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…Yeah. I mean, I wanted to challenge you on this a tiny bit. Right? So you kind of said in in your I think it was in the second version of of the blog post that you just released last week that at the moment, the models can do domain specific thinking. So they can do math thinking and they can do code thinking. And what we want to do is is imbue, like, the core machinations of thinking into these models. And I'm a little bit skeptical. I feel that these models, because they're not Turing complete, because they're not symbolic, you know, similar to what Francois believes that I'm sure you read that LLM biology paper as well. They were talking about these circuits that we can find in papers that do things like multiplication and and and addition. And what we what we saw was that they are quite patterned. They're quite templated. They're not thinking in a very general sense. And my suspicion is it will always be that way because the models don't have semantics. They're non symbolic and and so on. Do you think we could ever make them truly think in a general way? 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.…

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