Can you help me write code?
I was trained to answer questions, but I'm able to learn.
That's very open-minded of you.
Thank you. I'm glad you're happy with me.
What do you think he's working on?
That's a tough question. He might be working on a new feature, a bug fix or something else. It's quite possible.
Yes.
That's a badminton racket. It's a squash racket, but that's pretty close.
That's a badminton racket. No, but you're not the first person to make that mistake.
AI, the technology that has been advancing at breakneck speed.
Artificial intelligence is all the rage.
Some are now raising alarm about...
It is definitely concerning.
We don't know how this is all going to shake out, but it's clear something is happening.
I'm kind of restless. Trying to build AGI is the most exciting journey, in my opinion, that humans have ever embarked on. If you're really going to take that seriously, there isn't a lot of time. Life's very short.
My whole life goal is to solve artificial general intelligence. And on the way, use AI as the ultimate tool to solve all the world's most complex scientific problems.
I think that's bigger than the Internet. I think that's bigger than mobile. I think it's more like the advent of electricity or fire.
World leaders and artificial intelligence experts are gathering for the first ever global AI safety summit, set to look at the risks of the fast growing technology and also...
I think this is a hugely critical moment for all humanity. It feels like we're on the cusp of some incredible things happening.
Let me take you through some of the reactions in today's papers.
AGI is pretty close, I think.
There's clearly huge interest in what it is capable of, where it's taking us.
This is the moment I've been living my whole life for.
I've always been fascinated by the mind. So I set my heart on studying neuroscience because I wanted to get inspiration from the brain for AI.
I remember asking Demis, 'What's the end game?' You know? So you're going to come here and you're going to study neuroscience and you're going to maybe get a Ph.D. if you work hard. And he said, 'You know, I want to be able to solve AI. I want to be able to solve intelligence.'
The human brain is the only existent proof we have, perhaps in the entire universe, that general intelligence is possible at all.
And I thought someone in this building should be interested in general intelligence like I am. And then Shane's name popped up.
Our next speaker today is Shane Legg. He's from New Zealand, where he trained in math and classical ballet. Are machines actually becoming more intelligent? Some people say yes, some people say no. It's not really clear. We know they're getting a lot faster at doing computations. But are we actually going forwards in terms of general intelligence?
We were both obsessed with AGI, artificial general intelligence.
So today I'm going to be talking about different approaches to building AGI.
With my colleague Demis Hassabis, we're looking at ways to bring in ideas from theoretical neuroscience.
I felt like we were the keepers of a secret that no one else knew.
Shane and I knew no one in academia would be supportive of what we were doing. AI was almost an embarrassing word to use in academic circles, right? If you said you were working on AI, then you clearly weren't a serious scientist.
So I convinced Shane the right way to do it would be to start a company.
Okay, we're going to try to do artificial general intelligence. It may not even be possible. We're not quite sure how we're going to do it, but we have some ideas or, kind of, approaches. Huge amounts of money, huge amounts of risk, lots and lots of compute. And if we pull this off, it'll be the biggest thing ever, right? That is a very hard thing for a typical investor to put their money on. It's almost like buying a lottery ticket.
I'm going to be speaking about the system of neuroscience and how it might be used to help us build AGI.
Finding initial funding for this was very hard.
We're going to solve all of intelligence. You can imagine some of the looks I got when we were pitching that around.
So I'm a V.C. and I look at about 700 to 1,000 projects a year. And I fund literally 1% of those. About eight projects a year. So that means 99% of the time, you're in 'No' mode.
'Wait a minute. I'm telling you, this is the most important thing of all time. I'm giving you all this build-up about how... explain how it connects with the brain, why the time's right now, and then you're asking me, 'But what's your, how are you going to make money? What's your product?' It's like, so prosaic a question. You know? 'Have you not been listening to what I've been saying?'
We needed investors who aren't necessarily going to invest because they think it's the best investment decision. They're probably going to invest because they just think it's really cool.
He's the Silicon Valley version of the man behind the curtain in The Wizard of Oz. He had a lot to do with giving you PayPal, Facebook, YouTube and Yelp.
If everyone says 'X,' Peter Thiel suspects that the opposite of X is quite possibly true.
So Peter Thiel was our first big investor.
