Tim Scarfe: prediction

Transcript context

…Yeah. I think fun I think fundamentally taking a step back, the fact that our brains can do it and our brains are generally running similar algorithms, to me, this means that we will eventually be able to, inject general reasoning into the language models. I don't think there's a fundamental reason why, neural networks can't behave like biological neural networks. So that's, I guess, the higher the higher level point. And then, zooming in, right now, you know, the models are as bad as they're ever going to be. There, there's generally more compute going into pretraining than there is reinforcement learning, and of the compute going into reinforcement learning, a subset is going into specific general reasoning. And so I think that over time, you're going to see the models get better and better at general reasoning. But I guess a question I would have for you is do you think there's a fundamental difference between the way the brain works where there's some sort of symbolic nature to the brain and and it's not possible to inject that type of nature into an artificial network? 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.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.