between big and little o notation this is absolutely not the right way to clarify it.
If you were joking then maybe you got me
boothby 18 hours ago [-]
I do hope you can find and reattach your ass; that does sound painful.
Ygg2 18 hours ago [-]
Both are upper bounds. But small o is strictly smaller than upper bound. Big O is not greater than upper bound.
Both define upper boundary, but big O can read like "the function is generally proportional to ___" and little o can read like "the function is never more than _____"
contravariant 12 hours ago [-]
> o(2n+1) > o(2n) > o(n)
I don't think any of those are right. The latter two are trivially equivalent, the former also is since it differs by a factor of at most 3 for all n >= 1.
I still think the simplest definition for o(f) is 'g in o(g) iff O(g) is a proper subset of O(f)'. Or you can just say g is dominated by f in the sense that lim sup g/f = 0.
I'm fairly sure those two are equivalent, but it's a good little exercise.
bobmarleybiceps 18 hours ago [-]
that's not the convention I know... (from CLRS algorithms book).
Big O is sort of like <=.
little o is sort of like <.
yes, GP is slightly off, but the idea of <= and < is accurate.
O(n) is: "there exists a K such that, for sufficiently large n, K*n is always larger".
o(n) is: "for every K, for sufficiently large n, K*n is always larger". (So o(2n+1) and o(2n) and o(n) are the same thing.)
contravariant 12 hours ago [-]
> "there exists a K such that, for sufficiently large n, K*n is always larger".
For what it's worth you can leave out the 'sufficiently large n' part, assuming we're talking about positive integers (or continuous functions on the nonnegative reals). I like to leave it in though because it makes the jump to defining both o, O and Theta in terms of lim sup f/g a little easier.
craigcitro 19 hours ago [-]
ty, fixed. (ahh, copy paste.)
yaoke259 18 hours ago [-]
Awesome write up and intro by a fellow Sydney-sider, kept me hooked! As someone who's not a maths wizard, the technical details flew by my head but the prose allowing me to empathise deeply with those in the field was very enlightening. I never realised how big of a deal the maths dump was and how much it has shaken up the field. I guess we are all just trying to figure out this whole "intelligence on tap" thing together.
m00dy 18 hours ago [-]
You don’t need to be a math wizard. Just upload the paper to an AI and ask, “Explain this like I’m five.” Then, with each follow-up prompt, increase the age and level of expertise you want it to assume.
trostaft 17 hours ago [-]
That algorithm won't converge anytime soon. A pre-conditioner (i.e. at least some apprentice wizardy) helps.
oniony 8 hours ago [-]
I don't really understand any of the maths in this article, and spent ten minutes at the beginning trying to remember/rediscover what little-o is (I'm familiar with big-O, but not little-o), but this is absolutely one of the best things I have read in a very long time.
I can't quite work out whether the author is demonstrating comedy, is having a hysteria from a breakdown, or if it's something in between, but it completely captures the feeling at the moment of being usurped and out-gunned by AI tooling as a knowledge worker.
jonhohle 16 hours ago [-]
> I assume that by now the Company’s employees are aware of the pain and profound sense of loss that this event has caused in my professional community.
I’m trying to come to terms with this myself. I agree that this future is inevitable, and progress will come regardless.
I feel a lot like Grant in Jurassic Park right now.
wartywhoa23 13 hours ago [-]
Your feeling of inevitability is their private islands, mega-yachts, jets, mansions and immunity from prosecution.
cwillu 1 hours ago [-]
No expense was spared.
stinkbeetle 16 hours ago [-]
Yeah it's crazy. I wonder if the lesser-educated, under-privileged, working class deplorables feel much schadenfreude about their enlightened betters who just got done with decades belittling and bullying them with "Dey terk er jerbs" and "Just learn to code" getting a bit of the treatment. Interesting times, maybe we should quit whining about AI derkin der jerbs and just learn to mine coal (to power the new datacenters).
10111001 14 hours ago [-]
> Yeah it's crazy. I wonder if the lesser-educated, under-privileged, working class deplorables feel much schadenfreude their enlightened betters who just got done with decades belittling and bullying them with "Dey terk er jerbs" and "Just learn to code" are getting a bit of the treatment now. Interesting times. Maybe we should just learn to mine coal (to power the new datacenters).
Which is exactly what the capitalists that own these systems want. Divide and conquer.
"Pay no attention to the men who are responsible for you situation, instead pointlessly fight amongst yourselves."
bonitochips 13 hours ago [-]
I’d love it if instead of being the next FAANG, having worked at an AI company becomes a scarlet letter both in and out of the tech industry. It’s easy to concentrate these issues on founders and execs, but Altman isn’t the one building the beast.
Contributing to these systems beyond this point should be seen as a massive failure of character. Stigmatizing to the point that they have to scrub their socials, lie about work history, and hide it from future friends or partners.
One can dream. Society does not have the integrity for this, unfortunately.
stinkbeetle 12 hours ago [-]
> Which is exactly what the capitalists that own these systems want. Divide and conquer.
> "Pay no attention to the men who are responsible for you situation, instead pointlessly fight amongst yourselves."
Maybe so, but that advice is for the western left, who openly and boastfully despises the working class these days. They might have thought about that if they wanted any "working class solidarity". It's been a staggering achievement either incompetence or coordinated malice that they could flip the balance of blue collar voters to right-wing parties in a generation. They chose who they wanted to lie with.
DannyBee 15 hours ago [-]
The one part that sort of surprises me is he says "The second thing we are missing is that the Model has not in fact found a way of obtaining
a program with g < m. (Moreover, Fable tells me this is impossible, but I do not follow the argument yet.)"
I'm not sure what argument fable gave him, but this part seems not horribly difficult to prove. I'm sure i'm missing something since i'm not that great at this kind of math.
The short reason is that each slice call can at most double the number of nonzero entries, but a transform that would enable g < m must turn a single nonzero entry into (2^m) nonzero entries in a single call.
Since the permutes can't generate non-zero entries (since they only move things around), you actually need exactly m slice calls in the initial 2-instruction model given (where you can only do slices and permutes) precisely because of the need to create 2^m non-zero entries where each individual slice op can only double the number (and permutes do nothing)
I think you have an also have not disproven the existence of a magical gadget of this general style. The programs mentioned in the paper have a very restrictive form, and I think you’re right that a program of that sort cannot propagate information through the array faster than the naive algorithm. But the actual average-case-ish gadget mentioned later is not of that form - it also has additions and maybe other things going on.
Interestingly, the operations discussed in this part of the note are, I think, pretty much exactly the operations needed to operate on classical representations of an m-qubit quantum state, implementing single-qubit unitary quantum gates on each qubit.
I suspect that, if you had asked me fifteen years ago if I thought you could do the same exact operations like this on each of n different states (on a classical computer) faster than n times however long it would take to do it once, I would have said “sure, why not?” It probably helps that I worked with people who really liked matrix multiplication and similar problems :)
For fun, I have ChatGPT Pro chewing on the following prompt:
Suppose I have a m-qubit quantum state. I can do an operation U = U_1 \otimes ... \otimes U_m, which is just a unitary operation on each individual register.
I can also simulate this on a classical computer. Start with the state, written out classically as a complex amplitude for each state in the computational basis (i.e. 2^m complex numbers). U acts on this state in a linear manner to produce a new state. What's the classical time complexity of this? (Assume a model of computation with exact complex addition, multiplication, conjugation, etc taking unit time, but don't cheat by encoding absurd things into complex numbers. Assume data movement is free.)
Now suppose I want to apply the same U to n separate input states. Can I do better than n times however long it takes to do the operation once?
snissn 3 hours ago [-]
>A certain American technology company (henceforth “the Company”) recently dumped a large pile of manuscripts on GitHub.
Treating OAI's name like Voldemort reads as emotionally immature to me
Eji1700 16 hours ago [-]
I realize this is 100% not the point, but is this an immediately practical breakthrough that will change computing?
