Demis Hassabis

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.

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.

No expert system can do that because, of course, it's strictly limited by what we already know and can describe to the machine. But these systems can learn for themselves. And that's what resulted in Move 37 in Game 2 of the famous World Championship match, the challenge match we had against Lee Sedol in Seoul in 2016. And that was a truly creative move. Go has been played for thousands of years.

No expert system can do that because, of course, it's strictly limited by what we already know and can describe to the machine. But these systems can learn for themselves. And that's what resulted in Move 37 in Game 2 of the famous World Championship match, the challenge match we had against Lee Sedol in Seoul in 2016. And that was a truly creative move. Go has been played for thousands of years.

No expert system can do that because, of course, it's strictly limited by what we already know and can describe to the machine. But these systems can learn for themselves. And that's what resulted in Move 37 in Game 2 of the famous World Championship match, the challenge match we had against Lee Sedol in Seoul in 2016. And that was a truly creative move. Go has been played for thousands of years.

It's the oldest game humans have invented, and it's the most complex game. And it's been played professionally for hundreds of years in places like Japan. And even still, even despite all of that exploration by brilliant human players, This Move 37 was something never seen before. And actually, worse than that, it was thought to be a terrible strategy.

It's the oldest game humans have invented, and it's the most complex game. And it's been played professionally for hundreds of years in places like Japan. And even still, even despite all of that exploration by brilliant human players, This Move 37 was something never seen before. And actually, worse than that, it was thought to be a terrible strategy.

It's the oldest game humans have invented, and it's the most complex game. And it's been played professionally for hundreds of years in places like Japan. And even still, even despite all of that exploration by brilliant human players, This Move 37 was something never seen before. And actually, worse than that, it was thought to be a terrible strategy.

In fact, if you go and watch the documentary, which I recommend, it's on YouTube now, of AlphaGo, you'll see that the professional commentators nearly fell off their chairs when they saw Move 37 because they thought it was a mistake. They thought the computer operator, Adger, had misclicked on the computer because it was so unthinkable that someone would play that.

In fact, if you go and watch the documentary, which I recommend, it's on YouTube now, of AlphaGo, you'll see that the professional commentators nearly fell off their chairs when they saw Move 37 because they thought it was a mistake. They thought the computer operator, Adger, had misclicked on the computer because it was so unthinkable that someone would play that.

In fact, if you go and watch the documentary, which I recommend, it's on YouTube now, of AlphaGo, you'll see that the professional commentators nearly fell off their chairs when they saw Move 37 because they thought it was a mistake. They thought the computer operator, Adger, had misclicked on the computer because it was so unthinkable that someone would play that.

And then, of course, in the end, it turned out 100 moves later, that move 37, the stone, the piece that was put down on the board, was in exactly the right place to be decisive for the whole game. So now it's studied as a great classic game. of the, of the go, you know, history of go that game and that move.

And then, of course, in the end, it turned out 100 moves later, that move 37, the stone, the piece that was put down on the board, was in exactly the right place to be decisive for the whole game. So now it's studied as a great classic game. of the, of the go, you know, history of go that game and that move.

And then, of course, in the end, it turned out 100 moves later, that move 37, the stone, the piece that was put down on the board, was in exactly the right place to be decisive for the whole game. So now it's studied as a great classic game. of the, of the go, you know, history of go that game and that move.

And of course then, and then even more exciting for that is that's exactly what we hoped these systems would do because, um, the whole point of me and my whole motivation, my whole life of working on AI was to use AI to accelerate scientific discovery. And it's those kinds of new innovations, albeit in a game is what we were looking for from our systems.

And of course then, and then even more exciting for that is that's exactly what we hoped these systems would do because, um, the whole point of me and my whole motivation, my whole life of working on AI was to use AI to accelerate scientific discovery. And it's those kinds of new innovations, albeit in a game is what we were looking for from our systems.

And of course then, and then even more exciting for that is that's exactly what we hoped these systems would do because, um, the whole point of me and my whole motivation, my whole life of working on AI was to use AI to accelerate scientific discovery. And it's those kinds of new innovations, albeit in a game is what we were looking for from our systems.

Yeah, well, look, I think... I think there'll be a lot of move 37s in almost every area of human endeavor. Of course, the thing I've been focusing on since then is mostly being, how can we apply those types of AI techniques, those learning techniques, those general learning techniques to science, big areas of science. I call them root node problems.

Yeah, well, look, I think... I think there'll be a lot of move 37s in almost every area of human endeavor. Of course, the thing I've been focusing on since then is mostly being, how can we apply those types of AI techniques, those learning techniques, those general learning techniques to science, big areas of science. I call them root node problems.

Yeah, well, look, I think... I think there'll be a lot of move 37s in almost every area of human endeavor. Of course, the thing I've been focusing on since then is mostly being, how can we apply those types of AI techniques, those learning techniques, those general learning techniques to science, big areas of science. I call them root node problems.