Cal Newport

Okay.

The second thing that made this OpenAI result novel is the fact that it was an important problem.

And that's by far its biggest distinguishing factor is like people knew this problem.

If you're a combinatorial geometrist, as I know so many of you in my audience are,

This was a well-known problem.

And so this is like the first cool problem that was solved.

This technique of using LLMs and computer tools to help find proofs or just proofs, this has been going on for a little while now, but this is the first major problem that was solved.

So that is another, like a feather to put in OpenAI's cap.

Again, there's another caveat here, as Bloom was kind of emphasizing earlier,

This may have been a little bit of luck in the sense of this well-known problem happened to have a relatively easy counterexample that existed in the type of space that these type of tools are really good at, right?

Not that...

we now can solve all these type of open problems as shown now by the, the, you know, alpha proof nexus, which like is a, a, probably a higher power, better application of this type of thinking, much more automated, much more systematic.

And it could only solve nine of the 353 similar style of problems they pointed at, right?

So again, most problems,

for whatever reason, are unsolvable.

You know, a lot of them because they're actually literally unsolvable, and a lot of them because the types of solutions don't fall into that style of solution space where these tools are well-suited.

All right, so there are some caveats to that, but that's what's going on here.

All right, question number three.

Does this mean all equally hard challenges will now be conquered by AI?

I definitely got that sense on X. Mathematicians aren't saying this.