“All I needed was to sacrifice my queen,” he says. “This was his last roll of the dice. He’d been trying for hours to outmanoeuvre me. And that was his final cheap trick. And it worked. Basically, I had nothing to show for 12 hours of slog.” Hassabis recalls that, at that moment, he had an epiphany: he questioned the purpose of the brilliant minds in the room competing with each other to win a zero-sum game.
For a research company, DeepMind is big on project management. Every six months, senior managers examine priorities, reorganise some projects, and encourage teams – especially engineers – to move between endeavours. Mixing of disciplines is routine and intentional. Many of the company’s projects take longer than six months – generally in the range of two to four years. But, as much as DeepMind’s messaging is consistently around its research, it is now a subsidiary of Alphabet, Google’s parent company and the world’s fourth most valuable company. While the expectation from the academics in London is that they are involved in long-term, ground-breaking research, executives in Mountain View, California, will naturally have an eye on ROI – return on investment.
“We care about products in the sense that we want Google and Alphabet to be successful and to get benefit out of the research we’re doing – and they do, there are dozens of products now with DeepMind code and technology in them all around Google and Alphabet – but the important thing is that it’s got to be a push, not a pull,” Hassabis says. DeepMind for Google, led by Suleyman, comprises about one hundred people, mostly engineers who translate the company’s pure research into applications that are productised. For example, WaveNet, a generative text-to-speech model that mimics the human voice is now embedded in most Google devices from Android to Google Home, and has its own product team within Google.
“A lot of research in industry is product led,” Hassabis says. “The problem with that is that you can only get incremental research. [That’s] not conducive to doing ambitious, risky research, which, of course, is what you need if you want to make big breakthroughs.”
In conversation, Hassabis talks rapidly, often punctuating the end of a sentence with the interrogative “right?”, guiding the listener through a sequence of observations. He makes frequent, lengthy digressions into various tributaries – philosophy (Kant and Spinoza are favourites), history, gaming, psychology, literature, chess, engineering and multiple other scientific and computational domains – but doesn’t lose sight of his original thought, often returning to clarify a remark or reflect on an earlier comment.
Much like the 300-year vision of Masayoshi Son, the founder of SoftBank – the Japanese multinational with large stakes in many of the world’s dominant technology companies –Hassabis and the other founders have a “multi-decade roadmap” for DeepMind. Legg, the company chief scientist, still has a hard copy of the initial business plan circulated to potential investors. (Hassabis has lost his.) Legg occasionally reveals it at all-hands meetings to demonstrate that many of the approaches the founders were thinking about in 2010 – learning, deep learning, reinforcement learning, using simulations, ideas of concepts and transfer learning, and using neuroscience, memory and imagination – are still core parts of its research programme.
During its infancy, DeepMind had a single web page featuring just the company logo. There was no address, no phone number, no jaunty “about us” information. To make hires, the founders had to rely on personal contacts for people who already knew they were “serious people and serious scientists and had a serious plan”, as Hassabis puts it.
“With any startup, you’re really asking people to trust you as management,” he says. “But [with DeepMind] it’s even more because you’re basically saying you’re going to do this in a completely unique way that no one’s ever done before, and a lot of traditional, top scientists would have said was impossible: ‘You just cannot organise science in this fashion.’”
How scientific breakthroughs occur is as unknown as some of the problems that researchers are trying to solve. In academia, great minds are gathered together in institutions to undertake research that’s iterative, often with uncertain outcomes. Progress is usually painstaking and slow. Yet, in the private sector, supposedly free of restraint and with access to highly compensated management consultants, productivity and innovation are also declining.
In February 2019, Stanford economist Nicholas Bloom published a paper demonstrating declining productivity in a wide-ranging number of sectors. “Research effort is rising substantially while research productivity is declining sharply,” Bloom wrote. “A good example is Moore’s Law. The number of researchers required today to achieve the famous doubling every two years of the density of computer chips is more than 18 times larger than the number required in the early 1970s. Across a broad range of case studies at various levels of (dis)aggregation, we find that ideas – and in particular the exponential growth they imply – are getting harder and harder to find.”
Hassabis mentions the billions invested into research by Big Pharma: driven by quarterly earnings reports, the industry has become more conservative as the costs of failure have risen. According to a report by innovation foundation Nesta in 2018, over the past 50 years biomedical R&D productivity has steadily fallen – despite significant increases in public and private investment, new drugs cost much more to develop. According to the report, “the exponentially increasing cost of developing new drugs is directly reflected in low rates of return on R&D spending. A recent estimate puts this rate of return at 3.2 per cent for the world’s biggest drug companies; substantially less than their cost of capital.” Similarly, research from Deloitte estimated that R&D returns in biopharma had declined to their lowest rate in nine years, from 10.1 per cent in 2010, to 1.9 per cent in 2018.
“If you look at the CEOs of most of the big pharma companies, they’re not scientists, they come from the finance department, or the marketing department,” Hassabis says. “What does that say about the organisation? It means that what they’re going to do is try and squeeze more out of what has already been invented, cut costs or market better, not really invent new things – which is much more risky. You can’t put that down so easily in a spreadsheet. That’s not the nature of blue sky thinking… that’s not how you do it if you’re trying to land the rocket on the moon.”