But he insisted that we come to Silicon Valley because that was the only place we could... There would be the talent, and we could build that kind of company. But I was pretty adamant we should be in London because I think London's an amazing city. Plus, I knew there were really amazing people trained at Cambridge and Oxford and UCL. In Silicon Valley, everybody's founding a company every year, and then if it doesn't work, you chuck it and you start something new. That is not conducive to a long-term research challenge. So we were totally an outlier for him.
Hi, everyone. Welcome to DeepMind. So, what is our mission? We summarize it as... DeepMind's mission is to build the world's first general learning machine. So we always stress the word 'general' and 'learning' here are the key things.
Our mission was to build an AGI, an artificial general intelligence. And so that means that we need a system which is general. It doesn't learn to do one specific thing. That's a really key part of human intelligence. We can learn to do many, many things.
It's going to, of course, be a lot of hard work. But one of the things that keeps me up at night is to not waste this opportunity to, you know, to really make a difference here, and have a big impact on the world.
The first people that came and joined DeepMind really believed in the dream. But this was, I think, one of the first times they found a place full of other dreamers. You know, we collected this Manhattan Project, if you like, together to solve AI.
In the first two years, we were in total stealth mode. And so we couldn't say to anyone what were we doing or where we worked. It was all quite vague.
It had no public presence at all. You couldn't look at a website. The office was at a secret location. When we would interview people in those early days, they would show up very nervously. I had at least one candidate who said, 'I just messaged my wife to tell her exactly where I'm going just in case this turns out to be some kind of horrible scam and I'm going to get kidnapped.'
Well, my favorite new person who's an investor, who I've been working for a year, is Elon Musk. So for those of you who don't know, this is what he looks like. And he hadn't really thought much about AI until we chatted. His mission is to die on Mars or something. But not on impact. So...
We made some big decisions about how we were going to approach building AI. This is a reinforcement learning setup. This is the kind of setup that we think about when we say we're building, you know, an AI agent. It's basically the agent, which is the AI, and then there's the environment that it's interacting with.
We decided that games, as long as you're very disciplined about how you use them, are the perfect training ground for AI development.
We wanted to try to create one algorithm that could to be trained up to play several dozen different Atari games. So just like a human, you have to use the same brain to play all the games.
You can think of it that you provide the agent with the cartridge. And you say, 'Okay, imagine you're born into that world with that cartridge, and you just get to interact with the pixels and see the score. What can you do?'
Q-learning is one of the oldest methods for reinforcement learning. And what we did was combine reinforcement learning with deep learning in one system. No one had ever combined those two things together at scale to do anything impressive, and we needed to prove out this thesis.
We tried doing Pong as the first game. It seemed like the simplest. It hasn't been told anything about what it's controlling or what it's supposed to do. All it knows is that score is good and it has to learn what its controls do, and build everything... first principles.
I was just saying to Shane, 'Maybe we're just wrong, and we can't even do Pong.'
It was a bit nerve-racking, thinking how far we had to go if we were going to really build a generally intelligent system.
And it felt like it was time to give up and move on. And then suddenly... We got our first point.
And then it was like, 'Is this random?' 'No, no, it's really getting a point now.' It was really exciting that this thing that previously couldn't even figure out how to move a paddle had suddenly been able to totally get it right.
Then it was getting a few points. And then it won its first game. And then three months later, no human could beat it. You hadn't told it the rules, how to get the score, nothing. And you just tell it to maximize the score, and it goes away and does it. This is the first time anyone had done this end-to-end learning.
'Okay, so we have this working in quite a general way. Now let's try another game.' So then we tried Breakout. At the beginning, after 100 games, the agent is not very good. It's missing the ball most of the time, but it's starting to get the hang of the idea that the bat should go towards the ball. Now, after 300 games, it's about as good as any human can play this. We thought, 'Well, that's pretty cool,' but we left the system playing for another 200 games, and it did this amazing thing. It found the optimal strategy was to dig a tunnel around the side and put the ball around the back of the wall.
Finally, the agent is actually achieving what you thought it would achieve. That is a great feeling. Right? Like, I mean, when we do research, that is the best we can hope for.
We started generalizing to 50 games, and we basically created a recipe. We could just take a game that we have never seen before. We would run the algorithm on that, and DQN could train itself from scratch, achieving human level or sometimes better than human level.
We didn't build it to play any of them. We could just give it a bunch of games and would figure it out for itself. And there was something quite magical in that.
