DeepMind’s AI has beaten chess grandmasters and Go champions. But founder and CEO Demis Hassabis now has his sights set on bigger, real-world problems that could change lives. First up: protein folding.
Demis Hassabis – former child chess prodigy, recipient of a double first at the University of Cambridge, five times World Mind Sports Olympiad champion, MIT and Harvard alumnus, games designer, teenage entrepreneur, and co-founder of the artificial intelligence startup DeepMind – is dressed in a yellow helmet, a hi-viz jacket and worker’s boots. Raising his hand to shield his eyes, he gazes across London from a rooftop in King’s Cross. The view is largely uninterrupted in every direction across the capital, which is bathed in spring sunshine. Hassabis crosses the paved roof and, having used his phone to determine the direction, scans his eyes northwards to see if he can see Finchley, where he spent his childhood. The suburb is lost behind trees on Hampstead Heath, but he is able to make out the incline leading to Highgate, where he now lives with his family.
He is here to inspect what will be the new headquarters of DeepMind, the startup he founded in 2010 with Shane Legg, a fellow researcher at University College London, and childhood friend Mustafa Suleyman. Currently the building is a construction site, ringing with the relentless percussion of hammering, drilling and grinding – there are 180 contractors on-site today and this number will rise to 500 at the peak of the build. Due to open in mid 2020, the site represents, literally and figuratively, a new beginning for the company.
“Our first office was on Russell Square, a little ten-person office at the top of a townhouse next to the London Mathematical Society,” Hassabis recalls, “which is where Turing gave his famous lectures.” Alan Turing, the British pioneer of computing, is a totemic figure for Hassabis. “We’re building on the shoulders of giants,” Hassabis says, mentioning other pivotal scientific figures – Leonardo da Vinci, John von Neumann – who have made dramatic breakthroughs.
The location of the new headquarters – north of King’s Cross railway station in what has recently become known as the Knowledge Quarter – is telling. DeepMind was founded at a time when the majority of London startups submitted to the gravitational influence of Old Street. But Hassabis and his co-founders had a different vision: to “solve intelligence” and develop AGI (artificial general intelligence) – AI that can be applied in multiple domains. Thus far, this has been pursued largely through building algorithms that are able to win games – Breakout, chess and Go. The next steps are to apply this to scientific endeavour in order to crack complex problems in chemistry, physics and biology using computer science.
“We’re a research-heavy company,” Hassabis, 43, says. “We wanted to be near the university,” by which he means UCL – University College London – where he was awarded a PhD for his thesis, The Neural Processes Underpinning Episodic Memory. “That’s why we like being here, we’re still near UCL, the British Library, the Turing Institute, not far from Imperial…”
A few floors down, Hassabis inspects one of the areas that he’s most excited about, which will house a lecture theatre. With contentment he considers blue prints and renderings of what the space will look like.
Towards the north-east corner of the building he peers into a large void encompassing three floors, which will house the library. The space will eventually contain the feature that Hassabis seems most eager to see in its fully realised form: a grand staircase shaped like a double helix, which is in the process of being manufactured in sections. “I wanted to remind people of science and to make it part of the building,” he says.
Hassabis and his co-founders are aware that DeepMind is best known for its breakthroughs in machine learning and deep learning that have resulted in highly publicised events in which neural networks combined with algorithms have mastered computer games, beaten chess grandmasters and caused Lee Sedol, the world champion of Go – widely agreed to be the most complex game man has created – to declare: “From the beginning of the game, there was not a moment in time when I thought that I was winning.”
In the past, machines playing games against humans demonstrated characteristics that made the algorithm apparent: the style of play was relentless and rigid. But in the Go challenge, the DeepMind algorithm AlphaGo beat Sodol in a way that appeared to have human characteristics. One outlandish move – number 37 in game two – drew gasps from the live audience in Seoul and baffled millions watching online. The algorithm was playing with a freedom that, to human eyes, might be considered creative.
For Hassabis, Suleyman and Legg, if the first nine years of DeepMind have been defined by proving its research into reinforcement learning – the idea of agent-based systems that are not only trying to make models of their world and recognise patterns (as deep learning does) but also actively making decisions and trying to reach goals – then the proof points offered by gameplay will define the next ten years: namely, to use data and machine learning to solve some of the hardest problems in science. According to Hassabis, the next steps for the company will be based on how deep learning can enable reinforcement learning to scale to real-world problems.
“The problem with reinforcement was it was always working on toy problems, little grid worlds,” he says. “It was thought what maybe this can’t scale to messy, real-world problems – and that’s where the combination really comes in.”
For DeepMind, the emergence of the new headquarters is symbolic of a new chapter for the company as it turns its research heft and compute power to try to understand, among other things, the building blocks of organic life. In so doing, the company hopes to make breakthroughs in medicine and other disciplines that will significantly impact progress in a number of fields. “Our mission should be one of the most fascinating journeys in science,” Hassabis says. “We’re trying to build a cathedral to scientific endeavour.”
To be continue
Source: WiredRelated posts: