and the churn of research and scholarly life would
get in the way of co-operation.
“Interdisciplinary research is hard,” Hassabis
says. “Say you get two world-leading experts, in
maths and genomics – there obviously could be
some crossover. But who is going to do the work to
understand the other person’s field, their jargon,
what their real problem is?”
Identifying the right question to ask, why that
question hasn’t been answered – and what, if it’s
not been answered, the tricky thing about it is –
may seem, to outsiders, relatively straightforward.
But scientists, even in the same discipline, don’t
always see their work in the same way. And it’s
notoriously hard for researchers to add value to
other disciplines. It’s even harder for researchers
to find a joint question that they might answer.
The current DeepMind headquarters – two
floors of Google’s King’s Cross building – has
become increasingly populous in the past couple
of years. There are six or seven disciplines repre-
sented in the company’s AI research alone, and
it has been hiring specialists in mathematics,
physics, neuroscience, psychology, biology and
philosophy as it broadens its remit.
“Some of the most interesting areas of science
are in the gaps between, the confluences between
subjects,” Hassabis says. “What I’ve tried to do in
building DeepMind is to find ‘glue people’, those who
are world class in multiple domains, who possess
the creativity to find analogies and points of contact
between different subjects. Generally speaking,
when that happens, the magic happens.”
One such glue person is Pushmeet Kohli. The
former director at Microsoft Research leads the
science team at DeepMind. There is much talk in
artificial intelligence circles of the “AI winter” – athat’s where the combination really comes in.”
For DeepMind, the new headquarters is symbolic
of a new chapter for the company as it turns its
research heft and compute power to try to under-
stand, 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. “Our mission
should be one of the most fascinating journeys
in science,” Hassabis says. “We’re trying to
build a cathedral to scientific endeavour.”hen studying at UCL and later at MIT,
Hassabis found that interdisciplinary collaboration
was a hot topic. He recalls that workshops would
be organised involving different faculties – neuro-
science, psychology, mathematics and philosophy.
There would be a couple of days of talks and debates
before the academics returned to their depart-
ments, vowing that they must gather more regularly
and find ways to collaborate. The next meeting
would be a year later – grant applications, teaching
period where there was little tangible progress –
having ended during the past decade. The same
sense of movement is now also true of protein
folding, the science of predicting the shape of what
biologists consider to be the building blocks of life.
Kohli has brought together a team of structural
biologists, machine-learning experts and physicists
in order to address this challenge, widely recognised
as one of the most important questions in science.
Proteins are fundamental to all mammalian life –
they make much of the structure and function of
tissues and organs at a molecular level. Each is
comprised of amino acids, which make up chains.
The sequence of these determines the shape of the
protein, which determines its function.
“Proteins are the most spectacular machines ever
created for moving atoms at the nanoscale and often
do chemistry orders of magnitude more efficiently
than anything that we’ve built,” says John Jumper,
a research scientist at DeepMind who specialises
in protein folding. “And they’re also somewhat
inscrutable – these are self–assembling machines.”
Proteins arrange atoms at the angstrom scale, a
unit of length one ten-billionth of a metre; a deeper
understanding would offer scientists a much more
substantial grasp of structural biology. For instance,
proteins are necessary for virtually every function
within a cell, and incorrectly folded proteins are
thought to be contributing factors to diseases such
as Parkinson’s, Alzheimer’s and diabetes.
“If we can learn about the proteins that nature
has made, we can learn to build our own,” Jumper
says. “It’s about getting a really concrete view into
this complex, microscopic world.”
What has made protein folding an attractive
puzzle for the DeepMind team has been the
widespread availability of genomic data sets. Since
2006, there has been an explosion in DNA data
acquisition, storage, distribution and analysis.
Researchers estimate that by 2025, two billion
genomic data sets may have been analysed,
requiring 40 exabytes of storage capacity.LEARNING LESSONS:
HOW TO TEACH AN AIMachine learning
Both deep and
reinforcement
learning are machine
learning functions
that use algorithms
to identify patterns
in data. Their
goal is to enable
computers to then
learn on their own.Reinforcement learning
An autonomous,
self-teaching system
that essentially
learns by trial and
error. DeepMind
created a program
that plays classic
Atari games such as
Breakout. It learned
a set of actions to
achieve the maximum
score – and then
beat human gamers.Deep learning
The most advanced
subset of machine
learning, it aims to
instil human-style
thinking in machines- so, a facial
recognition program
would map features
from a photograph,
and then compare them
to existing data.
A “ribbon diagram”
visualisation of a
protein’s backbone,
folded into a 3D
structure predicted
by the AlphaFold
algorithm for the
CASP13 competition.09-19-FTDeepmind.indd 110 23/07/2019 10:59