Demis Hassabis

And that's learning from the data you give it, any data you have available.

But also, in a lot of cases with biology and chemistry, there isn't enough data to learn from.

So you also have to build in some of the rules about chemistry and physics that you already know about.

So, for example, with AlphaFold, the angle of bonds between atoms.

And make sure that AlphaFold understood you couldn't have atoms overlapping with each other and things like that.

Now, in theory, it could learn that, but it would waste a lot of the learning capacity.

So actually, it's better to kind of have that as a constraint in there.

Now, the trick is, with all hybrid systems, and AlphaGo was another hybrid system where there's a neural network learning about the game of Go and what kind of patterns are good.

And then we had Monte Carlo Tree Search on top, which was doing the planning.

And so the trick is, how do you marry up

a learning system with a more handcrafted system, bespoke system, and actually have them work well together.

And that's pretty tricky to do.

what you want to do is when you figure out something where there's one of these hybrid systems, what you ultimately want to do is upstream it into the learning component.

So it's always better if you can do end-to-end learning and directly predict the thing that you're after from the data that you're given.

So once you've figured out something using one of these hybrid systems, you then try and go back and reverse engineer what you've done and see if you can incorporate that learning, that information into the learning system.

And this is sort of what we did with AlphaZero, the more general form of AlphaGo.

So AlphaGo had some Go-specific knowledge in it.

But then with AlphaZero, we got rid of that, including the human data, human games that we learned from, and actually just did self-learning from scratch.

And of course,

then it was able to learn any game, not just Go.