What sparked your interest in science and AI? 

Demis Hassabis: My interest in science and AI started with games. I started to play chess from a very young age. I was actually four years old when I learned, and I took it very seriously. I was captaining all the England chess junior teams as I was growing up. That’s how I actually came across computers for the first time because on some of the chess training camps you would use chess computers, physical boards, to try and train and get better at. As we were playing against these chess computers, I remember being fascinated by the fact that someone had managed to program this inanimate lump of plastic to actually play chess. So although I was meant to be training my chess openings and things, I actually got more fascinated by the program and the AI underneath it. 

I ended up wanting to program my own AI systems because of that. That’s how I got into computers and then AI. Later on, the reason I spent my whole career on AI is because I believe it could be the ultimate tool to help with science. To use AI systems to find patterns in data, insights in data and structure, and then help us advance scientific knowledge. My aim was, as always, to build AI algorithms that were general enough to eventually be able to be applied to scientific problems like protein folding. 

What inspired you to move into biology? 

Demis Hassabis: I think I’ve thought of biology as an information processing system at a fundamental level. Biology is a hugely complex, emergent dynamic system. I’ve always believed it’s quite hard to describe that system just with a few mathematical equations, so it’s not like physics. Actually, I think AI systems and AI algorithms are quite a good match, a good description language, for biology. I’ve always thought biology would be one of the places where we could apply AI most usefully and then specifically proteins and protein folding. I came across protein folding problem as an undergraduate at Cambridge for the first time in the nineties. I thought it was a fascinating problem, kind of like a puzzle really. A puzzle that if one could solve it, you would unlock so many new avenues of research and important research and discovery, like drug discovery and disease understanding. So it was a fundamental problem, and it was a really interesting puzzle, and I thought it would be very amenable to AI techniques. 

How has your interest in gaming shaped your career? 

Demis Hassabis: I’ve used gaming. I mean, games has been a passion of mine and a core part of my entire life. I think I’ve used gaming at least in three different ways. First of all, to sort of train my own mind when I was a kid. If you play chess and things like that very seriously, it becomes very formative with the way that you think about the world and formative of how I approach problems. So that was the first step. The second one was actually writing AI for computer games. That was my first career – designing AI for commercial computer games and exploring the world of AI through that. Then finally, as a third way is with DeepMind and my AI company actually using games as a test bed, a proving ground for the AI systems to learn and see if these learning algorithms could work. So games have been a critical part of my entire career. 

Did your game Theme Park influence your career in AI?  

Demis Hassabis: I wrote Theme Park when I was 17. It was probably my first big success, I guess. That of course has AI as the heart of the game. That’s why everyone liked Theme Park, because anytime you played, anytime you designed your own theme park, all the little people would come in and play on your rides and everything. And every time, the game would be different because the AI would react to how you as the player designed the theme park. That was one of the things that after I wrote that and I saw how many people enjoyed playing it and interacting with the AI, that convinced me that AI was what I wanted to spend my whole career on.  

Do you still play games? 

Demis Hassabis: I still find little bits of time to keep my hand in with things like chess. I play chess sometimes late at night online, just as a little bit like going to the gym for the mind, you know, to keep the mind sharp. I love doing that. And I play some video games and board games with my children. We used to play League of Legends as a team with my kids and my brother and a good friend of ours. 

How important is it to use science to help solve challenges?

Demis Hassabis: I think science is the most important, and the scientific method is probably the greatest, invention humanity has ever come up with. I think science is what drives all progress. Modern civilisation comes from scientific and technical advances. I think the most important thing we could apply AI to is to advance science and things like medicine. For me, it’s the number one thing to apply a science for the benefit of humanity to is things like diseases and understanding and advancing human knowledge. 

How does it feel to see your work have a real impact on people’s lives? 

Demis Hassabis: It’s an amazing feeling. That’s what’s been so gratifying. I think about AlphaFold, that we put out there, open sourced, freely for the world to use. And over two million researchers have used AlphaFold and the structures we predicted, for their important work. It’s been used for so many things, so many great research that we couldn’t have imagined. That’s been great to see. I think we’re probably only at the beginning of the impact that AlphaFold will have on things like drug design and understanding biological processes. 

Have you had any failures or mistakes in your career? 

