Demis Hassabis

I mean, fluid dynamics, Navier-Stokes equations, these are traditionally thought of as very, very difficult, intractable kind of problems to do on classical systems.

They take enormous amounts of compute, you know, weather prediction systems, you know, these kind of things all involve fluid dynamics calculations.

And, but again, if you look at something like VO, our video generation model, it can model liquids quite well, surprisingly well.

And materials, specular lighting.

I love the ones where, you know, there's people who generate videos where there's like clear liquids going through hydraulic presses and then it's being squeezed out.

I used to write...

physics engines and graphics engines in my early days in gaming.

And I know it's just so painstakingly hard to build programs that can do that.

And yet somehow these systems are reverse engineering from just watching YouTube videos.

So presumably what's happening is it's extracting some underlying structure around how these materials behave.

So perhaps there is some kind of lower dimensional manifold that can be learned if we actually fully understood what's going on under the hood.

That's maybe true of most of reality.

to the extent that it can predict the next frames in a coherent way, that is a form of understanding, right?

Not in the anthropomorphic version of, it's not some kind of deep philosophical understanding of what's going on.

I don't think these systems have that.

But they certainly have modeled enough of the dynamics, you know, put it that way, that they can pretty accurately generate whatever it is, eight seconds of consistent video that by eye, at least, you know, at a glance, it's quite hard to distinguish what the issues are.

And imagine that in two or three more years time.

That's the thing I'm thinking about and how incredible that they will look.

given where we've come from, you know, the early versions of that one or two years ago.

And so the rate of progress is incredible.