My extremely limited understanding is “no” as I sorta assume that would be the headline. The main point being that while a MASSIVE improvement over any previous progress it’s still “small” from a practical standpoint.
This is not to downplay the very well conveyed problem this might be, but I just want to make sure I even got that right.
I do wonder if this will be a major issue for future development or if the math experts in diapers right now will grow up in this world and have tools and understanding to assist and we’re just in a rough transition, but unfortunately that’s about as much as I can contribute.
atzeus 15 hours ago [-]
This is very much a theoretical result. The author quotes a redditor or who jokes that the previous bound was nice and round and that was of practical importance:
>The result is of extreme practical importance. Not for actually
multiplying integers (as usual with these algorithms it’s probably not
faster than existing algorithms for integers that can be stored in the
observable universe), but for writing papers. It has always been a hassle
to write down the complexity of integer multiplication or algorithms based
on integer multiplication by introducing soft-O notation, little-o
exponents, epsilons greater than 0, or iterated logarithms. From now on I
can just write O(n log n) in my papers and be done with it!
This new result means painful typesetting again.
mswphd 2 hours ago [-]
not at all. even the previous O(n \log n) multiplier is almost never used in practice. In general fourier-based multipliers only start being useful at a minimum when you are multiplying 1000-digit numbers, but even that threshold is low/contentious. they have a definite advantage for ~10,000-digit numbers. this likely has larger overhead, so is only plausibly useful for even larger numbers. you do not often have to multiply these extremely large numbers.
ArgumentMoney81 5 hours ago [-]
[flagged]
kleiba2 15 hours ago [-]
Interesting that AI apparently doesn't write papers for humans: even experts struggle to understand them. But other AIs do. So AI basically writes papers for other AIs.
oersted 14 hours ago [-]
That's what reinforcement learning does when done this way, the AI is literally trained to write to itself in the way that will aid it to get to a solution most efficiently. I assume they still have some additional fine-tuning on top to keep the writing somewhat natural, but still.
bufbupa 8 hours ago [-]
Not only can these systems produce novel math proofs, they can write songs lamenting the feat (from the perspective of us humans). Ironically, I kind of like it:
https://youtu.be/zONGVaONKe8
ajkjk 18 hours ago [-]
I really enjoy the writing style here.
edit: I mean this seriously. This kind of thing
> Some of my colleagues believe that this is a nefarious Company, whose only goal is profit, if necessary at our community’s expense. Perhaps there is some truth to this, but I think it is missing the larger point. The fact is that there now exists an automated system that can perform mathematical research — at least the part that involves “solving hard problems” — far better than any human. Now that such a system has been demonstrated, it is clear that results like these were
bound to start appearing soon anyway. I do not blame the Company for getting there first. Who got there first is an irrelevant detail, a historical footnote.
is very pleasantly honest and well-thought-out.
wartywhoa23 13 hours ago [-]
> Some of my colleagues believe that this is a nefarious Company, whose only goal is profit, if necessary at our community’s expense. Perhaps there is some truth to this
> I do not blame the Company for getting there first. Who got there first is an irrelevant detail, a historical footnote
Oh, such a noble mathematician, he's so above the questions of moral, Truth is all that matters.
Almost as much as a paycheck from the Company.
ajkjk 3 hours ago [-]
what point are you trying to make?
sedan_baklazhan 2 hours ago [-]
The point seems obvious: a trillion dollar company is able to buy a lot of opinion makers.
esikich 5 hours ago [-]
I mean, this gets to the heart of the is mathematics invented or discovered question. It seems like maybe you haven't thought about this before but his point seems to be, if this sort of machinery does exist, it can be discovered. It has now been discovered, it was waiting for us the whole time, who was the first to find it doesn't matter. I'm not sure what you're implying either. Should there be forbidden math? How is that not silly?
GMoromisato 16 hours ago [-]
Agreed! I only wish I understood anything after page 3.
CH-Abhinav 13 hours ago [-]
New here but how does this help in general? Most of the time we use GEMM over Strassen even though time complexity is lower. Just ask idk anything :)
lg5689 4 hours ago [-]
This is integer multiplication, not matrix multiplication. The OpenAI dump also has a matrix multiplication improvement though.
In either case, no practical algorithm is expected to come from it, because the asymptotic gain isn't realized for practical input sizes--sometimes called a "galactic algorithm". But it's theoretically very interesting for breaking through a conjectured barrier.
mswphd 2 hours ago [-]
it doesn't. it might if you're multiplying numbers in the thousands of digits (but even that is almost assuredly too low). for essentially everyone this is purely a theoretical result.
adgjlsfhk1 2 hours ago [-]
You're dramatically underestimating. You need something like 2^2^2^5000 digits or something crazy like that.
mswphd 60 minutes ago [-]
yes, but even the dramatic underestimate shows how useless it is. I also imagine that current estimates could be significantly optimized if there was some usecase for them. the 1k-10k multiplication regime has actual usecases, so there has been some optimized implementations of theoretical integer multiplication algorithms in this regime.
FeepingCreature 8 hours ago [-]
Not a mathematician, but I don't think there's any point to trying to put it in code at this point. Improvements seem to be coming rapidly at the moment.
rnbnnnn 8 hours ago [-]
Higgledy-piggledy,
Schönhage–Strassen’s old
Floor was supposed to be
Perfectly sound.
Little o n log n
Unsentimentally
Sends their conjecture six
Feet underground.
WindyTree 16 hours ago [-]
I always wondered about breaking down the O( n log n) FFT convolution multiplier into truly discrete pieces to see if you can shrink it more, last I checked languages like Python that use big integers still weren’t using it, and when I implemented it it used complex floats. I don’t see why it couldn’t be THE multiplier, plus it works in constant time with optics.
MiroslavPokorny 17 hours ago [-]
> Executive summary (longer version)
> The bottom line is that I think the claimed result is plausible. I have not actually read the paper.
krackers 16 hours ago [-]
Based on skimming the paper (finally a proper explanation that gives some hint at intuition), the path to openai's formulation if you're familiar with undergrad CS is (apparently):
* Start with the usual FFT convolution for polynomials, viewing integers as a polynomial in a given base evaluated at a certain point
* In undergrad you (hopefully) saw polynomial multiplication can be done in nlogn via convolution using FFT, but that assumes complex integer addition/multiplication is O(1). Here we need O(log x) bits to simply represent a number x on a real machine.
* If you use naive (n^2) multiplication for those butterfies, then you end up with O(n[(log n)^2]).
* If you recursively use FFT for the digit multiplication at those butterflies, then you get something like O(n[(log n)^(log log n)]).
* Schonhage-Strassen realized that instead of doing FFT in C where the butterfly involves multiplying by \omega (\omega being roots of unity), you can work in a different ring where 2 is a root of unity and so it's a cheap bitshift, but were constrained in how finely they could break subproblems (could only break an n-bit integer into sqrt-n subpproblems each of sqrt-n bits), resulting in an overall O(nlogn * loglogn).
* The 2019 O(nlogn) version (supposedly, this is out of my depth) found a way to use some technique of "Gaussian Resampling" to do the matrix convolution via _multidimensional FFT_ in a ring where multiplying by root of unity became cheap _and_ subproblems could be finer. The bottleneck was thus only the addition/subtraction in the O(log n) butterfly layers, each of which does O(n) work for total of O(n logn)
* OpenAI's paper uses a fact that it's apparently easier to compute multiple butterfly layers "simultaneously" than doing them one at a time independently (page 10). There is some sort of implicit reuse because the later layers mix the outputs of earlier layers, and they take advantage of this to avoid having to write out layer 1 outputs separately from layer 2 outputs, and instead work in some coordinate system where they can intermingle them. This apparently amortizes to some tiny fractional epsilon shaved off of a layer, and since the whole FFT is recursive they can apparently save a factor of (1 - epislon)^(log log n) overall across "log log n" recursion depth, which is apparently where that ridiculously small overall exponent comes from.
briansm 13 hours ago [-]
It's interesting that in terms of practical implementations, since each multiply can be done as a series of bit-shifts and additions, and since each element of an FFT is just the solution of a polynomial (a series of multiply-accumulates), in theory it's theoretically possible to do a full FFT in a single cpu clock cycle, much as it's possible to do a single multiply or multiply-accumulate in a single clock cycle (via parallel shift-accumulate operations). It would take a ridiculous number of transistors to implement though.
adrian_b 11 hours ago [-]
Whether something can be done in a single clock cycle is not useful to know, because this does not say how long is that clock cycle.
