Demis Hassabis

And it sort of fits with the way I think about physics in general, which is that, you know, I think information is primary.

Information is the most sort of fundamental unit of the universe, more fundamental than energy and matter.

I think they can all be converted into each other.

But I think of the universe as a kind of informational system.

That's right.

Yeah, I think it's one of the most fundamental questions, actually, if you think of physics as informational.

And the answer to that, I think, is going to be very enlightening.

Yeah, I think that there are actually a huge class of problems that could be couched in this way, the way we did AlphaGo and the way we did AlphaFold, where you model what the dynamics of the system is, the properties of that system, the environment that you're trying to understand, and then that makes the search for the solution or the prediction of the next step efficient.

basically polynomial time, so tractable by a classical system, which a neural network is.

It runs on normal computers, classical computers, Turing machines in effect.

And I think it's one of the most interesting questions there is, is how far can that paradigm go?

I think we've proven, and the AI community in general, that classical systems, Turing machines, can go a lot further than we previously thought.

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.