This year's Nobel Prize in chemistry went to three scientists,
David Baker, John Jumper and Demis Hasabes, for their
groundbreaking work using artificial intelligence to advance biomedical
and protein research. The AI model they developed,
called alphafold, uses databases with hundreds of thousands
of protein structures and millions of protein sequences to
predict and even design protein structures, speeding up
a months or years long process to mere hours or
even minutes. Alphafold was rolled out just four
years ago and has since been cited in scientific studies more than
20,000 times. Joining us now from London is
Demis Hasabes, co founder and CEO of Google
DeepMind and recipient of the Nobel Prize in Chemistry.
Demis, congratulations and welcome.
Thanks so much. Great to be here. So, first and foremost, what did you
think when you heard the news? Well, I was totally
stunned, to be honest, and it still hasn't really sunk in, even now,
24 hours later. So it just feels very surreal. So I'm going to
try here in simplest terms, which is not simple at all, but to
condense the work you and your colleagues have done. Basically,
you and your colleague John jumper discovered new and powerful ways to not only
decode, but also design proteins using artificial intelligence.
I'm not even going to try to understand the details of your work, but in
terms of application, how could this impact future development
of things like medicine and vaccines? What does it mean?
Well, proteins are the kind of building blocks of life, really. So everything,
all the functions that own your body are kind of supported
by proteins, and it's really important to understand their structure, their 3d
structure, so that you can understand what the function is they have. And so
that's what alphafold, our program does. It predicts that 3d structure
just from the genetic sequence, and it's going to be really important for things
like drug discovery and understanding disease. There's one
scientist who reacted to the news of your award by calling your work
the holy grail in terms of what it's been able to do. Do you agree
with that characterization? Well, it's very nice of them to say
so. I mean, it's certainly a grand challenge. I mean, the thing,
the reason that I sort of, it kind of caught my attention
is it's been a kind of grand challenge of biology for the last 50 years.
So people have been predicting since the seventies that this should be possible,
but until now, no one has been able to do it to an accuracy high
enough that it's useful for biologists and medical
experts. So just in your background, you co founded
DeepMind back in 2010, but before that, I was interested to read,
you designed video games. Before that, you were a chess
prodigy. You were once even ranked the second highest player in the
world under the age of 14. How did those endeavors,
chess and gaming, feed into this work today?
Well, there's actually. There's a connection all the way through my career, even though I've
done different things. So it's because of gaming and chess specifically,
that I got started to think about thinking, and I
was trying to improve my own thought processes, as you do when
you're playing chess for the junior teams and the national junior teams
and things like that. And I was playing it very professionally. And part of
that training, you also used chess computers, very early chess computers in the eighties,
there were actually physical blocks of plastic that you had to press the keys on.
And I was sort of intrigued by the fact that someone had
programmed this inanimate object to actually play
chess and play chess well. And that's what got me into AI.
And then I studied neuroscience as well as computer science, tried to understand
better how our own brains work and how intelligence is produced
and the mechanisms behind it. And then finally, that all comes together
with the work we've been doing on AI. As you well know, one of your
fellow laureates this year as a man named Jeffrey Hinton,
who's often called the godfather of AI, he resigned from
Google last year, and he's really been sounding the alarm on what
he says are the potential dangers here, that,
as he puts it, he worries that the overall consequences of this might be
systems that are more intelligent than us, that might eventually
take control. Do you share that concern?
You know, I've known Jeff, we've been colleagues for many, many years,
and he's a fantastic scientist, and I
think that my view is sort of more
moderate than that. I feel like, of course, I worked my
whole life on AI because I think it's going to be unbelievably beneficial
to humanity and to society. Alphafold is just,
I think, the first expression of that, and I think
we can go on next to try and cure the many terrible diseases.
I think it can help with climate crisis, new materials,
new energy sources, new mathematics. I think AI is going to accelerate
scientific discovery, medical discovery across the board.
So those are just some of the benefits I think AI is going to bring
and why I've worked my whole life on AI. So it's going to
be this hugely transformative technology. But as with any new powerful
technology, and perhaps AI will be the most powerful,
that comes with risks, attendant risks as well, and unknowns
and some of those are to do with controllability, understanding these
systems, interpreting what they do and how to manage
the, you know, what are the values we want these systems to have, what do
we want to use them for, how do we want to deploy them? And some
of these questions are technical in nature, technical challenges and others are more societal
and need discussion with the whole of society, civil society, academia as
well as the tech companies and industrial labs and also government.
And I've been encouraging all of those debates to happen and it's
great that it's starting to see those things happen and I think given
enough time and effort we will solve these challenges.
I'm a big believer in human ingenuity, but we need to
start discussing and researching those things now. That is Demis
Hasabas, co founder and CEO of Google DeepMind and recipient
of this year's Nobel Prize in Chemistry. Demis,
thank you and congratulations again. Thank you very much.