Demis Hassabis

So clearly that it must be possible with evolutionary systems to generate new patterns, going back to the first thing we talked about, and new capabilities and emergent properties.

And maybe we're on the cusp of discovering how to do that.

Yeah, and it's amazing, which is a relatively simple algorithm, right, effectively, and it can generate all of this immense complexity.

emerges, obviously running over 4 billion years of time.

But you can think about that as, again, a search process that ran over the physics substrate of the universe for a long amount of computational time, but then it generated all this incredible rich diversity.

Yeah.

I think that's going to be one of the hardest things to mimic or model is this idea of taste or judgment.

I think that's what separates the great scientists from the good scientists.

All professional scientists are good technically, right?

Otherwise they wouldn't have made it that far in academia and things like that.

But then do you have the taste to sort of sniff out what the right direction is, what the right experiment is, what the right question is?

So picking the right question is the hardest part of science and making the right hypothesis.

And that's what today's systems definitely they can't do.

So, you know, I often say it's harder to come up with a conjecture, a really good conjecture than it is to solve it.

So we may have systems soon that can solve pretty hard conjectures.

You know, I am in maths Olympiad problems where we, you know, alpha proof last year, our system got, you know, silver medal in that really hard problems.

Maybe eventually we'll better solve a millennium price kind of problem.

But could a system come up?

with a conjecture worthy of study that someone like Terence Tao would have gone, you know what, that's a really deep question about the nature of maths or the nature of numbers or the nature of physics.

And that is far harder type of creativity.