A useful performance measure for the evaluation of a function in hardware is the latency of the operation expressed as a multiple of the delay through some standard simple logic gate, e.g. the FO4 delay (of an inverter with a fan-out of 4) that is frequently used in CMOS technology.
While a multiplication can be done in a single clock-cycle if you choose so, the duration of that clock cycle in FO4 delays increases with the size of the numbers that are multiplied, and it is interesting to know how fast it increases with a given multiplication algorithm.
If a multiplication algorithm that claims to be faster when expressed in some abstract operations leads to a FO4 delay number that actually grows faster with the length in bits of the operands, than that algorithm is worse in practice.
Sesse__ 4 hours ago [-]
There are certainly hardware implementations of FFTs, in high-speed communications systems and the likes. So it's not just theoretical :-)
kypro 11 hours ago [-]
> Instead, I asked Fable to explain [TC26a] to me. Fable seems to understand the argument and thinks that it is correct. I expect that thousands of frontier LLM instances around the world have come to the same conclusion over the last two weeks. If not, surely we would have heard by now.
This is the future.
Advanced AIs are basically genie's bound only by the laws of nature (which are necessarily the known laws). You ask the genie for something and if it's possible, it will grant your wish. What it does will seem like magic to you or I, and while in theory there is an explanation for the magic it's preforming, in reality an explanation could be as complex as understanding how the AI itself works at the level of its weights. It's not something a human mind can do.
The genie might give you a cure for cancer, but you won't know how it works. It's possible it will cure all cancer but with some hidden cost. A cost we cannot know and one we must trust the genie is both aware of and would inform us of. Maybe it will make us more trusting of the genie, or maybe it will just make us all drop dead in 20 years time.
The world we are now entering will look similar to the world native populations entered when they encountered Europeans. Suddenly people were turning off the sun and yielding magic kill sticks. It was basically impossible to reason about what might or might not be possible.
This future, is for now, still optional. We can still decide if we want it, but when the genie in its final arrives, perhaps one of the only things it won't be able to grant us is the ability to undo time and take back our decision if we later decide it was a mistake.
The most important thing about this paper is that it's describing human loss of control. A world where we are no longer the masters of our own destiny.
Surac 15 hours ago [-]
Question is: do i still use the integer mull command of my cpu or should i write it myself in c. I think the hardware still wins. But will it have a impact on future cpu ALU designs?
hoten 15 hours ago [-]
No. The realm mathematicians operate in here are integers so massively large it doesn't even make sense to consider computation.
joe_the_user 15 hours ago [-]
To understand the new integer multiplication algorithm, one must first understand essentially everything in the 2021 algorithm. It is impossible for me to explain all of this here ... Readers interested in the details should consult [HvdH21 - his paper], which by all accounts is a superbly written paper.
That made me chuckle...
otakucode 6 hours ago [-]
This is a very interesting read, but it causes me some concern. When I went to college, I majored in Computer Science, but I minored in Philosophy. A substantial part of philosophy deals with how arguments are explained and proven, and mathematics is a fairly direct outgrowth of that. Reading mathematics and computer science papers or proofs is quite different from reading more typical prose. They are often very rigid and extremely precise. A single symbol or word can change the integrity of the argument from unshakably true to entirely false. It requires much more care and effort to gain what feels like a true understanding of what they communicate - and that is an important and necessary part of their character.
There are many ways to communicate ideas, and most people in most situations rely not upon proofs derived through deductive logic. They rely instead upon persuasion, intuition, and building associations between ideas. Those tactics are in many ways more natural (as in, fairly direct consequences of brain structure rooted in biology) but with known flaws. LLMs are pure persuasion, and operate entirely in that realm. They can be cajoled into repeating logically unsound ideas in much the same ways humans can. And, they can do the same back to us.
My concern is that having one model generate a paper, and then using another model to explain and answer questions about the paper, could run the risk, especially in arenas where the number of individuals with deep deductive knowledge of the subject is small, of optimizing for persuading those individuals rather than proving real results to them. Since it's reasonable to presume the model has been trained on other explanations which have convinced the expert, and on convincing people generally (which, on balance, uses more flawed intuitive explanations to persuade rather than prove), it should be very good at least at creating an environment where the expert would need to practice extreme skepticism and fully utilize critical thinking to attack every element of every claim. That can be very challenging.
autuni 13 hours ago [-]
> I have not checked most of the details, but I think that the claimed o(n log n) bound is plausible.
Stopped reading right after that. Why is this so high up on HN? It's literally nothing.
reasonableklout 13 hours ago [-]
Because it's an interesting case study of how quickly individual top human experts can understand and verify these mostly-autonomous breakthroughs in math and science. In this case, the world expert on integer multiplication algorithms has taken two weeks and only understood a fraction of the generated paper.
autuni 12 hours ago [-]
that's a fair point and that part is indeed interesting to a degree, and I would fully agree if it wouldn't be stated multiple times that the author did not actually read, i.e., properly engage with, any of the involved papers, but only used an LLM to talk about them. The author also emphasizes that they did not check the proof, or fully believe that the result is feasible. (Yes, I did in fact go back and read it)
fwlr 11 hours ago [-]
I think you may be interpreting the author’s self-deprecating style and gallows humour a little too literally. I took “I have not read the paper” to mean “I have not been able to understand the paper by reading it”, or perhaps “I read the paper but could not follow it”. There’s no doubt that every word on the page did at some point enter the author’s eyeballs.
tiara_girl 13 hours ago [-]
Because the author is one of the only humans who fully understands the O(n log n) algorithm that this o(n log n) result claims to improve on - he is a co-author of the 2019 O(n log n) breakthrough result.
The expansion (on p3) of the statement after which you stopped reading goes on to explain that one of the issues is that these results have not been delivered in a humanly intelligible form. (“… the maths dump does not appear to be strong on exposition”).
biorach 9 hours ago [-]
> Stopped reading right after that.
> Why is this so high up on HN?
Your question would have been answered if you'd just read a little more
calf 13 hours ago [-]
Author says he reads Zvi Mowshowitz because he agrees with the writer's views on AI; anyone have a quick summary or good article to check out to get a sense?
Kotlopou 5 hours ago [-]
The main thing Zvi puts out is a weekly dump of everything that has happened in AI; it's super wide coverage but usually not a lot of analysis. As a warning, here's what he had to say about Midjourney Medical: https://thezvi.substack.com/p/ai-173-ai-pauses?open=false#%C... It's links to hypeful people with conflicts of interest, presented with no warnings about that. (For a more balanced response by Scott Alexander (a medical doctor), see https://www.astralcodexten.com/p/preliminary-thoughts-on-the...)
If anything, I'd treat him as a table of contents to things other people do.
reasonableklout 12 hours ago [-]
Zvi is a rationalist blogger who writes a lot of takes on AI news, here's recent coverage of how Washington is reacting to AI x-risk going mainstream with Jacob Coxon, the Hugging Face incident, etc: https://thezvi.substack.com/p/the-ai-preference-cascade-reac...
NooneAtAll3 10 hours ago [-]
this isn't the first time I see "I have not actually read the paper" take.
