DeepMind and Demis Hassabis

Recent profile in the Guardian; 15 facts about Hassabis. The mastery of Atari games through reinforcement learning deep neural nets is described here (Nature). See also Deep Neural Nets and Go: AlphaGo beats European champion.

Guardian: ... “We’re really lucky,” says Hassabis, who compares his company to the Apollo programme and Manhattan Project for both the breathtaking scale of its ambition and the quality of the minds he is assembling at an ever increasing rate. “We are able to literally get the best scientists from each country each year. So we’ll have, say, the person that won the Physics Olympiad in Poland, the person who got the top maths PhD of the year in France. We’ve got more ideas than we’ve got researchers, but at the same time, there are more great people coming to our door than we can take on. So we’re in a very fortunate position. The only limitation is how many people we can absorb without damaging the culture.”

That culture goes much deeper than beanbags, free snacks and rooftop beers. Insisting that the Google acquisition has not in any way forced him to deviate from his own research path, Hassabis reckons he spends “at least as much time thinking about the efficiency of DeepMind as the algorithms“ and describes the company as “a blend of the best of academia with the most exciting start-ups, which have this incredible energy and buzz that fuels creativity and progress.” He mentions “creativity” a lot, and observes that although his formal training has all been in the sciences, he is “naturally on the creative or intuitive” side. “I’m not, sort of, a standard scientist,” he remarks, apparently without irony. Vital to the fabric of DeepMind are what he calls his “glue minds”: fellow polymaths who can sufficiently grasp myriad scientific areas to “find the join points and quickly identify where promising interdisciplinary connections might be, in a sort of left-field way.” Applying the right benchmarks, these glue people can then check in on working groups every few weeks and swiftly, flexibly, move around resources and engineers where required. “So you’ll have one incredible, genius researcher and almost immediately, unlike in academia, three or four other people from a different area can pick up that baton and add to it with their own brilliance,” he describes. “That can result in incredible results happening very quickly.” The AlphaGo project, launched just 18 months ago, is a perfect case in point.

... “just thinking time. Until three or four in the morning, that’s when I do my thinking: on research, on our next challenge, or I’ll write up an algorithmic design document.” ... It’s not so much actual AI coding, he admits, “because my maths is too rusty now.  [ Quel dommage! ]  It’s more about intuitive thinking. Or maybe strategic thinking about the company: how to scale it and manage that. Or it might just be something I read in an article or saw on the news that day, wondering how our research could connect to that.”

See also Don’t Worry, Smart Machines Will Take Us With Them: Why human intelligence and AI will co-evolve.