Demis Hassabis

And so that's how you can go beyond potentially what is already known.

So the model can model everything that you currently know about, right?

All the data that you currently have, but then how do you go beyond that?

So that starts to speak about the ideas of creativity.

How can these systems create something new, discover something new,

Obviously, this is super relevant for scientific discovery or pushing science and medicine forward, which we want to do with these systems.

And you can actually bolt on some fairly simple search systems on top of these models and get you into a new region of space.

Of course, you also have to make sure that you're not searching that space totally randomly.

It would be too big.

So you have to have some objective function that you're trying to optimize and hill climb towards and that guides that search.

Yeah, exactly.

So you can get a bit of an extra property out of evolutionary systems, which is some new emergent capability may come about.

Of course, like happened with life.

Interestingly, with naive sort of traditional evolutionary computing methods without LLMs and the modern AI,

The problem with them, they were very well studied in the 90s and early 2000s and some promising results.

But the problem was they could never work out how to evolve new properties, new emergent properties.

You always had a sort of subset of the properties that you put into the system.

But maybe if we combine them with these foundation models, perhaps we can overcome that limitation.

Obviously, natural evolution clearly did because it did evolve new capabilities, right?

So bacteria to where we are now.