For many startup founders, there is a degree of serendipity to their mission – a problem they’ve encountered that they decided to solve, a chance encounter with a co-founder or investor, an academic advocate. This is not the case for Hassabis, who has purposefully made a series of decisions – some very early in life – that would lead to DeepMind. “It’s what I spent my whole life preparing for,” he says. “From games design to games playing to neuroscience to programming, to studying AI in my undergrad, to going to a lot of the world’s top institutes, doing a PhD as well as running a start-up in my earlier career… I’ve tried to use every scrap of experience. I’ve consciously picked each of those decision points to gather that piece of experience.”
Add to that list being a CEO, which is now his day job. He has another role – that of researcher – and, in order to do both, he structures his time into distinct periods so that he can balance the running of the business with his academic interests. Having played the role of executive during the day, he returns home around 7.30pm to have dinner with his young family before embarking on a “second day” around 10.30pm, which will generally end around 4.00am to 4.30am.
“I love that time,” he says. “I’ve always been a nocturnal person, since I was a kid. Everything is quiet in the city and the house and I find it very conducive to thinking, reading, writing these kinds of things. So that’s when I mostly keep up to speed with the scientific literature. Or maybe I’ll be writing or editing a paper, or thinking up some new algorithmic idea, or thinking about something strategic, or be investigating some area of science that AI could be applied to.”
He listens to music when he works. The nature of the music – from classical to drum and bass – depends “on the emotion I’m trying to evoke in myself. It depends on whether I’m trying to be focused or inspired.” There are a couple of rules: there can be no vocals, otherwise he will try and listen to the lyrics; and there needs to be a level of acquaintance with the music. “It needs to be something I’m familiar with, but not too familiar with. And it can’t be a new piece of music because that is too disturbing for the brain. You’ve got to break a tune in and then you can use it.”
Hassabis says that he would like to spend 50 per cent of his time on direct research. As part of this, in April 2018, he hired Lila Ibrahim, a Silicon Valley veteran who spent 18 years at Intel before becoming Chief of Staff at Kleiner, Caulfield, Perkins and Byers – one of the most established venture capital firms in the Valley – before moving to the startup Coursera. Ibrahim is taking on many of Hassabis’s managerial tasks – he says his direct reports have dropped from 20 people to six. Ibrahim describes her decision to join DeepMind as “a moral calling,” prompted by conversations she had with Hassabis and Legg regarding the establishment of its Ethics & Society initiative, which is attempting to establish standards around the application of the technology.
“I think being based in London it brings a slightly different perspective, she says. “What would have happened if DeepMind had been headquartered in Silicon Valley would have been a very different, I think. London feels like there’s so much more humanity… the art, the cultural diversity. There’s also what the founders brought in from the start and the type of people who choose to work at DeepMind brought in certain ways of doing things, a mindset.”
One incident perhaps offers insight into the approach Ibrahim describes. Hassabis was a chess prodigy. Starting at the age of four, he rose up the rankings until, when 11, he found himself competing against a Danish master at a big, international competition in the town hall of a village outside Liechtenstein.
After playing for close to twelve hours, the endgame approached. It was a scenario that Hassabis had never seen before – he had a queen, while his opponent had a rook, bishop and knight, but it was still possible for Hassabis to force a draw if he could keep his opponent’s king in check. Hours passed, the other games ended and the hall emptied. Suddenly, Hassabis realised that his king had been trapped, meaning that check mate would be forced. Hassabis resigned.
“I was really tired,” he says. “We were 12 hours in or something and I thought somehow I must have made a mistake and he’s trapped me.”
His opponent – a man Hassabis recalls being in his 30s or 40s – stood up. His friends were standing around him and he laughed and gestured at the board. Hassabis realised that he had resigned unnecessarily – the game should have been a draw.
“All I needed was to sacrifice my queen,” he says. “This was his last roll of the dice. He’d been trying for hours to outmanoeuvre me. And that was his final cheap trick. And it worked. Basically, I had nothing to show for 12 hours of slog.”
Hassabis recalls that, at that moment, he had an epiphany: he questioned the purpose of the brilliant minds in the room competing with each other to win a zero-sum game. He would go on to play the game at the highest level, captaining his university team, and still talks of his continued love of complex games, but the experience led to him channeling his energy into something beyond games. “The reason that I could not become a professional chess player, he says. “Is that it didn’t feel productive enough somehow.”
Even as the company expands into its new headquarters, Hassabis maintains that DeepMind is still a startup, albeit one that is competing on a world stage – “China is mobilised and the US… there are serious companies trying to do these things,” he says. Indeed, the US and the China are both positioning themselves to standardise the field to their own advantage, both commercially and geopolitically. He mentions several times that, despite having made progress (“small stepping stones on the way”), there is still a long way to go in DeepMind’s bigger mission of solving intelligence and building AGI. “I still want us to have that hunger and the pace and the energy that the best startups have,” he says.
Innovation is hard and often singular. Building the processes and culture of an organisation that will enable it to “make a dent in the universe,” as Steve Jobs told the team building the Macintosh computer – and doing so in multiple fields or with more than one product – is something that few companies or institutions achieve. As DeepMind grows, it will be the role of the founders to pursue the road ahead, while keeping an eye on the founding principles of a business focused on what is likely to be the most transformative technology of the coming years, one fraught with possible dangers, as well as opportunities.
“You’re going to hit a lot of rough days and I think, at the end of the day, trying to make money or whatever isn’t going to be enough to get you through the real pain points,” Hassabis says. “If you have a real passion and you think what you’re doing is really important, then I feel like that will carry you through.”Related posts: