Demis Hassabis

So if that's true, then there should be some sort of pattern that you can kind of reverse learn and a kind of manifold really that helps you search to the right solution, to the right shape, and actually allow you to predict things about it in an efficient way, because it's not a random pattern.

Right.

So it may not be possible for man-made things or abstract things like factorizing large numbers, because unless there's patterns in the number space, which there might be, but if there's not and it's uniform, then there's no pattern to learn.

There's no model to learn that will help you search.

You have to do brute force.

So in that case, you know, you maybe need a quantum computer, something like this.

But in most things in nature that we're interested in are not like that.

They have structure that evolved for a reason and survived over time.

And if that's true, I think that's potentially learnable by a neural network.

Yes, right.

Yeah.

So they can be efficiently rediscovered or recovered because nature is not random, right?

Everything that we see around us, including like the elements that are more stable, all of those things, they're subject to some kind of selection process, pressure.

Yeah.

I mean, I've always been fascinated by the P equals NP question and what is modulable by classical systems, i.e.

non-quantum systems, you know, Turing machines in effect.

And that's exactly what I'm working on actually in kind of my few moments of spare time with a few colleagues about should there be, you know, maybe a new class of problem that is solvable by this type of neural network process and kind of mapped onto these natural systems.

So, you know, the things that exist in physics.

and have structure.

So I think that could be a very interesting new way of thinking about it.