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David R Bell's avatar

The foundation labs pursuing math problems was purely a commercial flex. LLM's are well suited to the sequential nature of math proofs and they had the tools to turn agents loose in a massive brute force approach. They found the solutions to some specific math problems, because they trained the systems on math problems. All it shows is that they can be good a math problems if you focus on it. The bigger question is not unsolved problems can it solve, what are the problems that are worth solving at all? Don't be misled by the headlines--I doubt if what the did with the NS problem is generalizable beyond a very narrow field that they put a lot of effort into.

Lost in Lindisfarne's avatar

One thing I’m thinking - is this just a transition to ‘big math’?

We already have big science in fundamental physics (cern), most of astronomy, biology (human genome project, alphafold, etc) and so on.

In all these fields there is still room for the lone genius. But the big shiny objects attract a lot of people and funding and they realize the only way to tackle the problems is as a team.

Math has largely managed to slip through, until now.

One way to view navier stokes is OpenAI stole the idea and scooped the two geniuses working on it.

Another way to view it is hundreds of engineers and business people and construction teams worked for years and spent hundreds of billions of dollars to solve the problem.

These big shiny theorems seem destined to be the territory of big math going forward. And big math will likely suck up many mathematicians into its vortex.

But there may still be a lot of room for mathematicians to work in a new, but perhaps still somewhat traditional, way, in neglected, but possibly very interesting corners.

That’s one thing I’m thinking.

But the other thing I’m thinking is that there is clearly something lost. And I feel bad for mathematicians. And these systems are only getting better. And they are coming for me next.

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