Speaker unverified: prediction

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…Yes. Yeah. I mean, you mentioned Jeff Hawkins. I I interviewed Jeff. He's absolutely amazing. And of course, his HTM algorithm is computationally stronger than a neural network. It's it's Turing complete. And our brains, even though they are finite, they run a Turing complete algorithm, which means our brains know how to expand their memory. Right? Our memory. We can go and write things on a whiteboard and we can go and, you know, get another notebook. And that is a special type of algorithm which is not traversable with stochastic gradient descent. So, you know, the the the rough argument is, yes, there is there is a difference there. And I also wanted to touch on this, you know, RL with verifiable rewards thing, which is that we do that at training time. I'm very excited in the future about an active inference version of that, like an agentic version where we're actually doing this kind of transductive active fine tuning in an agential way. Right? So, you know, I I take an action. I get some new information from the environment, and I update my weights. And that would be truly adaptive. That would be intelligent. But what we do now is we do all of this stuff at training time, and the resulting frozen artifact is still an LLM. It still has just a bunch of patterns in there. And I think that while that can uplift reasoning in many ways, I don't think it it has the intelligence. And and according to Charle, intelligence is simply the ability to search through the space of Turing programs. Right? And I don't think that's what's happening with these LLMs at the moment. I I think you're generally correct that it's not happening at the moment, but I still think, like, fundamentally, I don't think there is a, like, a fundamental blocker physically for why they won't be able to do it in the future. And it's possible that SGD, right, like stochastic gradient descent, is is an issue fundamentally, and I think we're going to overcome that. I guess what I would say is artificial neural networks, I think, have the structure, capable of, yeah, basically, being as smart in every way as a human brain. And I I subscribe to, Francois' definition of of general intelligence as well. Yeah. I mean, I I think we mostly agree. I I think I mean, you know, let's look at alpha 0 or mu 0 or something like that. They did this training loop where they were actually updating the, you know, like, the the value network and the policy network. And then it was frozen, and they did some kind of, you know, Monte Carlo tree search. So they were achieving adaptivity through exhaustive search during the actual games. And in an ideal world, we would have this adaptivity that's actually updating the weights. Now I believe the only reason we can't do that at the moment is just computational tractability. Right? We have these huge models. We couldn't possibly have a a dynamically updating model for every single person that's using ChatGPT. It it would it would just be ridiculously slow. But I think you and I agree that if that were possible, that would be an entirely different kind of form of intelligence.…

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