[nybsjytm]

I agree that the result is important regardless. But the tradeoff of computing time/cost with problem complexity is hugely important to think about. Finding a proof in a formal language is trivially solvable in theory since you just have to search through possible proofs until you find one ending with the desired statement. The whole practical question is how much time it takes.

Three days per problem is, by many standards, a 'reasonable' amount of time. However there are still unanswered questions, notably that 'three days' is not really meaningful in and of itself. How parallelized was the computation; what was the hardware capacity? And how optimized is AlphaProof for IMO-type problems (problems which, among other things, all have short solutions using elementary tools)? These are standard kinds of critical questions to ask.

[dash2]

Though, if you start solving problems that humans can't or haven't solved, then questions of capacity won't matter much. A speedup in the movement of the maths frontier would be worth many power stations.

[nybsjytm]

For some time a 'superhuman math AI' could be useful for company advertising and getting the attention of VCs. But eventually it would be pretty clear that innovative math research, with vanishingly few exceptions, isn't very useful for making revenue. (I am a mathematician and this is meant with nothing but respect for math research.)

[empath75]

The big exception being predicting market movements, and I can't imagine how much money the hedge funds are spending on this right now.

[airstrike]

"Making revenue" is far from being the only metric by which we deem something worthy.

[nybsjytm]

As a mathematician, of course I agree. But in a sentence like:

> A speedup in the movement of the maths frontier would be worth many power stations

who is it 'worth' it to? And to what end? I can say with some confidence that many (likely most, albeit certainly not all) mathematicians do not want data centers and power stations to guzzle energy and do their math for them. It's largely a vision imposed from without by Silicon Valley and Google research teams. What do they want it for and why is it (at least for now) "worth" it to them?

Personally, I don't believe for a second that they want it for the good of the mathematical community. Of course, a few of their individual researchers might have their own personal and altruistic motivations; however I don't think this is so relevant.

[auggierose]

There is something called applied math, and there is a big gulf between applied math and pure math. This new technology has the potential of making use of much more "pure math" for applied math, unifying much of pure math, applied math, and programming. I don't really care about the RH, I care about that.

[auggierose]

> Finding a proof in a formal language is trivially solvable in theory since you just have to search through possible proofs until you find one ending with the desired statement. The whole practical question is how much time it takes.

No, in my experience the whole practical question is, can it be found automatically, or can it not be found automatically? Because there is an exponential search space that conventional automated methods will not be able to chew through, it either works, or it doesn't. AlphaProof shows that for some difficult IMO problems, it works.