I've been wondering whether AI really is improving rapidly at open problems or we're being fooled.

- OpenAI invites researchers to use their models, in fact giving at least 100,000 researchers free access[1], but there are also those that pay

- Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]

- But researchers will typically work on open problems. A researcher who is using Codex to make progress on open problems will be feeding it fresh training data on precisely the problems the internal models are evaluated on.

- So while it looks like the new models are suddenly solving lots of open problems, they could be significantly piggybacking on human progress, with models "inspired" by the work of researchers from all around the world?

This theory predicts that there'll be many more researchers coming forward just like TFA, as sOpenAI announces more solutions. It doesn't assume all of AI progress is a mirage, just that there's plagiarism.

[1]: https://openai.com/index/chatgpt-for-academic-researchers/

[2]: https://xcancel.com/OpenAI/status/2097374643518640382#m

> I've been wondering whether AI really is improving rapidly at open problems or we're being fooled.

I think your suspicions are warranted and your explanation seems plausible.

If better training data is the reason here, it would still be a case of the models doing something that is in and of itself super useful! The models really can take that data and distill it into solutions for similar problems faster than humans can. This is great!

But there's so much vested interest in the AI companies to be opaque about all this, to hype up their models and avoid giving credit to people whose data made everything possible, that they would never tell us this fact if it were true.

I feel like so much of the AI hype cycle is like this. The models develop extremely useful capabilities, but it's hard to understand what they really are through the hype. The lies and obfuscation by their owners who have vested interests in capturing the value they provide makes it impossible to take anything they say at face value.

I’d argue the invitation of researchers was incredibly strategic.

Sam Altman knows what he’s doing. He will happily screw these folks to one-up his competition.

A mental model I was thinking about was - I remember when Travis Kalanick was talking about using the chatbot to discuss “vibe physics-ing” on the all-in podcast.

And like - I think there’s a presumption you could make that AI models could overfit to asymptote towards just the capabilities and knowledge we currently have.

And that would be amazing! And crazy useful. And there are probably a whole world of complex problems that remain unsolved because they’re adjacent to knowledge we have but they haven’t been invested in.

But can a human reliably tell the difference between “can do 99.999% of the things we currently know how to do which includes a small subset of things we didn’t know we had the capacity to do” and “super intelligent math and science research pushing the frontier of what we know”

A physicist that knows all the things we currently know in excruciating detail feels like it should be able to make the leap beyond the frontier.

But since these are computer models it might just be that it can ride that line extraordinarily well while the line remains firm.

there are also attempts to crowdsource human research directions - like the caltech mathathon challenge : https://mathathonchallenge.com these would help models on the same problems at the expense of the researchers. basically, math researchers are the reverse centaurs but they dont realize it.

>> Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]

Maybe I'm failing to read that graph properly but the y axis says "pass rate" and it only goes up to 0.5. That would mean every single problem is at most half-solved.

I don't know what that means though. What is "0.5 pass rate" in the context of "open math problems" (as in the graph title)?

Seems easy to picture high stakes startup cutting corners to justify their fame.

> they could be significantly piggybacking on human progress,

This is AI in a nutshell, its a plagiarism machine. An abstraction layer between vast amounts of stolen human-generated data that filters out the liabilities and accountability for that original theft. Its an IP laundering system.

It tells me that AI companies are just another mechanism to extract and extort value from the masses for the rich.

Just another rich man’s trick

Perhaps the last one before they destroy that world and try to hide away as people forget and history is rewritten again. I don’t think they’ll succeed this time.

The pudding is in the proof. The field is mathematics, the proof can be rigorously verified. If there is a flaw, OpenAI is out to lunch. If the proof is valid, OpenAI has produced something new.