DeepMind founder Demis Hassabis on how AI will shape the future


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.

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.

Read the source article at The Verge