Steal someone elses work, whose work was also AI generated . . .

lol, the "other work" was also probably 95-99% AI generated. By a similar breed of OpenAI (and some Anthropic) models, as well.

I dont know why this monumental achievement is being drowned out by some arbitrary drama. No matter which way you slice it, AI solved this problem. Doesn't matter if it was some internal OpenAI model, or whether it was Astra + Fable.

What is that supposed to prove? OpenAI is almost always going to be training new models.

All the ai labs are open about training on prompts. The question is if buckmaster had disabled that with the toggle openAI provides.

what's there to admit? they always said they do it and there's a way to opt out. you are making it sound more dramatic than it is.

Will history look back at comments like these as people being dumb, or people trying to cope?

You can see that your math friends completely wrote off LLMs entirely and were showing signs of coping.

4 years ago it was a "not yet" [0], since ChatGPT at this time was not ready nor it was "AGI". Now with this 'unreleased' AI model, it has reached a point where it has solved an unsolved problem which only one human solved a millennium prize problem (Poincare conjecture).

Now finally "AGI" means something again.

[0] https://news.ycombinator.com/item?id=33905609

Some observations:

1. It seems at least possible that some of the proof of NS was contained in the training data, making it less novel.

2. The formalisation of mathematics into lean has been an underappreciated force multiplier on discovery.

As someone with a background in AI and who has been playing around with neural nets for decades at this point, it's been genuinely amazing watching extremely intelligent people make confident predictions about AI capabilities and progress, then be so completely wrong.

There's a kind of theory of mind for AI (specifically neural nets) which I now realise I seem to have which is very hard to explain to people who haven't felt the magic of these algorithms. In fact, the algorithmic details almost doesn't matter at all. When you have a generalised learning algorithm really the only essential components are – compute, data and time. So long as you can scale these you can be certain you will also scale capabilities. There is never any exception.

That said, the capabilities neural networks tend to progress in step-functions rather than scale in correlation with compute, data and time, because algorithmic improvements tend to come every ~5 years and bring a significant step change in capability (or efficiency depending on what you measure).

I think people like Dario and others working at frontier labs see and understand this very clearly. And I suspect it's also why they worry about AI risk because even if you ignore the significant increases in compute and data these models are being trained with, it's concerning that it only took two real algorithmic improvements to take us from mostly useless predictive language models to AGI-level intelligence – and we're due another step change.