Is credit assignment the key to the next level of positive tech disruption
The momentum behind AI continues – we’ve written about a dozen pieces on AI in the past 12 months but the real breakthrough came as Google’s AI machine played, and won against the world Go champion this week – it wasn’t brute force but in the nuances. . . .
From the Verge: “DeepMind’s stunning victories over Go legend Lee Se-dol have stoked excitement over artificial intelligence’s potential more than any event in recent memory. But the Google subsidiary’s AlphaGo program is far from its only project — it’s not even the main one. As co-founder Demis Hassabis said earlier in the week, DeepMind wants to “solve intelligence,” and he has more than a few ideas about how to get there.
Hassabis himself has had an unusual path to this point, but one that makes perfect sense in retrospect. A child chess prodigy who won the Pentamind championship at the Mind Sports Olympiad five times, he rose to fame at a young age with UK computer games developers Bullfrog and Lionhead, working on AI-heavy games like Theme Park and Black & White, and later forming his own studio, Elixir. Hassabis then left the games industry in the mid-2000s to complete a PhD in neuroscience before co-founding DeepMind in 2010.
Sitting down with The Verge early in the morning after AlphaGo’s first triumph over Lee Se-dol, Hassabis could have been forgiven if media engagements were the last thing on his mind. But he was warm and convivial as he entered the room, commenting on the Four Seasons Seoul’s gleaming decor and looking visibly amazed when a Google representative told him that over 3,300 articles had been written about him in Korean overnight. “It’s just unbelievable, right?” he said. “It’s quite fun to see something that’s a bit esoteric being that popular.”
Beyond AlphaGo, our conversation touched on video games, next-gen smartphone assistants, DeepMind’s role within Google, robotics, how AI could help scientific research, and more. Dive in – it’s deep.
This interview has been lightly edited for clarity.
“Go has always been a holy grail for AI research.”
Sam Byford: So for someone who doesn’t know a lot about AI or Go, how would you characterize the cultural resonance of what happened yesterday?
Demis Hassabis: There are several things I’d say about that. Go has always been the pinnacle of perfect information games. It’s way more complicated than chess in terms of possibility, so it’s always been a bit of a holy grail or grand challenge for AI research, especially since Deep Blue. And you know, we hadn’t got that far with it, even though there’d been a lot of efforts. Monte Carlo tree search was a big innovation ten years ago, but I think what we’ve done with AlphaGo is introduce with the neural networks this aspect of intuition, if you want to call it that, and that’s really the thing that separates out top Go players: their intuition. I was quite surprised that even on the live commentary Michael Redmond was having difficulty counting out the game, and he’s a 9-dan pro! And that just shows you how hard it is to write a valuation function for Go.
Were you surprised by any of the specific moves that you saw AlphaGo play?
Yeah. We were pretty shocked — and I think Lee Se-dol was too, from his facial expression — by the one where AlphaGo waded into the left deep into Lee’s territory. I think that was quite an unexpected move.
Because of the aggression?
Well, the aggression and the audacity! Also, it played Lee Se-dol at his own game. He’s famed for creative fighting and that’s what he delivered, and we were sort of expecting something like that. The beginning of the game he just started fights across the whole board with nothing really settled. And traditionally Go programs are very poor at that kind of game. They’re not bad at local calculations but they’re quite poor when you need whole board vision.
A big reason for holding these matches in the first place was to evaluate AlphaGo’s capabilities, win or lose. What did you learn from last night?
Well, I guess we learned that we’re further along the line than — well, not than we expected, but as far as we’d hoped, let’s say. We were telling people that we thought the match was 50-50. I think that’s still probably right; anything could still happen from here and I know Lee’s going to come back with a different strategy today. So I think it’s going to be really interesting to find out.
Just talking about the significance for AI, to finish your first question, the other big thing you’ve heard me talk about is the difference between this and Deep Blue. So Deep Blue is a hand-crafted program where the programmers distilled the information from chess grandmasters into specific rules and heuristics, whereas we’ve imbued AlphaGo with the ability to learn and then it’s learnt it through practice and study, which is much more human-like.
If the series continues this way with AlphaGo winning, what’s next — is there potential for another AI-vs-game showdown in the future?
“Ultimately we want to apply this to big real-world problems.”
I think for perfect information games, Go is the pinnacle. Certainly there are still other top Go players to play. There are other games — no-limit poker is very difficult, multiplayer has its challenges because it’s an imperfect information game. And then there are obviously all sorts of video games that humans play way better than computers, like StarCraft is another big game in Korea as well. Strategy games require a high level of strategic capability in an imperfect information world — “partially observed,” it’s called. The thing about Go is obviously you can see everything on the board, so that makes it slightly easier for computers.
Is beating StarCraft something that you would personally be interested in?
Maybe. We’re only interested in things to the extent that they are on the main track of our research program. So the aim of DeepMind is not just to beat games, fun and exciting though that is. And personally you know, I love games, I used to write computer games. But it’s to the extent that they’re useful as a testbed, a platform for trying to write our algorithmic ideas and testing out how far they scale and how well they do and it’s just a very efficient way of doing that. Ultimately we want to apply this to big real-world problems.
I grew up in the UK in the late ‘90s and would see your name in PC magazines, associated with very ambitious games. And when I first started hearing about DeepMind and saw your name there I thought, “That kind of fits.” Can you draw a line from your previous career in the games industry to what you do now?
Yeah, so something like DeepMind was always my ultimate goal. I’d been planning it for more than 20 years, in a way. If you view all the things I’ve done through a prism of eventually starting an AI effort, then it kind of makes sense what I chose to do. If you’re familiar with my stuff at Bullfrog and so on, you’ll know that AI was a core part of everything I wrote and was involved with, and obviously Peter Molyneux’s games are all AI games as well. Working on Theme Park when I was 16 or 17 years old was quite a seminal moment for me in terms of realizing how powerful AI could be if we really tried to extend it. We sold millions of copies, and so many people enjoyed playing that game, and it was because of the AI that adapted to the way you played. We took that forward and I tried to extend that for the rest of my games career, and then I switched out of that back to academia and neuroscience because I felt around the mid-2000s that we’d gone as far as we could trying to sneak in AI research through the back door while you’re actually supposed to be making a game. And that’s hard to do, because publishers just want the game, right?
Was it just that games of the era were the most obvious application of AI?” more….