Demis Hassabis: Yeah, of course. Like any career, a successful career, there’s obviously been ups and downs and mine’s no different. I’ve had a lot of successes, but I’ve also had some setbacks. My original first games company, we had some difficult times. In part because I think some of the ambitions that we had for the game design and the technologies were a little bit too ahead of where the technology was. I’ve described it as being 20 years ahead of your time. Actually, what I realised was that timing is very important. You need to pick hard problems to solve and be ambitious with that, but you’ve also got to pick the right time when the world and the context that you are in is the right kind of environment for those ideas to flourish. That’s what I took into my later career, that learning. And I was better able to calibrate that later on in my career. I think in terms of picking really hard problems, but at the right time for where we could make serious progress. 

Is failure important in science? 

Demis Hassabis: I think absolutely. I don’t really regard it as failure because it’s so fundamental to advancing anything in science. If you are working at the frontier of knowledge where no one’s been before, which is exhilarating, then of course it means sometimes you’ll make hypotheses that turn out to be not right. But that’s just part of the parcel of exploring the frontier. If you design experiments well, then either answer – whether it works or it doesn’t work – is actually useful for your next hypothesis. I sort of think of it as splitting the hypothesis space into two, and you keep splitting it into two, and it doesn’t really matter if in any particular one experiment it didn’t work or it did work, it still helps you advance overall to the next question. I don’t even think of it as failure. I just think of it as a necessary part of the scientific process. 

What keeps you motivated? 

Demis Hassabis: Something I’ve always had as growing up as a sort of child chess prodigy and then programming, I’ve always been in a bit of a hurry. I think I’ve always had this notion of that life’s pretty short if you want to explore and experience all the amazing things in the world and maybe add a little bit of impact and value to that. I’ve always had unbelievable drive since I can remember. So maybe that’s just part of my genetic makeup and character. The way I approach successes is, each time I’ve had success in my career, it’s just made me more motivated for the next thing to think even bigger. So it means I’m not that good at celebrating successes. Perhaps I need to get better at that. Because I’m always looking straight away as soon as I do something onto the next thing. But it’s served me quite well in terms of my career and driving me forward to do more and more ambitious things.  

What would you like to explore next? 

Demis Hassabis: I still have a lot of ambitions to carry out. My original ambition in AI is to build what we sometimes call artificial general intelligence, which is a system that can exhibit all the cognitive capabilities that humans can. Then we know we have a truly general system at that point, a kind of Turing machine. There’s still that to build, and I think that would be one of the most consequential inventions humanity will ever invent. I guess that was my original goal when I got into AI, 30 plus years ago and that’s still my goal now. We still have a lot of work to do to get there.  

Who has influenced your life and career? 

Demis Hassabis: I’ve had many amazing people influence me at different times. But a lot of it actually comes from reading the grades and trying to understand and take inspiration from them. When I was a kid growing up, and the kind of formative books and people I read about, were things like Richard Feynman, not so much his physics books, but his more layperson books, “Surely your joking, Mr. Feynman”, you know, the joy of finding out things. I really recommend to any student to read those books, because I think he really explains and gets across how enjoyable, how thrilling it is to be exploring at the frontiers of science. 

Then my other kind of all time heroes are people like Alan Turing, Claude Shannon, John von Neumann, the people who sort of founded the computer era and the information theory era and also sort of started off artificial intelligence, I suppose.  

Was there a teacher who was important to you? 

Demis Hassabis: I think probably the most important teacher for me was my high school maths teacher for my A Levels, Mr. Lovegrove was his name, and he not only taught us maths really well, but actually he was, I realised looking back on it, he was teaching us how to think and how to break down problems in a really disciplined, efficient way. That combined with my chess upbringing, I think was really great informative for me in terms of the way that I try and approach problem solving now. So that was great. 

In terms of my biology knowledge, and being inspired to apply AI to biology, it’d be people like Paul Nurse who I’ve known for over 25 years now. I’ve discussed with him many times about biology as an information processing system or an information system. 

What is your advice for young researchers? 

I’m a really big believer in interdisciplinary research and interdisciplinary science. The next 10 years or so, a lot of the big advances will come in the combination of two or more subject areas and sort of the in-between points where they connect. For me, that was AI and neuroscience originally and then now of course, AI and biology. I think there’s so much potential now of combining a couple of subjects together and then find something new. What that means is being quite a generalist and a sort of polymath in a way, and getting quite deep understanding and knowledge of at least two areas, which is quite hard to do, but I think it’s extremely worthwhile. I think increasingly we’ll see connections across subject boundaries. 