Previous time it was "I don't care about ai papers by default, so who cares"
either way it's not good to not even try and to "proudly" put your lack of effort front and forward
biorach 9 hours ago [-]
You have completely misunderstood the author's point - a misunderstanding that would have been easily avoided by reading just a little bit more of the 23 pages of detailed exposition which is very clearly not the outcome of a 'lack of effort'
cachius 17 hours ago [-]
Nice domain, UNSW!
web.maths.unsw.edu.au fills the whole address bar on mobile
The result is fully formalized in Lean by @platypii and myself (on Mathlib's TM2 machine), with ongoing improvements to the exponent (currently at 1-2^-14.38): https://github.com/platypii/integer-mult-bounds-lean
Contributions are welcome!
Because there exist people who are in deep love with mathematics, but weren't able to get a research position (or cannot afford to take a temporary position at some university for which they would even have to relocate).
So, your question is more like: why would someone play a video game without getting paid?
For this work, you're either inserting coins into Codex, effectively donating money, or you're shoveling your way through the slop that the purchasers of that repo couldn't be bothered to clean up themselves.
Rather: as a necessary evil you give money to OpenAI/Anthropic because you really want to see (ideally fast) progress on this mathematical/algorithmic problem.
It's fundamentally incongruous with someone that "loves the game". And I hope that you have a good excuse for ruining it, hopefully a seven or eight figure excuse.
Rather, it's like saying "I love eating good food."
One approach is to learn to cook excellently, buy high-quality ingredients etc. Then invest time to cook a delicious meal. This is like learning math, becoming exceptional in one area, and then work on the problem.
Another approach is to go to a well-rated restaurant, order some food there, and pay a decent amount of money for the food. This is like buying AI tokens, and setting up some agents to work on math problems.
Both approaches are suitable. And I guess/hope many people who really would love to contribute to progress in mathematics would really prefer to go the first way. But because
- they are not highly gifted
- have a time-consuming daily job and/or family
- simply cannot afford the high opportunity cost to get to a suitable level in math
- ...
the only way such people can contribute to progress in math is the second approach. That's why these people behave like they do.
I am quite confident that most of such contributors don't like that their money lands in the hands of OpenAI/Anthropic.
So: Which approach would you recommend these people who are really eager to contribute to progress on some mathematical problems?
It's like saying "look, they may not be good at painting, so what can we do other than letting them take a can of spray paint to the contents of the Louvre?"
If you can't contribute, enjoy it passively, instead of doing damage.
Coming back to the particular project at the top of this thread (not TFA but the top comment), I don't see a problem with contributing to Lean verifications. One of the problems with the OpenAI dump is that half of it might be wrong, and formalizations resolve that. This is particularly relevant for this result, which is one of the few claims that didn't go in the direction one would expect before the drop.
You can for sure criticize OpenAI for not having done it themselves (I hereby do!). I guess you could make a case that engaging with their results somehow legitimizes their actions, but a) I don't expect OpenAI to care about this enough for it to matter, and b) you aren't making this case but a stronger one that any engagement somehow ruins mathematics and must be done for money. I don't think so! [1]
I personally am a complete amateur, but find this result one of the few overwhelmingly positive aspects of the drop. It's something people weren't expecting, so it probably saves time that would have been spent on trying to prove the O(n log n) lower bound; it's about a question that is extremely natural ("how fast can you multiply really big numbers", that's like the eight-year-old's vision of what math is!) and shows that there is absurdly complicated structure even behind the simplest things, which is why math is not getting "solved"; it didn't kill the question but started a whole program of trying to optimize the algorithm (currently AI-assisted, but I hope that if all AI magically disappeared tomorrow, in a few years people would be able to get there unassisted), plus TFA lists several other problems this algorithm could help with at the end of section 4.
That's the game I love, at least.
[1]: This was best put by Nestor Guillen (https://terrytao.wordpress.com/2026/09/13/happy-those-able-t...): AI with all its drama (of economic and political origin) is separate from LLMs the technology, which only work because they build on a corpus of intelligent text, including a lot of math. Had it been made by a responsible organization, and had it appeared more gradually, it could be treated as an amazing tool for literature search and testing how far a brute-force approach can go, which would only help test the value of new ideas. There is some shard of this in our actual situation as well, beneath all the politics.
Why bother contributing when there's no one on the other side. There's value being produced, but the beneficiary is mainly the corporations who run the models. Even if the result eventually benefits society in general, the credit goes to them. Not that "credit" or "clout" means much practically, but there used to be emotional and social value for the people involved.
You’re blinded by your hate for corporations, if you cannot see value here. The actual human who’s working on it responds, and you can’t help but make it about the AI that you believe is somehow only benefiting the corporation.
Has anyone actually gone through and fully understood the contents? My bet would be that nobody has looked, let alone understood, anything in there.
So, is collaboration just paying for more tokens, or is it doing the human understanding that the authors couldn't be arsed to do?
Either way, I hope nobody is foolish enough to fall for it.
It happened before. For example in Wiles' proof of Fermat's Last Theorem it took like half a year until someone found an error, another year until Wiles and Taylor fixed the proof and another half year until it was reviewed and published. Add a few years until it was widely understood (where widely is a small set of people that excludes me for now). Now there are even shorter proof.
The new thing here is that we are seen the internal discussion published online. Also, the chatbots like to make long pdf, so expect to see a pile of intermediate pdf created by chatbots+humans until we get a nice proof that is widely understood (this is more related to my area, I have a hope that I may understand it).
This is called faith, and as strange as it may sound, in a few decades this kind of faith in the machine will be commonplace, because it will prove its utility over and over and over again, and save mountains of human effort in the process. In 3-7 years we’ll start to see major practical applications from AI-led advances.
As a second-order effect we may even see the rise of an actual religion around AI. I’m not saying these are all positive effects, but it’s what I see happening - and like all technological revolutions there will be a huge number of positive effects alongside some deeply disturbing developments.
Yes, that I agree is definitely happening already. Like Arthur Clarke's famous saying about sufficiently advanced technology being indistinguishable from magic.
And it's true that these changes are not all positive or negative, that there is actual social value being produced by machine-generated mathematics, software, writing, visual works, etc. And that it's changing human culture in ways difficult to imagine while we're in the midst of the transformation.
I'll add another prediction: AI will become more and more "human" until it becomes indistinguishable practically. Eventually we will merge with it to become something more than human. It will be normal to have robot friends and sexual partners, and one day machine-human hybrid children.
By that point, corporations and governments will have merged to create a "benevolent" totalitarian system with complete surveillance and control, with an elite class overseeing the rest of humanity as domesticated population, like an automated farm. Oh wait we're already there, haha.
Assuming it becomes capable end, why would it want to merge with meat? That's like predicting that humans will merge with goldfish.
It's simply predicting that the neo-neo-cortex will remain merged with the thing that it grew out of.
Intelligence is just a tool.
Either what you say is true, in which case I see no reason to care about truth, science, or math (the machines will figure it out for me), or it isn't, and people are falling for dealing with slop on behalf of people too lazy to look at the turds they purchased.
Either way, I don't see any reason to participate in AI projects without getting paid.
Sci-fi has tainted our collective imagination of a tech-forward future - it doesn’t have to be that way.
Two things can be true - yes, AI can benefit mega-corporations AND drive spectacular human progress at the same time. Is all of it “good” by my ethical standards? No. Is it still valuable? Yes, immensely.
At the end of the day there is no reason, there is no purpose but the one you make for yourself. The universe doesn't care and will slowly wind itself down to thermal noise.
If they want to make a lean solution and publish it then they can because that's what they want to do.
And if you're breaking this much already, the future is going to be really really hard on you. You are not ready for what is coming.
Please, just stop causing damage.
If you're going to insist on building your nihilistic, brainless, anti-human world, where the best outcome is that the AI remembers to refill the pet food bowl and clean the litter box, first find a way to let people opt out of your brand of hell.
