10 Years of AlphaGo: The Turning Point for AI | Thore Graepel & Pushmeet Kohli

**Hannah Fry** (0:00)
Welcome back to Google DeepMind the Podcast. I'm Professor Hannah Fry. Picture this scene. It's March 2016 Inside a hotel suite in Seoul, South Korea, two players are playing the ancient game of Go, a game of unimaginable complexity, long thought impossible for a machine to master. On one side is Lisa Dole, a legendary 18-time Go world champion. On the other, AlphaGo, a neural network-based AI system, built on a powerful technique called reinforcement learning.

**SPEAKER_2** (0:35)
Welcome to the DeepMind Challenge live in Seoul, Korea.

**Thore Graepel** (0:43)
That's a very surprising move.

**SPEAKER_4** (0:45)
Not a single human player would have chosen move 37

**Hannah Fry** (0:50)
After hours of intense gameplay spread over seven days.

**SPEAKER_2** (0:54)
Yeah, that's an exciting move.

**Hannah Fry** (0:56)
Lisa Dole placed two stones on the board to signal his final resignation. And in the blink of an eye, the world changed.

**SPEAKER_2** (1:05)
The final result of 4-1. Congratulations to AlphaGo and to the entire team.

**Hannah Fry** (1:14)
That was exactly one decade ago, and the field of AI has changed unimaginably since then. We have seen the rise of large language models, the growing sophistication of AI agents, and the solving of scientific grand challenges like protein folding. But in many ways, the modern AI revolution arguably began right there on that wooden board in South Korea. So in this episode, we wanted to look backwards and forwards to how a bold experiment in teaching machines to play games became the foundation stone for the AI breakthroughs of today.
And with me are the perfect guests to tell that story. Thore Graepel is a distinguished research scientist at Google DeepMind, who was right there in Seoul as a key architect of the AlphaGo project. And Pushmeet Kohli, who leads Google DeepMind science work, and is the person to tell us how those early techniques pioneered in Go can tackle crucial problems today. Welcome to the podcast, both of you. Thore, I know you're an accomplished Go player yourself. Just explain to us why Go was seen as a good challenge for AI.

**Thore Graepel** (2:24)
Yes, the game of Go seemed like the perfect challenge for AI, because the game has such simple rules, yet it leads to such complex gameplay with tactics and strategies and complex patterns. And once the game of chess had been solved, as it were, or at least Deep Blue had won against the world champion, then Go was this open challenge. It's much more complex than chess by many orders of magnitude, and nobody was expecting it to be solved anytime soon. Yet it looks so elegant and simple for computer scientists, and so it was the perfect game to tackle at the time.

**Hannah Fry** (3:12)
I mean, that idea of nobody thinking it would be solved anytime soon, that sort of hits the nail on the head, right, Pushmeet? I know you were working at Microsoft at the time, but just how complex was this problem considered to be?

**Pushmeet Kohli** (3:24)
I think it was considered extremely complex, and that is because not only because of the breadth of the search space, of the number of moves you can make, but also the depth, how long you have to reason and how long the games are. In the game of chess, you might think about reasoning about 60 to 70 moves in the game of Go, it's much, much longer. And that leads to the challenge of the problem.

**Hannah Fry** (3:50)
Thore, I know when you first started at DeepMind, being a Go player, didn't you play against AlphaGo on your first day?

**Thore Graepel** (3:58)
Yeah, yeah, exactly. So imagine I come first day at work at DeepMind, I know a couple of people, including David Silver, and he asks me, Thore, you're a Go player, right? Couldn't you do us a favor and test our baby version of something that wasn't even called AlphaGo at the time, of course? You know, it was an internship project, and they had just about taken a few thousand games from the internet and had trained a system, or a few hundred thousand games maybe. And I had the opportunity to be one of the first people to play against it. But you can imagine, I was excited, but I was also nervous. It was my first day at work, and there I was being dragged to a centrally located table. On the other side, I think it was Aja Huang, who would later be known as the hand of AlphaGo, with his poker face. And I got to play against this baby version of AlphaGo.

**Hannah Fry** (5:03)
With people watching, presumably.

**Thore Graepel** (5:04)