Jeff Beck: recommendation

Transcript context

…like, relatively small toy grid world y type models. And part of the reason for that is that, you know, it is in fact difficult to scale Bayesian methods. Now that also has now begun to change, right? We now have a lot of great mathematical tools and a lot of great frameworks for approximating Bayesian inference. You'll never do it exactly. We're approximating Bayesian inference, which I believe is how the brain works, right? Bayesian brain and all that. That allows us to build these kind of structured models that are structured both after the brain, how the brain is structured and how the the world that we live in is actually structured. Hence the the the this notion that what we need to build, get the net to the next layer of of AGI, and I also don't like that term and don't intend to use it very often. But what we need to get to next level right is is is this is this framework that allows us to build the kinds of models that we know people actually use and just make them bigger and more sophisticated and and and and so on. And then take advantage like hyper scaling Bayesian inference is part of it, but also like it's, you know, constructing models of the world as it actually works. The way the world actually works, right, is what is it is it it, you know, provides us with the structure of our own thinking. Right? The atomic elements of thought is how I like to phrase it. Our models of the physical world in which we live. And the physical world in which we live is a world of macroscopic objects that, you know, that have specific relations and interact in certain ways that we understand. Right? You know, looking around the room for a good example. Right? You sit on a chair. Right? That's an example of a relationship. It holds you up and all that fun stuff. And those are the kinds of you know, that understanding of the physical world was necessary for us in order you know, for us to have in order to survive. Dogs have it too. Right? It's language isn't what make you know, it it isn't isn't all that special. Right? Well, it's it's actually quite special. But, those are the models that form that that understanding of the world in which we live is where we get our the models that form the the the the the the models that form the atomic elements of our thoughts out of which we have composed more sophisticated models that have allowed us to do all this great systems engineering, build this great technology that we've got. So that's what we wanna do. Right? Is we wanna is is we're focused on building cognitively inspired models that are based on our understand on on the way the world in which we live actually works because we believe intelligence must be embodied. Building a framework for for putting those models together and experimenting with them at scale, all in an approximately Bayesian way because we believe that's how the brain works. It's not just about putting your AI into a robot. It's about giving that giving the robot a model of the world that is like our model of the world. A model that is object centered. It's dynamic. It's it's largely causal. Right? It's you know, that's that's that's the big difference. robot a model of the world that is like our model of the world. A model that is object centered. It's dynamic. It's it's largely causal. Right? It's you know, that's that's that's the big difference. And I think that the the the sort of sparse structured models is another sort of key differentiating component. Like when you think about how like a transformer and LLM work, a transformer takes every word in the document and says, now how does this word relate to every other word? It does it many many many many times. Right? It's a it it, you know, it's it's very much word. It's the same thing with like your your your generative vision language action models. They operate in pixel space. They are microscopic models. Now, yes. Do they have an implicit notion of sort of macroscopic? Yes. They must because they work. Right? But it's implicit and it's not implemented with the kind of sparse structure that actually exists in the real world and in our conceptualization of it. And that's the thing that we are gonna that we are we're saying, no, no. Look. Like, we want an AI that thinks like us, right, then we are gonna build models that are structured both like the real world is structured. They have this sparse causal macroscopic structure to it. And so should our models and so should and and and the only way to do that is not just to like put a robot in the real world, but to put a robot with a model that is structured in that fashion into the real world. No one's using the XLSTM. Not many people are using Mamba because why not? All you need to do is just scale the transformer as much as possible. So many people just really think you just magically get these things for free, right?…

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