Demis Hassabis: What happens is, you know, if you think about — it’s a massive combinatorial problem. Like you’ve got lots and lots of possibilities. So you’ve got all these molecules that you could put together to create a new compound, which would create a new drug. And then you’ve got this complex new virus that you’ve just discovered, and you need to build a compound that will attach to it, right? So, really, the problem is that we know quite a lot about physics; we know a lot about chemistry and a lot about biology; but the combinations of possibilities is huge, right? It’s astronomical, the number of compounds you can combine together. And you would need to test that — each one — laboriously. Maybe you’d have some theories about things that could work, but you wouldn’t know for sure, as a scientist. What the kinds of systems we’re building — like AlphaGo and AlphaZero, our new systems — is you can think of them as optimization programs. You give them some goal to optimize, whether that’s efficacy with a drug or winning a game, and they figure out for themselves the right search path through this massive combinatorial space. And they do that by having to be able to experience many — look at much more data than any human could comprehend — and do that far more quickly. So these systems can find patterns that are there in the data that we, just as human scientists or human experts or human medical practitioners, wouldn’t have the bandwidth to do.