Demis Hassabis

So that gave us confidence that if we pushed hard enough in this direction, even though no one was really doing that, that eventually this should work, right? Because we have the existence proof of the human mind. And of course, that's why I also studied neuroscience, because when you're in the desert, like you say, you need any source of water or any evidence that you might get out of the desert.

There's even a mirage in the distance. is a useful thing to understand in terms of giving you some direction when you're in the midst of that desert. And of course, AI was itself in the midst of that because several times this had failed. The expert system approach basically had reached a ceiling.

There's even a mirage in the distance. is a useful thing to understand in terms of giving you some direction when you're in the midst of that desert. And of course, AI was itself in the midst of that because several times this had failed. The expert system approach basically had reached a ceiling.

There's even a mirage in the distance. is a useful thing to understand in terms of giving you some direction when you're in the midst of that desert. And of course, AI was itself in the midst of that because several times this had failed. The expert system approach basically had reached a ceiling.

Well, look, the reason Go was considered to be and ended up being so much harder than chess, so it took another 20 years, even us with AlphaGo, and all the approaches that have been taken with chess, these expert systems approaches had failed with Go, right? Basically couldn't even be a professional, let alone a world champion. And the reason was two main reasons.

Well, look, the reason Go was considered to be and ended up being so much harder than chess, so it took another 20 years, even us with AlphaGo, and all the approaches that have been taken with chess, these expert systems approaches had failed with Go, right? Basically couldn't even be a professional, let alone a world champion. And the reason was two main reasons.

Well, look, the reason Go was considered to be and ended up being so much harder than chess, so it took another 20 years, even us with AlphaGo, and all the approaches that have been taken with chess, these expert systems approaches had failed with Go, right? Basically couldn't even be a professional, let alone a world champion. And the reason was two main reasons.

One is the complexity of Go is so enormous. One way to measure that is there are 10 to the power 170 possible positions, far more than atoms in the universe. There's no way you can brute force a solution to Go. It's impossible. But even harder than that is that it's such a beautiful, esoteric, elegant game. It's considered art. an art form in Asia, really, right?

One is the complexity of Go is so enormous. One way to measure that is there are 10 to the power 170 possible positions, far more than atoms in the universe. There's no way you can brute force a solution to Go. It's impossible. But even harder than that is that it's such a beautiful, esoteric, elegant game. It's considered art. an art form in Asia, really, right?

One is the complexity of Go is so enormous. One way to measure that is there are 10 to the power 170 possible positions, far more than atoms in the universe. There's no way you can brute force a solution to Go. It's impossible. But even harder than that is that it's such a beautiful, esoteric, elegant game. It's considered art. an art form in Asia, really, right?

And it's because it's both beautiful aesthetically, but also it's all about patterns rather than sort of brute calculation, which chess is more about. And so even the best players in the world can't really describe to you very clearly what are the heuristics they're using. They just kind of intuitively feel the right moves, right?

And it's because it's both beautiful aesthetically, but also it's all about patterns rather than sort of brute calculation, which chess is more about. And so even the best players in the world can't really describe to you very clearly what are the heuristics they're using. They just kind of intuitively feel the right moves, right?

And it's because it's both beautiful aesthetically, but also it's all about patterns rather than sort of brute calculation, which chess is more about. And so even the best players in the world can't really describe to you very clearly what are the heuristics they're using. They just kind of intuitively feel the right moves, right?

They'll sometimes just say that this move, why did you play this move? Well, it felt right, right? And then it turns out their intuition, if they're a brilliant player, their intuition is brilliant, fantastic. And it's an amazingly beautiful and effective move. But that's very difficult then to encapsulate in a set of heuristics and rules that to direct how a machine should play go.

They'll sometimes just say that this move, why did you play this move? Well, it felt right, right? And then it turns out their intuition, if they're a brilliant player, their intuition is brilliant, fantastic. And it's an amazingly beautiful and effective move. But that's very difficult then to encapsulate in a set of heuristics and rules that to direct how a machine should play go.

They'll sometimes just say that this move, why did you play this move? Well, it felt right, right? And then it turns out their intuition, if they're a brilliant player, their intuition is brilliant, fantastic. And it's an amazingly beautiful and effective move. But that's very difficult then to encapsulate in a set of heuristics and rules that to direct how a machine should play go.

And so that's why all of these kind of deep blue methods didn't work. Now, we got around that by having the system learn for itself what are good patterns, what are good moves, what are good motifs and approaches, and what are kind of valuable and high probability of winning positions are.

And so that's why all of these kind of deep blue methods didn't work. Now, we got around that by having the system learn for itself what are good patterns, what are good moves, what are good motifs and approaches, and what are kind of valuable and high probability of winning positions are.

And so that's why all of these kind of deep blue methods didn't work. Now, we got around that by having the system learn for itself what are good patterns, what are good moves, what are good motifs and approaches, and what are kind of valuable and high probability of winning positions are.

So it kind of learned that for itself through experience, through seeing millions of games and playing millions of games against itself. So that's how we got AlphaGo to be better than world champion level. But the additional exciting thing about that is that it means those kinds of systems can actually go beyond what we as the programmers or the system designers know how to do.