Talk a little bit about that work.

Yeah.

So this is, uh, I think very exciting in the sense that, um...

So the, the thing that, um---- So there, there's two reasons why we thought we needed to think more about waves in science.

Um, and that is because, first of all, waves are actually seen in the brain now because, um, we've gone from single electrode measurements to maybe hundreds or thousands of electrode measurements.

And of course, in a single electrode, you cannot see a moving wave, a traveling wave, but if you put hundreds or thousands in them, you can actually see these traveling waves, and people have now observed them everywhere.

Um, and then the question becomes, is this functional or is it just a side effect of something? Um, that's the first reason.

And so what is spontaneous symmetry breaking?

Well, and then speaking of chatbots and LLMs, what's your take on the concept of world models? And especially, I don't know if you've seen our podcast or, you know, what, what's your take on like Yann LeCun's thoughts on that, for example?

Yeah, I think, um, I'm a fan of world models.

I think it's important to put the priors that we know about the world into these models.

And I think there is, and this goes back to also what I said about embodiment.

So in some sense, you need to put in every piece of knowledge you have about the world it's kind of silly not to put it in in a way um because it really helps your reason about the world for instance the fact that the world is three-dimensional is it is a is a very simple piece of prior and information and the and the fact that the world has symmetries like if i rotate my head um the physics of the world don't change or um a millisecond after this moment, the world hasn't dramatically changed either.

So there's a kind of a slowness prior.

So there's all these facts about the world that we know.

Do you want to tell us a bit about it and why you actually

Do you have a, a thread through there?

Yeah.

I think physics is the thread.

So having done, you know, spent a lot of time in theoretical physics, I think there is f- first, very fundamental and exciting questions, like things that haven't actually been figured out in quantum gravity, so that's really the frontier.

There's also a lot of mathematical tools that you can use, right? In, for instance, in particle physics, but also in general relativity, sort of symmetry space, play an enormously important role, and this goes all the way to, to gauge symmetries as well.

And so applying these kinds of symmetries to, uh, machine learning was actually, you know...

I thought of it as a very deep and interesting mathematical problem, right? I did this with Taco Cohen, and, you know, Taco Cohen was the main driver behind this.

Can you talk a little bit about your upcoming book and tell the listeners, like, what's exciting about it? Why they should read it.