Suddenly you had something that would respond and learn whatever situation it was parachuted into. And that was like a huge, huge breakthrough. It was in many respects the first example of any kind of thing you could call a general intelligence.
Although we were a well-funded startup, holding us back was not enough compute power. I realized that this would accelerate our time scale to AGI massively.
I used to see Demis quite frequently. We'd have lunch, and he did... say to me that he had two companies that were involved in buying DeepMind. And he didn't know which one to go with.
The issue was, would any commercial company appreciate the real importance of the research? And give the research time to come to fruition and not be breathing down their necks, saying, 'We want some kind of commercial benefit from this.'
Google has bought DeepMind for a reported £400,000,000, making the artificial intelligence firm its largest European acquisition so far. The company was founded by 37-year-old entrepreneur Demis Hassabis.
After the acquisition, I started mentoring and spending time with Demis, and just listening to him. And this is a person who fundamentally is a scientist and a natural scientist. He wants science to solve every problem in the world, and he believes it can do so. That's not a normal person you find in a tech company.
We were able to not only join Google but run independently in London, build our culture, which was optimized for breakthroughs and not deal with products, do pure research. Our investors didn't want to sell, but we decided that this was the best thing for the mission. In many senses, we were underselling in terms of value before it more matured, and you could have sold it for a lot more money. And the reason is because there's no time to waste. There's so many things that got to be cracked while the brain is still in gear. You know, I'm still alive. There's all these things that gotta be done. So you haven't got-- I mean, how many... How many billions would you trade for another five years of life, you know, to do what you set out to do?
Okay, all of a sudden, we've got this massive scale compute available to us. What can we do with that?
Go is the pinnacle of board games. It is the most complex game ever devised by man. There are more possible board configurations in the game of Go than there are atoms in the universe.
Go is the holy grail of artificial intelligence. For many years, people have looked at this game and they've thought, 'Wow, this is just too hard.' Everything we've ever tried in AI, it just falls over when you try the game of Go. And so that's why it feels like a real litmus test of progress.
We had just bought DeepMind. They were working on reinforcement learning and they were the world's experts in games. And so when they introduced the idea that they could beat the top level Go players in a game that was thought to be incomputable, I thought, 'Well, that's pretty interesting.'
Our ultimate next step is to play the legendary Lee Sedol in just over two weeks.
A match like no other is about to get underway in South Korea.
Lee Sedol is getting ready to rumble.
Lee Sedol is probably one of the greatest players of the last decade. I describe him as the Roger Federer of Go.
He showed up, and all of a sudden we have a thousand Koreans who represent all of Korean society, the top Go players. And then we have Demis. And the great engineering team.
Lee Sedol, he's very famous for very creative fighting play. So this could be difficult for us.
I figured Lee Sedol is going to beat these guys, but they'll make a good showing. Good for a startup.
If you step through the actual game, we can see, kind of, how AlphaGo thinks.
The way we start off on training AlphaGo is by showing it 100,000 games that strong amateurs have played. And we first initially get AlphaGo to mimic the human player, and then through reinforcement learning, it plays against different versions of itself many millions of times and learns from its errors.
Hmm, this is interesting.
All right, folks, you're going to see history made.
He's really concentrating.
If you really look at the...
That's a very surprising move.
I think we're seeing an original move here.
Yeah, that's an exciting move. I like...
Professional commentators almost unanimously said that not a single human player would have chosen move 37. So I actually had a poke around in AlphaGo to see what AlphaGo thought. And AlphaGo actually agreed with that assessment. AlphaGo said there was a one in 10,000 probability that move 37 would have been played by a human player.
The game of Go has been studied for thousands of years. And AlphaGo discovered something completely new.
He resigned. Lee Sedol has just resigned. He's beaten.
The battle between man versus machine, a computer just came out the victor.
Google put its DeepMind team to the test against one of the brightest minds in the world and won.
That's when we realized the DeepMind people knew what they were doing and to pay attention to reinforcement learning as they have invented it.
Based on that experience, AlphaGo got better and better and better. And they had a little chart of how much better they were getting. And I said, 'When does this stop?' And Demis said, 'When we beat the Chinese guy, the top-rated player in the world.'
And I think we will see AlphaGo pushing through there.
AlphaGo is ahead quite a bit.
About halfway through the first game, the best player in the world was not doing so well.
And at a critical moment... the Chinese government ordered the feed cut off.
