If I design a bulldozer to push a five ton rock, did I push the rock or the bulldozer?
If the AI really did get the Millennium Prize, then why can't you get a Millennium Prize when you have access to the exact same model in ChatGPT?
Unequivocally the bulldozer. You get to take the blame in design of the bulldozer, though.
Most scientific breakthroughs are just the completing the last 5% of work already done, but that last 5% is very hard and still only happens very rarely. That an AI was able to synthesize all the work and bring it forward is evidence that AI can make novel progress on the same level as renown mathematicians.
"When Ι asked him how he had learned of the Michelson Morley experiment, he told me that he had become aware of it through writings of Η. Α. Lοrentz, but only after 1905 had it come to his attention! "Otherwise" he said, "I would have mentioned it in my paper!" indeed, Einstein's 1905 paper contains no mention of Μichelson's experiment or references to Lorentz's papers."
From https://physics.stackexchange.com/questions/89375/did-einste...
Correcting a professor at MIT (head of the department alas) on this erroneous belief during a grad school interview cost me admission as he insisted otherwise and wouldn't back down. Ironically, one of my college professors was interviewed on NPR a week later and confirmed what I had stated.
Ask me what I think of checked out, tenured academics. Go ahead...
>https://link.springer.com/chapter/10.1007/978-3-663-19510-8_...
https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf
"The preceding memoir by Lorentz was not at this time known to the author."
But that's not what it did. A mathematician literally prompted it towards that, helping it every step of the way for years.
And only the mathematician was able to make the AI do that. Anybody else with the same AI could not do the same thing.
If you think it was the AI that solved a Millennium Prize, then why don't you try using ChatGPT to get a Millennium Prize?
The model which did the NS solution is not yet available to the public, and it required over 15 million dollars of tokens (based on API pricing) to produce the solution.
>And only the mathematician was able to make the AI do that. Anybody else with the same AI could not do the same thing.
That is only speculation, the total sum of the prompt's help to the model could have just been suggesting research directions. Also mathematicians talk to each other and use/learn from each others' research all the time. Should every mathematician, in order to produce a "legitimate" proof, lock themselves in a room for the entire duration of their work and ensure no one else helped?
Why don't you try getting a Millennium Prize then? If you think it was the AI that did it, you have access to the exact same ChatGPT.
But since Turing's time we've known that intelligence is just computation--it's not until recently that we've been able to come up with the specific algorithm.
Think back to Kasparov playing Deep Blue. Back then, some people (including Kasparov) believed that a computer would never beat a human. They felt that human creativity and ability to see the whole board would always beat brute-force computation.
I watched the pivotal game 5 live. There was a point where Deep Blue made a pawn move away from the main action. The commentators at the time, chess master all, almost cheered--it looked like the machine had blundered. "It's playing like a computer" they said. But one look at Kasparov told you they were wrong. Kasparov was worried. The main action resolved, but in the end, that one pawn move, 20 moves prior, left Deep Blue in a better position.
What modern LLMs do is apply brute-force computation to any domain expressible in language--not just a restricted chess domain. That's the algorithm.
Which means that companies with sufficient computational resources and money will be capable of unlocking problems thousands of times faster and more effective than any individual even when lacking the skills, just by a matter of try and error.
The answer I got from chapgpt was essentially that it has very little to do with superstition and all to do with being able to use a language to talk about stuff while still not move outside cultural norms. More sort of a secret language where you can probe questions like if your boyfriend is violent, or if your friend is having an affair.
I think last year I saw some research on how the reading of tea leaves originated in the ottoman empire, it was remarkably similar. The point is that I learned something new that would have been extraordinarily hard to google, or even understand without putting some serious study into the subject.
Then how can you possibly know that it's true?
From the expose in Terence Taos blog [1], it seems the difficulty of the Navier-Stokes counter example is a delicate balancing act between having a blow-up solution and a well behaved force field. And this involves a lot of technical arguments based on already existing ideas.
If this is true, then the achievement of the AI is rather to correctly navigating this balancing than inventing something completely new.
[1] https://terrytao.wordpress.com/2026/09/07/finite-time-blowup...
Deterministic machines do the same stuff again and again. Add entropy and they do new original stuff. Add a checker or verifier and you can filter for new stuff that is better. At the very least here, you now have evolution.
There is nothing that is particularly compelling about a system that can generate new stuff that is an improvement. What's compelling if anything is the verifier, but that isn't particularly any more mysterious than LLM output already. At least not nearly as mysterious as "Meat brains have a magical ability to manifest original ideas".
No, no. Any random sentence generator can generate original idea. Actually it is said that randomness contain all the answers. You don't need a "meat brain" to do that.
From https://openai.com/index/navier-stokes-solution/:
> we cannot rule out that de-identified data derived from their usage of our products helped improve our models
I believe AI is quite capable in the right circumstances, but I'm not convinced "this" is the watershed moment.
The right way to look at it is that LLMs help us to maximize the utility of the entirety of current human achievement/knowledge by discovering obscure connections in it.
"I remember early systems struggling with something as simple as 2+2. Then, within just a few years, we went from that to systems achieving IMO gold-medal-level performance and now, assuming this proof is correct, to a Millennium Prize problem. That completely changes how I think about the trajectory".
How would Sept. 8 completely change how they think about the trajectory? Seems like there has been tremendous progress at all time.
What do they need to do for you to consider them non parrots, and do you consider a lot of humans as parrots?
I don't think we know enough about how they work to claim that. OpenAI said they had 10 THOUSANDS agents working on the problem, testing all ideas they found in the literature (including, it seems, the breakthrough of the guys who had it for the hypo viscose case).
If this can only answer questions, then it fails this test. Because at least the question has to come from somewhere...
What's the tl;dr here?
"I'm one of the last few who needed convincing, now listen to my thoughts on what's next!"
I can't imagine having eyes and being able to hold the wrong belief regarding AI for so long. The fact that AI can surpass humans and make novel contributions to our civilization was obvious for me at least about a year earlier.
You need to have pretty messianic view of humans to believe otherwise.
It was evident in GPT-3.5
imo it's been not like that for a few years now
(alternatively, stochastic parrots' abilities have been underestimated)