I wonder how many of the recent results are due to the fact that very few looked at the problem to start with. Still great results, but the general impression is that it's more about the so many low-hanging fruits than the actual capability.
Now on to the Voynich Manuscript :)
More money than the GDP 90% of the sovereign countries around the world is hanging in the balance, and people are taking everything OpenAI and Anthropic are saying at face value as if this isn't the financial / marketing equivalent of war, assuming they they wouldn't use every legal and shady tactic, bending every truth available to them to sway the balance of public opinion in their favor. It makes me feel like I'm living in the twilight zone. People need to wake up.
https://news.ycombinator.com/item?id=48600107
https://aiclambake.com/clamtakes/linear-a/
Despite the announcement originating from a blog named "AI Clambake" covering "weekly, human-powered newsletter for advertising folks". Written by a personal friend of the author. Announced without any corroboration or commentary whatsoever from academics or subject matter experts of any kind. And, of course, not submitted to any peer reviewed journal or even Arxiv.
The author of the purported discovery was described as a "self taught AI engineer and amateur linguist". In the comments the friend insisted several times that a draft of the paper (not posted), was emailed to a top professor at Rutgers, giving it additional credibility that his friend wasn't another one of ten thousand cranks who has made the same claim over the years (seemingly unaware that cold emailing random professors found from a Google search is the first thing basically every crank does).
You would think this should have set of dozens of alarm bells for everyone, making the value of this announcement basically zero. And yet it hit the front page with the bulk of comments ecstatic that some random guy with Claude Code could do something experts in academia who spent their lives devoted to the problem couldn't.
I had become accustomed to the toxic optimism of this hype cycle in which even mild criticism leads to accusations of being a discredited "AI skeptic"/Gary Marcus/Ed Zitron type who was "coping" (?). But this was like something you'd see shared on FB linking to a .xyz domain by an elderly family member who recently drained their accounts buying Xbox gift cards to pay their IRS bill.
It feels a lot like the week or two when HN was overflowing with exuberance from the LK-99 room temperature superconductor "discovery ". You'd see post after post fantasizing about an imminent future with a world full of maglev hovercrafts, MRIs built into every phone, fusion reactors and more. But people pointing out none of that was scientifically plausible and evidence of LK-99 superconductoring was non-existent were accused of knee-jerk negativity and the typical HN cynicism and pessimism.
Compare e.g. https://arstechnica.com/science/2019/05/no-someone-hasnt-cra... . (It's a debunking, but the reason a debunking got published is the media hype frenzy beforehand.)
That does not mean that specific instances of it are still very interesting though. This article is the "I had claude vibecode a thermostat for my bathtub" of cryptography.
* And in this case I'm not sure it even meets that bar. For all we know a couple readers back when the book released had a delightful afternoon with it, solved the riddle, then forgot about it.
It kind of blows my mind how quickly people have forgotten both the state of AI in ~2010, and the outlook. If you had asked 100 people in 2010 whether they would see AI that could actually pass the Turing test in their lifetimes, you would have got 100 "no"s.
AI had been an unsolved problem for literally decades and it was firmly in the nuclear fusion/flying cars category.
Edit: if you're going to try to stage an intellectual wrestling match on the topic of is this thing awesome or not, you might as well make it a proper wrestling match and maybe get greased-up Turkish style. It would be more entertaining and you'd be more likely to arrive at a meaningful conclusion.
you can't claim "you keep moving the bar", if the very first thing when an LLM drooled out a piece of code, was to proclaim "this is good enough cause it gets the job done" followed by a barrage of "we're not quite there yet but exponentials or something, so very soon it'll be incredible"
yeah, from there it sure looks like "moving up the bar"
But, at the same time, I'm really tired of there always being some shroud of dishonesty (ex. navier stokes and the two mathematicians working on it).
At this point my default is that I don't blindly trust the companies, I try to keep in mind they are trying to sell their product and win market share, there are so many perverse incentives at play I just can't take anything at face value.
There's no way that's accurate.
We already had big claims of the Turing test being passed in 2014, by a bot that had been doing almost as well for years.
There were plenty of people expecting fusion in their lifetimes too and that's going okay.
Nah there was that bullshit Loebner prize or whatever, but that was just shitty chatbots being "judged" by people asking questions like "how are you today?" and then being breathlessly reported by the press. It was a publicity stunt.
Perhaps I should have said "100 people well-informed about AI".
That's not what happened, go read about it harder, please.
I think there's a bit less to that than meets the eye. Yes, OpenAI's result builds on human work. It's possible that it builds on more human work than OpenAI admitted. But even if we suppose that everything Buckmaster and Alpöge did (which, btw, was itself very heavily LLM-assisted/generated work) was a necessary precursor to what OpenAI released, it's still the case that OpenAI's clankers completed the solution and Buckmaster and Alpöge didn't.
My understanding from what Buckmaster has written about this is that the deep mathematical ideas behind their work (and presumably OpenAI's) are due to Córdoba and Martínez-Zoroa. Those ideas are in the published literature, and human mathematicians and AI systems alike are allowed to use them, and doing so doesn't mean they didn't actually do something impressive. Mathematicians build on one another's work; that's how mathematics progresses and always has been.
It may very well be that OpenAI's announcement has a serious problem of professional ethics, especially as their first version of it didn't even list Córdoba and Martínez-Zoroa in its references. (On the specific question of what if anything they learned from B&A's work before that was published: OpenAI are now claiming that after investigating carefully they are confident that the model was not trained on anything Buckmaster and Alpöge did after early July. B&A had been working on this thing for much longer than that. However, on Buckmaster's account of things it wasn't until mid-August that they got beyond what he calls "preliminary results".)
