Tim Scarfe: preference

Transcript context

…Yeah. I also believe that using a system like Shinkai Evolve could be able to sort of automatically detect whether or not like an instruction based optimization approach for a given problem or a transform based approach is actually the right thing to do. And sometimes potentially, it's like even the mixture. Right? There's some things you can probably easier even articulate in Python than you can articulate in in sort of language. Right? So I would be really interested in sort of exploring that. Yeah. I mean, you said earlier about Jeff's clean what what was Jeff clean's paper? The the thing that generates capability discovery. I did speak to him about this at Neuros, but something like that could be fascinating as well. You know, where we're also generating the problems and solutions and then kind of moving them back in. But I I I think the way this will land commercially is there'll be a new type of GPT where everyone is solving different types of problems and and the system, it'll be like a kind of Cinca Revolve but a massively distributed version where mathematicians are using the platform over here to solve this problem and it will see commonalities and and it will kind of like link them together. Because you need to leverage like human creativity in this process as well, I think. Like a big challenge going forward is going to be like how do we change our incentive system for this to actually scale. Right? I think like for example, some amount of economy will be needed or some amount of mechanism design in order to make sure that everyone is still happy to engage in it. Right? So maybe we're gonna have many more leaderboards for whatever is numerically sort of scorable. And I think this this will be really, really interesting to see how sort of compute these automated agents, human shepherding and steering will ultimately sort of change and revolutionize science and, I guess, society more generally.…

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