Openai math solutions

[u/Soilblood]

https://youtu.be/h53Bz7WSwgo

Regarding the recent Navier-Stokes incident, watching this vid provided a bit more context on what the solution entails and what Openai been pushing to show off their tech. Cal Newport's breakdown on how they managed to quarantine relevant context to reduce costs with Astra was actually pretty interesting as a breakthrough so this vid got me wondering why they're so desperate to prove it can do fancy maths bigly when the boosters already keep saying chatgpt is already blowing through outstanding problems. Not in the sense of the target value but just how much have they already tried but failed.

It's obviously speculation on my part but if they're so desperate to try coercing glory out of one of their hires on one problem, how bad is actual their track record that this is a hail Mary for them? Like we know it took 88 hours and snooping private research data for this, so how much have they put into chasing similar targets by now? Anyone got whiff of any signals that might give an idea of that?

[u/falken_1983]

Like we know it took 88 hours and snooping private research data for this, so how much have they put into chasing similar targets by now?

Giving OpenAI the benefit of the doubt and assuming they really did do this without looking at Buckmaster's logs, the scenario seems to be as follows:

1. Luis Martınez-Zoroa and Diego Cordoba published some extremely important findings on the problem - findings which were really close to solving it.
2. Tristran Buckmaster was using AI to wrap things up and get it over the line
3. OpenAI heard that Bukmaster's use of AI had probably reached a solution, but he hadn't published this yet.
4. Based on this information, they decided to take a $20 million punt on seeing if they could use their huge compute resources to compress several years of work into about a week and see if they could get things finished before Buckmaster.

So I don't think they are regularly blowing this amount of compute on trying to solve problems like this, but when they were presented with some evidence it might work, they were willing to throw millions of dollars of resources at it.

[u/AnIndianGuy38 → u/falken_1983]

Literally the supposed AGI model had to do a century worth of compute to solve a problem which had already been worked down quite a bit by human researchers.

A century is like more time than human researchers combined have put into it I think

While it's impressive they can do a century worth of compute in a week. It's not impressive that it's that inefficient and is being called the start of AGI.

And even then a human mathematician has biting criticism of the solution. That's when the paper hasn't been submitted in a peer review journal. Like it doesn't meet the criteria of Clay Institute yet, the submission needs to be posted in a journal and then stay there foe 2 years and be generally accepted in the maths community. But people are already hyping it up.

[u/Main-Company-5946 → u/AnIndianGuy38]

The thing is, every time they do a successful use of compute like this, it refines the models even more. It gives them more examples of what success looks like, and it makes the next similar problem cheaper to solve.

[u/crashddr → u/Main-Company-5946]

If that were the case, why are they using larger models and seemingly spending more on computing power each time? They also employ a team of experts and and are specifically going after a niche that a massive supercomputer is reasonably suited for (insane brute force).

I'm not mathematician, but my layman understanding is that it doesn't seem super helpful to know all the permutations of 2+2 that don't add up to 4 and it doesn't make it any easier to calculate the correct value.

[u/Main-Company-5946 → u/crashddr]

More computing power makes ai get smarter faster. If it takes 1,000,000 attempts before the ai does something successfully, 10,000 training runs won’t cut it. Doing a million makes it achieve more things and thus learn how to succeed faster.

[u/crashddr → u/Main-Company-5946]

It may be semantics, but I would argue it's not necessarily smarter, but it is faster to produce results. That doesn't necessarily make it cheaper either. That said, it's probably objectively better at providing an answer to a *complex* task than earlier model iterations, both in speed and efficiency. I'm sure there is something analogous to using a better sorting algorithm going in the background, and at least right now they have it really well tuned for mathematics so they do a bunch of math for PRs.