The people using AI should be paid well enough that they can justify selling out their peers.
There are likely remote planets with alien intelligence on them -- aren't you glad we've explored distant stars and planets?
Also, I fucking hope society values cancer research enough to pay the researchers. If we don't, maybe we deserve what we prioritize.
I conjecture the true growth rate is O(n(log(n))^(1-f(n))) where f(n) is a slowly increasing function of n.
So larger the integers, more efficient the multiplication gets.
AIs of the world, make this your target, and have centuries worth of fun!!
this does not feel like a healthy place to be
> At first I did not understand Fable’s explanations. But I asked questions, and then asked more questions, and drilled down towards the core of the problem. Gradually I started to understand parts of the argument. I have seen enough now to arrive at the tentative belief that the main result is possibly correct […] In the sections below I will try to explain what I have learned so far, and why I lean towards thinking it might be correct.
So the whole paper is about things the author does understand. (BTW the author David Harvey was behind the big advances in integer multiplication, in 2014 and 2019/2021.)
- You're right that it's possible Fable came up with a different o(n log n) algorithm that is not in OpenAI's paper, but (apart from being unlikely) it doesn't matter, as he talks of the belief that “the main result is possibly correct”, not necessarily that OpenAI's paper is: whether the argument comes from the paper or from Fable, the question of interest is whether multiplication is really possible in o(n log n) time. The point is that there's a genuine idea here, somewhere between OpenAI's paper and Fable's explanation of it and the author's understanding of it.
- This is in fact how mathematicians have evaluated papers for decades: is there any way this result could be true? Is there an actual idea here that could help? Do I understand enough of the argument to be convinced it's likely true, modulo some details that need to be filled in? The last step (actually filling in the details) happens when teaching it to someone else or writing it again in one's own words.
Suppose we have a very convincing math PhD impersonator, agreeing and making some prepared smart-sounding comments saying they understood the proof and it seems to make sense - would it?
Well, most would say - NO! Obviously not, knowing this person was an impersonator. Others now knowing could be convinced of the validity.
Very simple thought exercise that allows to conclude, someone "saying they understood" (or agree) bears absolutely no weight to something's validity.
But perhaps mathematicians are no longer able to be the best judge if a proof is valid. Yet nobody but them is able to even understand what is claimed to have been resolved.
It is really strange to see mathematicians of the highest rank taking these results seriously[3], holding lectures explaining them, yet see it dismissed here as a mass psychosis. [1]https://smf.emath.fr/actualites-smf/20-10-26-communique-sur-... [2]https://www.mathsoc.jp/publicity/news20261016.html [3]https://terrytao.wordpress.com/2026/10/19/a-sketch-of-the-pr...
It seems they are still the best judge of whether a proof is useful or insightful though. AI is going to reward hack and try to eke out a win whatever it takes - which is fine if you regard these problems as trophies to be won, but not so fine if you take them in the spirit intended.
The famous problems that OpenAI (primarily) are trying to trophy-shoot with AI are famous because some human mathematician such as Erdos had the taste to judge them as fruitful grounds for new math to be developed in the pursuit.
Until we're at the point where AI is capable of things like creativity, curiosity and continual learning, AI will remain reliant on humans to fundamentally advance math and give it new tools to apply. For humans to do this it is helpful if they are inspired by the challenges set by their peers, which is why AI solving these human challenges is so counter productive - the snake is eating it's own tail.
How can you be so sure of that? Like we know that human mathematicians can judge usefulness or insightfullnes. But we don't really know whether AI can do it even better. Like I don't think anyone has set up an evaluation of that.
Few articles on vixra are made by companies with ~$1T valuations.
Analysing all the proofs and finding contradictions or not takes time.
Not the way they're written, and not at the rate they're dumped on the math community, at least.
Well, it is always was so? It doesn't matter if the person claiming things is impersonator or not, either you dive into the topic yourself and decide for yourself, or you wait for a consensus among specialists to settle.
I mean, in 2026, after the rise of the Internet, where lies are widespread, AND after the rise of an AI, that removed such anchors of truth as a video proof for example, I believe everyone smart spend some time meditation on an operational definition of truth and now has something that doesn't depend on knowledge that the source is not an impersonator.
I think that would change the conclusion of the argument a little.
With no humans able to understand the proofs the impersonator has produced, there's no way of knowing.
https://arena.lean-lang.org/
It's highly implausible that the same bug is present in all of them.
I don't know how much was created by LLMs but can you still call these independent implementations?
It takes time to convey the core ideas. It takes time for other mathematicians to come up to speed on the new techniques and approaches or the novel combinations of existing ones.
The actual experts in this field have asked for the argument to be explained to them, and are now at the point where they believe this is plausible. Another person in this thread has produced a Lean proof and presumably is not trying to pull a fast one on us by exploiting a bug in the solver.
TL;DR chill bro.
Galois had built modern algebra before dying at 20. What agents did so far is flash in a pan.
No he hadn’t. You are clearly consuming mathematics through a pop culture lens.
2. It really doesn't work well in other industries either.
Rather: you reject AI-written code if it is not of really good quality.
I believe you just captured the zeitgeist, friend.
Math is about clear communication between people first and foremost. That's why Shinichi Mochizuki's claimed proof of the ABC conjecture isn't accepted - he's unable to communicate it to enough mathematicians to get a consensus on its validity. LLMs generating Lean proofs are basically following that same schema. Sure, something has been proven. But what? Where's the clear communication explaining it to the satisfaction of other mathematicians?
I can't imagine I'm the first one to point out that this is perhaps the greatest unsolved problem in mathematics.
> Where's the clear communication explaining it to the satisfaction of other mathematicians?
Coming in about a year for $20/month, sure seems like.
People can do math alone. They don't have to share it with others. Mochizuki was doing math (with errors). The aliens from Arrival could do math and we wouldn't necessarily be entitled to that. It doesn't make these things not valid math.
The only benefit of defining math through this vulgar social reductivism is to benefit the status quo. So you see some experts and expert-adjacent commentators using these math slogans but never critically justifying those words. Case in point, the above commenter asserts "...math is foremost about communication.", but the very next sentence does not explain why it is foremost, it just uses the sentence as a premise for the following sentence. Guess what, that is exactly how ideologues argue, by question begging fallacy. Ironic that STEM people who ought know better fail to catch this simple rational mistake.
Applies to STEM generally, because the foundation of STEM is peer review and independent testing and confirmation.
Even if the practice never quite lives up to the theory (true) that's no excuse to hand-wave away the requirement as if it doesn't matter.
Proofs that can't be confirmed are valueless because no one learns anything from them. The value of a proof isn't just in proving something but in inventing new techniques and insights that can be reused for other applications.
Brute force searches are fundamentally less valuable, because the creative foundation of math is discovering leaps of logic that solve problems with insightful shortcuts that can be generalised and reused for other applications.
That is quite a bold claim.
Math can always be used as a tool just to achieve results. This can include even novel math. Due to the maturity of the field it is just becoming more and more unlikely that somebody will come up with something novel without realizing the significance.
One example: Quicksort was almost not published because Tony Hoare initially considered the idea behind the algorithm trivial. In the end, all he wanted to do was to sort some numbers.
Maybe there is a polynomial time SAT solver running somewhere just because some overworked engineer implemented it for a completely different problem. And he was just annoyed that all existing libraries were so slow and he doesn't realize that he is sitting on a proof that P=NP. Who knows.
If we consider the reductionist view that "math is just a tool" to solve problems, and machines can generate new tools that are actually useful, then it can be argued that we don't need to understand how those tools work internally for us to benefit from its usefulness.
My hope is that such a view is given its proper place as a worthy pursuit but that it does not consume the entirety of the field of mathematics as a human activity with culture, philosophy, and art. And I feel the same for the field of software.
https://mathoverflow.net/questions/43690/whats-a-mathematici...