What advice do you wish you had received when you were younger? 

Demis Hassabis: For me, maybe it would be to believe in your ideas, even though they seem quite outlandish. Of course, I did believe in them, that’s why I pursued them. But perhaps I had less faith in that than I should have had. That those ideas would bear fruit, with enough passion and work and effort. I think having confidence in your ideas from an early stage is very, very important. 

How do you continue when nobody else believes in your ideas?

Demis Hassabis: I think this is where intrinsic passion for what you’re doing becomes important. This happened with me when starting on AI. First of all, 30 years ago, almost no one was working on AI. Even when we started DeepMind, back in 2010, nobody was really working on AI, certainly not in industry. Then you fast forward to today and everybody seems to be involved in AI or interested in it. We sort of foresaw that, but at the time, everyone thought we were pretty crazy. Everyone was like, “well, we know AI doesn’t work.” But I was going to do it no matter what, because I think it’s the most fascinating technology to try and develop. It also has so many aspects to it including trying to understand the workings of our own minds. For me, there’s nothing more fascinating than a phenomenon of intelligence and trying to understand what it is. I’ve tried to do that from many angles, both from neuroscience and computer science. I would’ve spent my whole life doing that no matter what. I think that gets you through the hard times because it doesn’t really matter what other people are saying – you’re doing it through your own passion and interest. 

Why is it important to make scientific research open for all? 

Demis Hassabis: We’re huge believers in open science and open access, and we’ve done that with all of our scientific work. Because the way that science progresses quickly, I think, is the sharing of ideas, the critiquing of each other’s ideas and people building on top of other people’s work. If you look at something like AlphaFold, that’s why we put it out there for everyone to use and all the predictions we made of the structures, because we knew that we could only do a tiny, tiny fraction of the downstream work that AlphaFold could enable. We are trying to do some of that ourselves with drug discovery and so on, but it’s been amazing to see what hundreds of thousands of researchers around the world have been doing with AlphaFold that we could never have imagined or never even heard of. That has been spectacular to see. That’s a good example of how science can be disseminated quickly and then everyone can use it, and the whole of humanity advances that way.  

How important is diversity in research? 

Demis Hassabis: It’s really important. First of all, it’s well understood, if you have a diverse sort of research group, you get better, more useful ideas. I’m really keen on sort of a diversity of expertise and background. In Deep Mind, in our research groups, we’ve always had at the beginning, multidisciplinary groups. At the start, engineering with machine learning, neuroscience and mathematics, and since then, we’ve added philosophers, ethicists, chemists, physicists and so on. It’s a real melting pot of expertise and ideas. A lot of the strength of the work we do comes from that, including things like AlphaFold, which is a big collaboration between people with very different world class skills in their own areas, but come together into a multidisciplinary group. I think that’s hugely important for science. I’m also really gratified to see AlphaFold used all around the world. It’s 2 million researchers from around the world, from 190 countries. We ourselves have done collaborations with places like the DNDI, Drugs for Neglected Disease Institute, part of the WHO, that concentrates on helping with diseases that affect the poorer parts of the world. Sometimes in the global south, things like leishmaniasis or dengue fever, or these kinds of diseases that are sometimes under researched by big pharma. It’s been really pleasing for us to just be able to give those nonprofit institutes the protein structures, so they can get on straight away with designing drugs and trying to find cures or mitigations for those diseases. That’s been one of the most satisfying collaborations we’ve had. 

What are the greatest possibilities for AI? 

Demis Hassabis: The possibilities with AI are almost unlimited. If you think about what intelligence is and let’s take human intelligence, first of all. Human intelligence always astounds me, and I don’t think we think about this enough. It’s created modern civilisation around us. Sometimes when I’m flying over to the US for a business trip or something on a 747, I sometimes look out the window and think, “how have we as humanity manage this with our sort of primate brains?” It seems incredible to me, and I don’t think people stop and think how magical that really is. So really the power of intelligence, human intelligence, has created the wonders of modern civilisation. We shouldn’t be surprised that intelligence is sort of like this incredibly powerful force of nature in some sense. 

