Y Combinator Startup Podcast
00:27:40 · Francois
[00:27:40] S4: I think it's the the mappings to humans will get more and more like this, where, like, the the the optimal thing to do may be to to put a new s a r, state action reward, back into context. It's very quick. But then there needs to be some go to sleep for a while. Almost everything that is intelligent sleeps. Like, tell me an intelligent system that doesn't sleep in some way. And then during sleep, compression happens. The weird thing that happens from your hippocampus and it shortwave ripples to both lobes and like there's weird, you know, paths from memories that were compressed throughout the day to train the weights. And similarly, maybe what the right thing to do is similar to DAgger, data segregation framework in our classic RL, where you're you're going, you're collecting a bunch of data, and then you're somewhat reflecting on it, and then you're using it to update your wait file. And then you have distillation in... From those experiences back into an updated wait file that maybe isn't Astra, but maybe is your own models.
00:28:36 · Vincent, S0
[00:28:36] S3: It sounds very DreamCoder Mhmm. Esque. Mhmm. But a lot of those specific tools, I think, that people used to build... To take DreamCoder, for example. Right? It had this library of skills, and then during the sleep phase, it basically refactored everything into more compact representation and so on. I mean, we're seeing sort of similar things just happen not as rigidly as before in the space of programs, but now it's maybe, like, maybe you wanna refactor traces. Maybe you want to refactor skills. A lot of these robotic coders policy papers, right, they have this growing library of skills, and it's anyone's guess really how you print them, how you organize them, and things like that. So, yeah, I think as as Waddle goes forward, like, how you manage our growing context of skills of deployment data, all of that is going to be quite interesting. [00:29:19] S0: I think with that, I think this was an excellent discussion. Thank you so much, Vincent, Han Ming, and Jay for being here. I'm very excited for all the incredible robotics advancements I think, we're gonna see over the next few years, and I think you're totally right, Jay, that I don't think broader society is totally aware of how much is coming. And it's gonna be a very incredible few years to come. So thank you so much.