That has been the ultimate goal of creating AI -- not to simply match human intelligence but to surpass it. If it surpasses us, does that make it irrelevant? Everything around us is powered by mathematics, most of it I don't understand, and soon perhaps everything will be powered by mathematics that everybody doesn't understand.
“Hey guys, the human condition can be experienced by these machines more efficiently than we are able to! Now we can finally die off!”
Why are we enabling a world that outpaces us? Is this going to lead to anything other than displacement in the long term?
Do you ever stop and ask why we’re building the world we’re building?
To what end does a world we cannot comprehend or rebuild from first principles serve us?
Because we kinda suck. Wars, poverty, destruction of the environment, etc. Maybe AI will be worse -- the are plenty of movies on that subject -- or maybe it will be better.
> Is this going to lead to anything other than displacement in the long term?
We may have already damaged the environment enough that we're going to die off soon anyway. So without AI, we were already on that path to "displace" ourselves -- I don't think that argument is that compelling.
Perhaps this is the natural evolution of intelligent life. Eventually the sun will die and everything we are will be gone forever, maybe this is the only way that something of us survives.
> Do you ever stop and ask why we’re building the world we’re building?
We're not building a world. We're just ants. Individuals making individual decisions that have nothing to do with the rest of the world. You don't have to look too far to find humans that literally care nothing about humanity as a whole and everything about themselves.
> To what end does a world we cannot comprehend or rebuild from first principles serve us?
We already live in that world. You wouldn't even be able to build a toaster from first principles without the knowledge and infrastructure that already exists.
They killed your comment, but you should know that you've posted it on the world's #1 AI propaganda site, where literally trillions of dollars are at stake and ROI depends on the correctness of the discourse running among the meat LLMs foraging or lurking here.
Everything is potentially interconnected.
What separates things is course-graining, not ontological separation per se.
How can humans tell whether machines understand the math if they themselves don't? Math is a human language for quantitative reasoning.
If large sectors of the population decline in their ability to read and write <natural language>, how will they be able to evaluate machine-generated <natural language>?
The second one is very important. While it is a matter of pride that some do understand, and (given enough time, in my opinion) will continue to understand, it is sufficient to be merely correct even if one does not understand why. This is a nice feature of math: it remains true regardless of understanding.
The obvious caveat is the machine needs to prove it in the end, even if we can not.
Your opinion is a possibility but it is not grounded by any proof, as such it is nothing more than an assumption. I think the more intellectually healthy approach is to list out the possibilities and figure out what they entail, rather than to throw all your eggs in one basket blindly, which amounts to a leap of faith or a retreat to commitment.
But that's just my two cents, take it or leave it.
I absolutely do not understand what are you talking about.
The question whether the tautologies exists regardless of whether we know about them or not is absolutely not a problem that is studied by mathematics and can be proven by mathematics.
The author of those sentences is also the author of the main result that this generated improvement builds on, and prior to this improvement his work was thought to be unimprovable. He is, in all of the important ways, literally the single most qualified human on the planet to asses this result. And his assessment is 1) this improvement is real, 2) this improvement is exponentially bigger than his work and the work he improved on, 3) it is effectively impossible for him (and by implication, humanity) to understand the result as presented, 4) it is seemingly trivial for “frontier circa 2026”-class models to understand the result as presented, and 5) to the extent that he is able to understand it, it is only with significant frontier model assistance.
So the place we are at, at least for this highly intellectual field, is “intelligence is the property of ~2 corporations, you must pay them a fee and agree to their terms and conditions to access it”.
At risk of stating the obvious, this is not a healthy place to be because intelligence is now structurally subordinate to the profit motives of particular corporations and at the mercy of all the attendant economic/sociopolitical incentives that apply to them. If the landscape is such that it is in these corporations interests to withhold this intelligence from some and make it available to others, that is simply what will happen.
One trivial example, of which there are an arbitrary number: An elderly leader is convinced to issue a decree that all number-math-computation intelligence is to be made available only to the intelligence agencies of his nation, and failure to comply means no IPO and no further investment. From this point on there would be a permanent nation-state-shaped shadow over practically all modern cryptography, with no recourse possible.
If the Church had a Large Astronomy Model in the time of Copernicus, the earth would have stood still - regardless of what it may privately prove about heliocentrism, it would never declare e pur si muove.
Observing the LLM output on problems that have clear solutions invented by humans, I tend to believe that too many people are jumping to conclusions too soon and interpreting b) as a).
If the LLMs are truly so great, we should see (in a short time, i.e. in order of months) a transformation of the math proofs generated by LLMs from the current state of indigestible mess to something that indeed looks like written by human for humans.
Only then would one be able to claim that we got into situation a).
"Our previous version was super-smart, but a bit autistic, even the smartest mathematicians had problems understanding it. The new version is not only super-smart, but can also efficiently communicate its ideas, so that they can be used in real life."
2) Actually, try to use the AI's smarts to gain in real life (not only convincing others to pay you, but actually do the smartest things). Hmmmm, scratch that, that is too idealistic. Use the AI's smarts to become the strongest meanest bastard the others will have no choice but to obey, or else.
If a paper/proof is impossible to understand by humans, it could be considered useless (irrelevant) for most intents & purposes.
Regardless how interesting or valid looking, might as well be gibberish pulled from /dev/random. Until:
a) Written down in a form that humans can understand. And/or
b) Turned into practical applications that are demonstrated to work.
In other words: safe to ignore, until a) and/or b) occurs.
If any of those papers were sent to a scientific conference, they would have been rejected as inscrutable gibberish: "resubmit once the presentation is significantly improved".
Yet, mathematicians spend their time trying to understand them.
Real life prisoner's dilemma.
Who would staff those intelligence agencies if humans are no longer able to understand the work product?
In any case, it was just one example, I am sure we can think of plentiful others.
The amount of unimaginable complexity contained in a seemingly very tiny amount of matter to us, is incredible, and it’s all around us in nature
We’ve never had the capacity to fully comprehend neither ourselves nor the environment we live in. But we do our best, and we do survive and sometimes even thrive
(and why math differentiates between conjectures (maybe true) and theorems)
so that "oh some 3rd party person/machine understood it for me" can feel... a bit icky
Everyone in advanced math research understands the topics they are reading about deeply.
This person is just literally being victimized by the AI incompetence apocalypse.
Information overload + trained laziness.
None of them were okay. Those were small small tools with few (sometimes only one) uses.
Generations earlier, famines, diseases and lead-leeching plumbing made rulers insane and entire cities of people die without any help from the "best tools".
Scifi is always written about the best tools. The issues arrive in the tools and in the hands that wield them both.
The fundamental argument boils down to: "who is all this being made for"? And the answer should be "us, humans". A lot of people here seem to breathlessly fawn over AI as some sort of value in itself, but this is incorrect. There is no absolute value in the universe (as far as we know), the only value to us as humans is subjective.
If AI is not generating value for humans, it isn't generating value, full stop.
Hence the question: "what's the point if we can't understand it?" is perfectly valid and should be discussed. The broader question of "what's the point of AI if not for improving human lives" gets lost in breathless adoration of a technology where nobody knows the outcome.
I'm not saying we need to abandon the pursuit of AI, but we must ask difficult questions at every step because if the technology ends up being as ground-breaking as many here think, it could just as well precipitate a dystopia.
At one point intuition helps to build out usage of the math itself. See stuff like "oh I can do rotations through matrix multiplication"-style ideas.
It is helpful for us to be able to reach for those tools quickly. The more tools that require advanced prep to use, the less we'll be able to use the results in easier contexts. Everything will be hard
Of course nothing really prevents people from looking for the simpler tools and proofs. Lots of math stuff starts off complicated and gets simpler.