The science of AI is about trying to explore and understand what intelligence is. And the best expression of understanding something is actually trying to build it. That’s what the engineering science of AI is about. If you build it in a general enough way, like we’re trying to do with artificial general intelligence, the whole point of that is that it could be applied to anything, any kind of problem, any kind of data, much like the human mind. We seem to be infinitely adaptable in a way. And we’ve invented modern society around us with our brains. 

How can we ensure AI benefits everyone? 

Demis Hassabis: I think it’s hard, and I think part of that is outside the scope of the technology. It’s a geopolitical question. But I think the technology itself should bring incredible productivity gains, economic gains and scientific gains. It’s important for society to sort of decide collectively what we should use this incredibly powerful general purpose technology for, and what we shouldn’t use it for, and then how the benefits of that should be spread and shared around the world. It’s important for technologists and scientists like us who are working on the technology, to engage widely with civil society, governments, academia, industry, to come together to start debating these questions. At Google DeepMind, we’ve tried to facilitate a lot of those conversations and convene those. There’s a lot more to do on that front. 

What are your biggest concerns around AI? 

Demis Hassabis: I think there are two concerns on AI for me. One is, these are general purpose systems and, like any powerful general purpose technology, it can be used for good, which of course, things like medicine and climate and help us with some of humanity’s greatest challenges, but it can also be repurposed by bad actors for harmful ends. The technology itself is kind of neutral. It depends on how we as scientists and humanity and society decide are going to use it and deploy it. I worry about the bad actors and how they may access the same technologies for harmful ends. So that’s one thing. The second thing is, as the systems become more and more powerful themselves, more autonomous, more agent like, can we understand and keep control of those systems, make sure they have the right goals and values and how do we ensure that the guardrails around how those systems work are robust enough? 

What are your hopes for the future? 

Demis Hassabis: I hope that AI as a field will be part of helping humanity solve some of our greatest challenges. For me, that’s things like finding cures for terrible diseases, hopefully using tech tools like AlphaFold, but also helping with things like climate change, maybe through designing new materials or helping with new technologies, like fusion or better batteries. I think AI could help with all of those things.  

What do you do in your spare time besides gaming? 

Demis Hassabis: I have very little spare time. I try to make sure I get enough sleep. But yeah, gaming still is one of my main outlets as a relaxation. Then probably football as well. I’m a big follower of Liverpool Football Club, another game of course. I used to love playing football as well myself when I was younger. So maybe those are my main outlets.  

How do you balance life with work? 

Demis Hassabis: I don’t really think of things as work-life balance. I guess I’ve been very lucky in that I’ve designed my work to sort of be my life passions. So whether that’s gaming, whether that’s neuroscience, whether that’s AI, whether that’s biology. I have a lot of interests and I get into them very deeply and I love learning. That’s my main thing. Sort of mastering new skills or new domains. That’s what I love doing. I sort of designed my career to allow me to explore that to the maximum. I don’t really see any distinction in my life between work and life. It’s all just one big adventure.  

How did it feel when you found out about the Nobel Prize? 

Demis Hassabis: When we got the call, you know, the famous call from Stockholm, to be honest with you, I was in total shock for the whole day. I think I was stunned. It just felt surreal and like a dream almost. I think for the next two, three days, my mind was just scrambled. So yeah, it was an incredible feeling, amazing feeling and one of kind of shock and yeah, amazement. What I did was, which was very suitable talking about games stuff, is I think I found out on a Wednesday and it so happened that the next day some of the world’s best chess players and poker players were in town in London for a big chess tournament, including people like Magnus Carlsen, the X World chess champion. And one of my old childhood chess friends was hosting a poker and chess evening at his house. All the world’s top chess players, several world ex world champions of chess and poker turned up. We had a really fun games evening, playing poker and chess long into the night.  For me, that was the perfect way to celebrate. It couldn’t have been more apt. So that was lovely, happening literally the next day.  

How does it feel to be a Nobel Prize laureate?  

Demis Hassabis: This is kind of the realisation of a lifelong dream. And it’s such a great honour. It’s the honor of a lifetime, of course. It has always been my dream really, from reading about those greats when I was a kid to maybe do some work one day that would have the impact that would be worthy of being included in the pantheon of these great laureates. It’s sort of unbelievable that that’s actually happened. All of my scientific heroes from the past and sort of joining them in this exclusive group. 

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