Demis Hassabis

They can do things like model the structures of proteins and play Go to better than world champion level.

And a lot of people would have thought maybe 10, 20 years ago, that was decades away, or maybe you would need some sort of quantum machines to quantum systems to be able to do things like protein folding.

And so I think we haven't really even sort of scratched the surface yet of what classical systems so-called could do.

And of course, AGI being built on a neural network system, on top of a neural network system, on top of a classical computer would be the ultimate expression of that.

And I think the limit, you know, what the bounds of that kind of system, what it can do, it's a very interesting question and directly speaks to the P equals NP question.

Yeah, I think those systems would be right on the boundary, right?

So I think most emergent systems, cellular automata, things like that could be modelable by a classical system.

You just sort of do a forward simulation of it and it'd probably be efficient enough.

Of course, there's the question of things like chaotic systems where the initial conditions really matter and then you get to some, you know, uncorrelated end state.

Now, those could be difficult to model.

So I think these are kind of the open questions.

But I think when you step back and look at what we've done with the systems and the problems that we've solved, and then you look at things like VO3 on like video generation, sort of rendering physics and lighting and things like that, you know, really core fundamental things in physics.

It's pretty interesting.

I think it's telling us something quite fundamental about how the universe is structured, in my opinion.

So, you know, in a way, that's what I want to build AGI for, is to help us as scientists answer these questions like P equals MP.

And so if there's one to follow and you can specify the objective function correctly, you know, you don't have to deal with all that complexity, which I think is how we maybe have naively thought about it for decades, those problems.

If you just enumerate all the possibilities, it looks totally intractable.

And there's many, many problems like that.

And then you think, well, it's like 10 to the 300 possible protein structures, 10 to the 170 possible go positions.

All of these are way more than atoms in the universe.