Source: FT, Mar 2017

With its cadre of researchers, from Bayesian mathematicians to cognitive neuroscientists, statisticians and computer scientists, DeepMind has amassed arguably the most formidable community of world-leading academics specialising in machine intelligence anywhere in the world.

“What we are trying to do is a unique cultural hybrid — the focus and energy you get from start-ups with the kind of blue-sky thinking you get from academia,” says Demis Hassabis, co-founder and chief executive. “We’ve hired 250 of the world’s best scientists, so obviously they’re here to let their creativity run riot, and we try and create an environment that’s perfect for that.”

DeepMind’s researchers have in common a clearly defined if lofty mission: to crack human intelligence and recreate it artificially.

Today, the goal is not just to create a powerful AI to play games better than a human professional, but to use that knowledge “for large-scale social impact”, says DeepMind’s other co-founder, Mustafa Suleyman, a former conflict-resolution negotiator at the UN.

To solve seemingly intractable problems in healthcare, scientific research or energy, it is not enough just to assemble scores of scientists in a building; they have to be untethered from the mundanities of a regular job — funding, administration, short-term deadlines — and left to experiment freely and without fear.

“If you look at how Google worked five or six years ago, [its research] was very product-related and relatively short-term, and it was considered to be a strength,” Hassabis says. “[But] if you’re interested in advancing the research as fast as possible, then you need to give [scientists] the space to make the decisions based on what they think is right for research, not for whatever kind of product demand has just come in.”

DeepMind’s three appearances in quick succession in Nature, along with more than 120 papers published and presented at cutting-edge scientific conferences, are a mark of its prodigious scientific productivity.

Our research team today is insulated from any short-term pushes or pulls, whether it be internally at Google or externally. We want to have a big impact on the world, but our research has to be protected,” Hassabis says. “We showed that you can make a lot of advances using this kind of culture. I think Google took notice of that and they’re shifting more towards this kind of longer-term research.”

DeepMind has six more early manuscripts that it hopes will be published by Nature, or by that other most highly regarded scientific journal, Science, within the next year. “We may publish better than most academic labs, but our aim is not to produce a Nature paper,” Hassabis says. “We concentrate on cracking very specific problems. What I tell people here is that it should be a natural side-effect of doing great science.”

Structurally, DeepMind’s researchers are organised into four main groups with titles such as “Neuroscience” or “Frontiers” (a group comprising mostly physicists and mathematicians who test the most futuristic theories in AI).

Every eight weeks, scientists present what they have achieved to team leaders, including Hassabis and Shane Legg, head of research, who decide how to allocate resources to the dozens of projects. “It’s sort of a bubbling cauldron of ideas, and exploration, and testing things out, and finding out what seems to be working and why — or why not,” Legg says.

Projects that are progressing rapidly are allocated more manpower and time, while others may be closed down, all in a matter of weeks. “In academia you’d have to wait for a couple of years for a new grant cycle, but we can be very quick about switching resources,” Hassabis says.

This organisational culture has been a magnet for some of the world’s brightest minds. Jane Wang, a cognitive neuroscientist at DeepMind, used to be a postdoctoral researcher at Northwestern University in Chicago, and says that she was attracted to DeepMind’s clear, social mission. “I have interviewed at other industry labs, but DeepMind is different in that there isn’t pressure to patent or come up with products — there is no issue with the bottom line. The mission here is about being curious,” she says.

For Matt Botvinick, neuroscience team lead, joining DeepMind was not just a career choice but a lifestyle change too. The former professor who led Princeton University’s Neuroscience Institute continues to live in the US, where his wife is a practising physician, and commutes to DeepMind’s labs in London every other week. “At Princeton, I was surrounded by people I considered utterly brilliant and had no interest in working in an environment any less focused on primary scientific questions,” he says. “But I couldn’t resist the opportunity to come here because there is something qualitatively new going on, both with the scale and the spirit of ideas.”

What sets DeepMind apart from academic labs, he says, is its culture of cross-disciplinary collaboration, reflected in the company’s hiring of experts, who can cut across different domains from psychology to deep learning, physics or computer programming.

“In a lot of research institutions, things can become siloed. Two neighbouring labs could be working on similar topics but never exchange and pool information,” Botvinick says. “Unlike any place I’ve ever experienced before, all conversations are enhanced rather than undermined by differences in background.”


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