But! The results of B&A were themselves largely AI-generated. (From Buckmaster's statement: "on August 15th, we obtained the blow up results, with smooth forcing, for both Boussinesq and Euler. I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable." That is: the LLMs found the proof, and B&A had to work to understand what the LLMs had done. It's not that humans did the thinking and AIs just did the gruntwork. (Except in so far as one might want to give all the credit for Real Deep Cleverness to C&MZ.)
And! What OpenAI say their model has proved goes well beyond what B&A did.
I don't see any way of slicing this that makes it unreasonable to say (unless it turns out that there's an error in the proof -- unlikely, given that it comes with Lean verification, but there have been misformalizations and Lean bugs in the past and there surely will be in the future) that AIs solved the N-S problem. No, they couldn't have done it without the work of C&MZ, but again: important mathematical work almost always builds on earlier important mathematical work, that's just how it is. Yes, if OpenAI are lying through their teeth their model might have had early access to B&A's ideas -- but it seems like most of the B&A work was actually done by AI systems anyway.
It is (I think -- I am not an expert and in particular I have not so much as looked at OpenAI's publication) reasonable to say that the deepest ideas here came from humans, and that it was already widely expected that the N-S problem would be solved in the not-impossibly-distant future in something like the way it has been. So, sure, what the AIs have done here is much less impressive than if they'd settled the Riemann Hypothesis or (probably even harder) PvNP. But it's still a resolution of a famous mathematical problem that any human mathematician would have been very proud to have achieved.
I would say, the only reason it was never solved was because not enough people actually cared about it to begin with.
This isn't a big accomplishment.
(1) take a 300 line NN algorithm,
(2) throw a quarter of the world's GDP + all literature ever collected at using the algo to train a NN
(3) throw another quarter of the world's GDP at billions of teraflops for inference, and
(4) aim the resulting world's-largest-computer at marketing itself to investors, for instance by decoding ciphers from obscure medieval manuscripts,
that you could perform some pretty magical tricks. There are other feats humans have performed for less cost, like sending people to the moon, or landing a rocket vertically, or idk, curing Polio.
I'm not knocking the "miraculous" advance here. The unique thing about the solution which makes it particularly non-trivial and something that humans would struggle with was exactly what LLMs excel at: Diffing loads of texts against each other. But the 176k tokens at around $10 doesn't tell the story of the cost. It says a lot about the externalized cost and the amount of money flowing in to support the hardware. If they'd put a $100,000 bounty out to solve that cipher, I think the internet would've solved it in a couple days.
IC production takes a vast amount of resources and wealth, and it's a known quantity (after all, we've been doing it for decades), but it's still impressive what modern fabs can achieve.
First, it’s AI can’t multiply 4-digit numbers.
Then it’s AI can only, by brute force, get silver in the IMO with specialized systems.
Then it’s OK, well, now a general-purpose model can get gold, but it’s still just the IMO, it’s for high schoolers.
Then it’s OK, it can solve a few trivial Erdős problems, but only because nobody seriously tried them before, they were low-hanging fruit.
Then it’s OK, a lot of serious mathematicians tried this one, but the result was still obvious in hindsight, it just combined knowledge from a thought-to-be-unrelated field, if any human knew that, they would solve it.
And then to OK, but there are still Millennium Prize Problems.
Then OK well it's just Navier-Stokes wake me up when its the Riemann Hypothesis.
Then-
Given the close relationship between compression and intelligence, I'm somewhat surprised at how poorly the cutting edge models do with being concise.
For Earth, the proof presented for NS is just our first attempt navigating from our previously known facts to the proof.
I expect we will be able to shorten it dramatically (most likely with human and AI insights), but I don't think we should read too much into the length. If you want a similar point of comparison, see the original proof (by humans) of Fermat's last theorem. It has been shortened significantly. This is normal.
because they're not intelligent in the sense you're hinting at (conceptual integrity or generalization) but they are as the name suggests, large. Like comparing a forklift to a human. It's easier to bulldoze through a lot of things than tie your shoes.
If we weren't quite as impoverished conceptually and still had the vocabulary of the Catholics we'd recognize this as ratio (discursive knowledge) vs Intellectus (apprehending knowledge)
Prove that human intellect is different and that we solve problems using fundamentally different processes. I’m waiting.
the question is, when comparing a human and a large language model, whether the intellect (that cannot be captured in language) is different from anything the language model can actually do (e.g. language)
the answer to this seems quite obvious to me, and I would actually posit that the onus is on the other side, to prove they are even remotely similar
maybe people think that the voice in their heads is what is doing the thinking? is that the confusion here?
No, it's the other way around, it's a reductive view on intelligence that mistakes its own methodology for ontology.
It's obvious to see that there's no intellect in an LLM as defined above because of how they work. LLMs put one token in front of the other, they don't work towards formal ends, there's no intentionality in them. They don't synthesize the information they process into a unified experience. Thinking an LLM can apprehend what it does because it can process large amounts of text is like thinking your TI-83 understands math because it can multiply large numbers.
That's also why the failure modes of LLMs are what they are. They can churn out tens of thousands of lines of code but also just as easily go in circles like a roomba. They can process an entire encyclopedia but not solve problems a 10 year old can solve.
> how poorly the cutting edge models do with being concise
LLMs solve a Millennium prize problem. People complain the proof is too long, within a week. What a time to be alive!
It is the mechanism the LLMs use to do it. They seem to accel right now at quantity of work over quality of work. I'd be willing to wager there is a much simpler way to achieve the proof.
I've also seen this with code, LLMs do get the job done, but they tend to write 10-100x more code than humans to get the same job done. Still a massive value gain because they can write that much code extremely quickly.
We weren't willing to pay for 200 math PhD students to try to find singularities in Navier-Stokes, I am skeptical of how much we would be willing to pay OpenAI to do research on "niche scientific areas"?