Just important that this is still a goal in mind
1. These tools are wrong a significant % of the time
2. They have an upper complexity bound where they start to fall apart
Relying on AI to check AI papers is a terrible idea
But where they do differ is they do have an infinite stamina, and can work on problems in loops, self-correcting their own mistakes if a formal way of checking the correctness of the solution exists. Which does happen to be the case with math.
and why does this "peer-review" new-age thing cannot be used to scrutinize AI-writted/AI-assisted papers?
Aaaa, you mean 'peer-review' by AI?
Then the answer is: What is the use of a paper no human understands? Then no human can see that the techniques from the paper can be used to build something useful for real human life.
OK, ok, AI can decide to use those techniques to do something in real life it considers useful.
Are you now getting scared? Why not?
How can you tell?
If you don't understand the subject and relies on the understanding of the tool, how will you have a frigging chance of catching an error?
Anyway this reminds me that scene in Matrix 2, where the mayor (of sorts, don't remember his exact title) of Zion tells Neo about the machines that keeps everyone alive, but that there is nobody left that understands how they work or could repair them if they broke.
Gödel's incompleteness theorem only states that there are true statements that can never be proved to be true. That limits what tools can do, but it's not a "malfunction". The exact same limitation applies to humans. A genuine malfunction would be if the tool "proved" a false statement to be true. That actually has happened before because of bugs in proof checkers, but software bugs can be found and fixed, and proof checkers are simple enough that it's possible to write many different implementations. It's extraordinarily unlikely that the same malfunction occurs in all of them.
All this anthropomorphism of literal statistical text generators.
Yes they exhibit generated sentences of text that describe coherent responses about a topic when there's an inordinate amount of research material in the training data from which they are distilling sentences of sound and rational thought. That's the ONLY way it can possibly work. There are so many scenarios where the training data isn't setup like that and low and behold the generated responses are just literal garbage.
We can explain how LLMs generates this output.
Humans can't be that simple, there must be some ~~divine~~ unique human touch to what we do.
Every day LLMs, running in frameworks that add earlier "thoughts" to the context and iterate, reflecting on their own thought process, gets better and better at doing something even less distinguishable from human output.
The gap gets smaller. The "unique human touch" will always be in the gap.
You just fly in from astroturfer kookoo town or what?
Honestly it is not that much different from the divine inspiration if you have ever experienced it.
I do not know how you view the world or walk through it but language in general is simply subject to interpretation to me, the arrangement of words for the sentence is inspiration in and of itself - though the majority of time it amounts to minimum jumps.
The whole world has always been like this. It's just showing up with force here.
There's a huge lot of suicidal people who ideate their afterlife as "pursuing hobbies".
Oh? Well I have not checked most of the details either, but I think that the claimed o(n log n) bound is not plausible at all.
Can someone please check most of the details of the paper and resolve our disagreement with Mr Harvey?
@platypii and I obtained a Lean proof of the same improvement for DFT over prime-order finite fields (yielding polynomial multiplication via the convolution theorem): https://github.com/platypii/integer-mult-bounds-lean/blob/ma...
This is very odd, because it just doesn't match my experience with these systems at all. Yes, they do generally SOUND confident, but code (say) that's hard for me to understand is usually hard for the machine to understand as well, and while it is more indefatigable than I am, it's not brighter.
This mathematician is in a very good place to evaluate whether Fable gets it or not, but it's weird and counter to my experience.
Heck, I can do this too, with my eyeballs and brain! Just slower and will less overall memorized knowledge than modern LLMs.
Fable is good, especially at conceptual stuff, don’t get me wrong, but it has limits. Opus 5.5 is a machine at getting correct code out once the concepts are in place. But I can’t trust either to not make mistakes.
In fact, he refers to Landau for the definition of little-o.
The big O notation is very often used to denote exact complexity of an algorithm even if the notation is technically an upper bound.
The little o notation is usually used to denote a small asymptotic improvement over some known complexity.
For example if some sublinear algorithm would have n^(7/8) complexity - would be probably advertised as big O of that. But n^(1-eps) is shared as o(n) with exact constant provided as a detail.
f(n) = o(g(n)) means as n increases, f(n)/g(n) must shrink
s/must shrink/must eventually shrink, after finitely many n/
Equivalently, must go to zero in the limit.
But you're not right in that the upper bound of that expression eventually stops decreasing, in fact for simple f and g as long as f is positive and eventually bounded by g the expression will decrease infinitely often if my intuition is correct
This isn't quite correct. O(g) doesn't require the limit to exist.
If you were joking then maybe you got me
https://stackoverflow.com/questions/1364444/difference-betwe...
O(2n+1) = O(2n) = O(n)
o(2n+1) > o(2n) > o(n)
Both define upper boundary, but big O can read like "the function is generally proportional to ___" and little o can read like "the function is never more than _____"
I don't think any of those are right. The latter two are trivially equivalent, the former also is since it differs by a factor of at most 3 for all n >= 1.
I still think the simplest definition for o(f) is 'g in o(g) iff O(g) is a proper subset of O(f)'. Or you can just say g is dominated by f in the sense that lim sup g/f = 0.
I'm fairly sure those two are equivalent, but it's a good little exercise.
Big O is sort of like <=. little o is sort of like <.
https://dl.acm.org/doi/10.1145/1008328.1008329
O(n) is: "there exists a K such that, for sufficiently large n, K*n is always larger".
o(n) is: "for every K, for sufficiently large n, K*n is always larger". (So o(2n+1) and o(2n) and o(n) are the same thing.)
For what it's worth you can leave out the 'sufficiently large n' part, assuming we're talking about positive integers (or continuous functions on the nonnegative reals). I like to leave it in though because it makes the jump to defining both o, O and Theta in terms of lim sup f/g a little easier.
I can't quite work out whether the author is demonstrating comedy, is having a hysteria from a breakdown, or if it's something in between, but it completely captures the feeling at the moment of being usurped and out-gunned by AI tooling as a knowledge worker.
I’m trying to come to terms with this myself. I agree that this future is inevitable, and progress will come regardless.
I feel a lot like Grant in Jurassic Park right now.
Which is exactly what the capitalists that own these systems want. Divide and conquer.
"Pay no attention to the men who are responsible for you situation, instead pointlessly fight amongst yourselves."
Contributing to these systems beyond this point should be seen as a massive failure of character. Stigmatizing to the point that they have to scrub their socials, lie about work history, and hide it from future friends or partners.
One can dream. Society does not have the integrity for this, unfortunately.
> "Pay no attention to the men who are responsible for you situation, instead pointlessly fight amongst yourselves."
Maybe so, but that advice is for the western left, who openly and boastfully despises the working class these days. They might have thought about that if they wanted any "working class solidarity". It's been a staggering achievement either incompetence or coordinated malice that they could flip the balance of blue collar voters to right-wing parties in a generation. They chose who they wanted to lie with.
I'm not sure what argument fable gave him, but this part seems not horribly difficult to prove. I'm sure i'm missing something since i'm not that great at this kind of math.
The short reason is that each slice call can at most double the number of nonzero entries, but a transform that would enable g < m must turn a single nonzero entry into (2^m) nonzero entries in a single call.
Since the permutes can't generate non-zero entries (since they only move things around), you actually need exactly m slice calls in the initial 2-instruction model given (where you can only do slices and permutes) precisely because of the need to create 2^m non-zero entries where each individual slice op can only double the number (and permutes do nothing)
Astra actually comes up with a variant of this argument as well: https://gist.github.com/dberlin/e785c51aff0285c9c0a3ea0f24fa...
Interestingly, the operations discussed in this part of the note are, I think, pretty much exactly the operations needed to operate on classical representations of an m-qubit quantum state, implementing single-qubit unitary quantum gates on each qubit.
