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 work 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, “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 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.
“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 near UCL, the British
Library, the Turing Institute, not far from Imperial...”
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, one
that remains central to it’s future: 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.
A few floors down, Hassabis inspects one of
the areas that he’s most excited about, which will
house a lecture theatre, studying blueprints 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 Sodol, the world champion of Go – widely
agreed to be the most complex game humans have
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 that maybe this
can’t scale to messy, real-world problems – and
‘ Theproblemwithreinforce-ment wasit wasalwaysworkingon toyproblems,and maybecouldn’tscale’Praveen Srinivasan, head of DeepMind for Google;
Drew Purves, creative lead, Worlds; and Raia Hadsell,
research scientist, in DeepMind’s unfinished HQ>09-19-FTDeepmind.indd 108 23/07/2019 10:59