Solve Intelligence; Use That to Solve Everything Else

Source: The Guardian (UK), Feb 2016

Demis Hassabis … is on a mission to “solve intelligence, and then use that to solve everything else”.

This is artificial general intelligence (AGI), with the emphasis on “general”. In his vision of the future, super-smart machines will work in tandem with human experts to potentially solve anything. “Cancer, climate change, energy, genomics, macroeconomics, financial systems, physics: many of the systems we would like to master are getting so complex,” he argues. “There’s such an information overload that it’s becoming difficult for even the smartest humans to master it in their lifetimes. How do we sift through this deluge of data to find the right insights? One way of thinking of AGI is as a process that will automatically convert unstructured information into actionable knowledge. What we’re working on is potentially a meta-solution to any problem.”

In DeepQ they combined deep neural networks with “reinforcement-learning”, which is the way that all animals learn, via the brain’s dopamine-driven reward system. With AlphaGo, they went one step further and added another, deeper level of reinforcement learning that deals with long-term planning. Next up, they’ll integrate, for example, a memory function, and so on – until, theoretically, every intelligence milestone is in place. “We have an idea on our road map of how many of these capabilities there are,” Hassabis says. “Combining all these different areas is key, because we’re interested in algorithms that can use their learning from one domain and apply that knowledge to a new domain.”

Born in north London in 1976 to a Greek-Cypriot father and Singaporean-Chinese mother, he is the eldest of three siblings.

The smartest people on the planet are queuing up to work here, and the retention rate is, so far, a remarkable 100%, despite the accelerating focus on AI among many of Google’s biggest competitors, not to mention leading universities all over the globe.

“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.”

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.

He adds, reflectively: “It’s true though, I don’t have much of a normal life. Every waking moment, this is what I’m thinking about, probably in my dreams as well. Because it’s so exciting, it’s so important, and it’s the thing I’m most passionate about.”

There is a look in his eyes of what I can only describe as radiant purpose, almost childlike in its innocence. “I feel so lucky. I can’t think of more interesting questions than the ones I’m working on, and I get to think about them every day. Every single moment I’m doing something I really believe in. Otherwise, why do it, given how short life is?”


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