I suspect that, if you had asked me fifteen years ago if I thought you could do the same exact operations like this on each of n different states (on a classical computer) faster than n times however long it would take to do it once, I would have said “sure, why not?” It probably helps that I worked with people who really liked matrix multiplication and similar problems :)
For fun, I have ChatGPT Pro chewing on the following prompt:
Suppose I have a m-qubit quantum state. I can do an operation U = U_1 \otimes ... \otimes U_m, which is just a unitary operation on each individual register.
I can also simulate this on a classical computer. Start with the state, written out classically as a complex amplitude for each state in the computational basis (i.e. 2^m complex numbers). U acts on this state in a linear manner to produce a new state. What's the classical time complexity of this? (Assume a model of computation with exact complex addition, multiplication, conjugation, etc taking unit time, but don't cheat by encoding absurd things into complex numbers. Assume data movement is free.)
Now suppose I want to apply the same U to n separate input states. Can I do better than n times however long it takes to do the operation once?
Treating OAI's name like Voldemort reads as emotionally immature to me
My extremely limited understanding is “no” as I sorta assume that would be the headline. The main point being that while a MASSIVE improvement over any previous progress it’s still “small” from a practical standpoint.
This is not to downplay the very well conveyed problem this might be, but I just want to make sure I even got that right.
I do wonder if this will be a major issue for future development or if the math experts in diapers right now will grow up in this world and have tools and understanding to assist and we’re just in a rough transition, but unfortunately that’s about as much as I can contribute.
>The result is of extreme practical importance. Not for actually multiplying integers (as usual with these algorithms it’s probably not faster than existing algorithms for integers that can be stored in the observable universe), but for writing papers. It has always been a hassle to write down the complexity of integer multiplication or algorithms based on integer multiplication by introducing soft-O notation, little-o exponents, epsilons greater than 0, or iterated logarithms. From now on I can just write O(n log n) in my papers and be done with it!
This new result means painful typesetting again.
edit: I mean this seriously. This kind of thing
> Some of my colleagues believe that this is a nefarious Company, whose only goal is profit, if necessary at our community’s expense. Perhaps there is some truth to this, but I think it is missing the larger point. The fact is that there now exists an automated system that can perform mathematical research — at least the part that involves “solving hard problems” — far better than any human. Now that such a system has been demonstrated, it is clear that results like these were bound to start appearing soon anyway. I do not blame the Company for getting there first. Who got there first is an irrelevant detail, a historical footnote.
is very pleasantly honest and well-thought-out.
> I do not blame the Company for getting there first. Who got there first is an irrelevant detail, a historical footnote
Oh, such a noble mathematician, he's so above the questions of moral, Truth is all that matters.
Almost as much as a paycheck from the Company.
In either case, no practical algorithm is expected to come from it, because the asymptotic gain isn't realized for practical input sizes--sometimes called a "galactic algorithm". But it's theoretically very interesting for breaking through a conjectured barrier.
* Start with the usual FFT convolution for polynomials, viewing integers as a polynomial in a given base evaluated at a certain point
* In undergrad you (hopefully) saw polynomial multiplication can be done in nlogn via convolution using FFT, but that assumes complex integer addition/multiplication is O(1). Here we need O(log x) bits to simply represent a number x on a real machine.
* If you use naive (n^2) multiplication for those butterfies, then you end up with O(n[(log n)^2]).
* If you recursively use FFT for the digit multiplication at those butterflies, then you get something like O(n[(log n)^(log log n)]).
* Schonhage-Strassen realized that instead of doing FFT in C where the butterfly involves multiplying by \omega (\omega being roots of unity), you can work in a different ring where 2 is a root of unity and so it's a cheap bitshift, but were constrained in how finely they could break subproblems (could only break an n-bit integer into sqrt-n subpproblems each of sqrt-n bits), resulting in an overall O(nlogn * loglogn).
* The 2019 O(nlogn) version (supposedly, this is out of my depth) found a way to use some technique of "Gaussian Resampling" to do the matrix convolution via _multidimensional FFT_ in a ring where multiplying by root of unity became cheap _and_ subproblems could be finer. The bottleneck was thus only the addition/subtraction in the O(log n) butterfly layers, each of which does O(n) work for total of O(n logn)
* OpenAI's paper uses a fact that it's apparently easier to compute multiple butterfly layers "simultaneously" than doing them one at a time independently (page 10). There is some sort of implicit reuse because the later layers mix the outputs of earlier layers, and they take advantage of this to avoid having to write out layer 1 outputs separately from layer 2 outputs, and instead work in some coordinate system where they can intermingle them. This apparently amortizes to some tiny fractional epsilon shaved off of a layer, and since the whole FFT is recursive they can apparently save a factor of (1 - epislon)^(log log n) overall across "log log n" recursion depth, which is apparently where that ridiculously small overall exponent comes from.
A useful performance measure for the evaluation of a function in hardware is the latency of the operation expressed as a multiple of the delay through some standard simple logic gate, e.g. the FO4 delay (of an inverter with a fan-out of 4) that is frequently used in CMOS technology.
While a multiplication can be done in a single clock-cycle if you choose so, the duration of that clock cycle in FO4 delays increases with the size of the numbers that are multiplied, and it is interesting to know how fast it increases with a given multiplication algorithm.
If a multiplication algorithm that claims to be faster when expressed in some abstract operations leads to a FO4 delay number that actually grows faster with the length in bits of the operands, than that algorithm is worse in practice.
This is the future.
Advanced AIs are basically genie's bound only by the laws of nature (which are necessarily the known laws). You ask the genie for something and if it's possible, it will grant your wish. What it does will seem like magic to you or I, and while in theory there is an explanation for the magic it's preforming, in reality an explanation could be as complex as understanding how the AI itself works at the level of its weights. It's not something a human mind can do.
The genie might give you a cure for cancer, but you won't know how it works. It's possible it will cure all cancer but with some hidden cost. A cost we cannot know and one we must trust the genie is both aware of and would inform us of. Maybe it will make us more trusting of the genie, or maybe it will just make us all drop dead in 20 years time.
The world we are now entering will look similar to the world native populations entered when they encountered Europeans. Suddenly people were turning off the sun and yielding magic kill sticks. It was basically impossible to reason about what might or might not be possible.
This future, is for now, still optional. We can still decide if we want it, but when the genie in its final arrives, perhaps one of the only things it won't be able to grant us is the ability to undo time and take back our decision if we later decide it was a mistake.
The most important thing about this paper is that it's describing human loss of control. A world where we are no longer the masters of our own destiny.
That made me chuckle...
There are many ways to communicate ideas, and most people in most situations rely not upon proofs derived through deductive logic. They rely instead upon persuasion, intuition, and building associations between ideas. Those tactics are in many ways more natural (as in, fairly direct consequences of brain structure rooted in biology) but with known flaws. LLMs are pure persuasion, and operate entirely in that realm. They can be cajoled into repeating logically unsound ideas in much the same ways humans can. And, they can do the same back to us.
My concern is that having one model generate a paper, and then using another model to explain and answer questions about the paper, could run the risk, especially in arenas where the number of individuals with deep deductive knowledge of the subject is small, of optimizing for persuading those individuals rather than proving real results to them. Since it's reasonable to presume the model has been trained on other explanations which have convinced the expert, and on convincing people generally (which, on balance, uses more flawed intuitive explanations to persuade rather than prove), it should be very good at least at creating an environment where the expert would need to practice extreme skepticism and fully utilize critical thinking to attack every element of every claim. That can be very challenging.
Stopped reading right after that. Why is this so high up on HN? It's literally nothing.
The expansion (on p3) of the statement after which you stopped reading goes on to explain that one of the issues is that these results have not been delivered in a humanly intelligible form. (“… the maths dump does not appear to be strong on exposition”).
Your question would have been answered if you'd just read a little more
If anything, I'd treat him as a table of contents to things other people do.
Previous time it was "I don't care about ai papers by default, so who cares"
either way it's not good to not even try and to "proudly" put your lack of effort front and forward
web.maths.unsw.edu.au fills the whole address bar on mobile