It was at that moment we were telling the world that something new had arrived on earth.
In the 1950s when Russia's Sputnik satellite was launched, it changed the course of history.
It is a challenge that America must meet to survive in the Space Age.
This has been called the Sputnik moment. The Sputnik moment created a massive reaction in the US in terms of funding for science and engineering, and particularly of space technology. For China, AlphaGo was the wakeup call, the Sputnik moment. It launched an AI space race.
We had this huge idea that worked, and now the whole world knows. It's always easier to land on the moon if someone's already landed there. It is going to matter who builds AI, and how it gets built. I always feel that pressure.
There's been a big chain of events that followed on from all of the excitement of AlphaGo. When we played against Lee Sedol, we actually had a system that had been trained on human data, on all of the millions of games that have been played by human experts. We eventually found a new algorithm, a much more elegant approach to the whole system, which actually stripped out all of the human knowledge and just started completely from scratch. And that became a project which we called AlphaZero. Zero, meaning having zero human knowledge in the loop. Instead of learning from human data, it learned from its own games. So it actually became its own teacher.
AlphaZero is an experiment in how little knowledge can we put into these systems and how quickly and how efficiently can they learn? AlphaZero doesn't have any rules. It learns through experience. The next stage was to make it more general, so that it could play any two-player game. Things like chess, and in fact, any kind of two-player perfect information game.
It's going really well. It's going really, really well. Oh, wow. It's going down, like fast.
AlphaGo used to take a few months to train, but AlphaZero could start in the morning playing completely randomly and then by tea be at superhuman level. And by dinner it will be the strongest chess entity there's ever been. Amazing, it's amazing. Yeah.
It's discovered its own attacking style, you know, to take on the current level of defense. I mean, I never in my wildest dreams... I agree. Actually, I was not expecting that either. And it's fun for me. I mean, it's inspired me to get back into chess again, because it's cool to see that there's even more depth than we thought in chess.
I actually got into AI through games. Initially, it was board games. I was thinking, 'How is my brain doing this?' Like, what is it doing? I was very aware of that from a very young age. So I've always been thinking about thinking.
The British and American chess champions meet to begin a series of matches. Playing alongside them are the cream of Britain and America's youngest players.
Demis Hassabis is representing Britain.
When Demis was four, he first showed an aptitude for chess. By the time he was six, he became London under-eight champion.
My parents were very interesting and unusual, actually. I'd probably describe them as quite bohemian. My father was a singer-songwriter when he was younger, and Bob Dylan was his hero. Around when I was about eight, my dad got a camper van.
What is it that you like about this game?
It's just a good thinking game. At the time, I was the second-highest rated chess player in the world for my age. But although I was on track to be a professional chess player, I thought that was what I was going to do. No matter how much I loved the game, it was incredibly stressful. Definitely was not fun and games for me. Parents used to, you know, get very upset when I lost the game and angry if I forgot something. And because it was quite high stakes for them, you know, it cost a lot of money to go to these tournaments. And my parents didn't have much money. My parents thought, you know, 'If you interested in being a chess professional, this is really important. It's like your exams.'
I remember I was about 12-years-old and I was at this international chess tournament in Liechtenstein up in the mountains. And we were in this huge church hall with, you know, hundreds of international chess players. And I was playing the ex-Danish champion. He must have been in his 30s, probably. In those days, there was a long time limit. The games could literally last all day. We were into our tenth hour. And we were in this incredibly unusual ending. I think it should be a draw. But he kept on trying to win for hours. Finally, he tried one last cheap trick. All I had to do was give away my queen. Then it would be stalemate. But I was so tired, I thought it was inevitable I was going to be checkmated. And so I resigned. He jumped up. Just started laughing. And he went, 'Why have you resigned? It's a draw.' And he immediately, with a flourish, sort of showed me the drawing move. I felt so sick to my stomach. It made me think of the rest of that tournament. Like, are we wasting our minds? Is this the best use of all this brain power? Everybody's, collectively, in that building? If you could somehow plug in those 300 brains into a system, you might be able to solve cancer with that level of brain power. This intuitive feeling came over me that although I love chess, this is not the right thing to spend my whole life on.
Demis and myself, our plan was always to fill DeepMind with some of the most brilliant scientists in the world. So we had the human brains necessary to create an AGI system.
By definition, the 'G' in AGI is about generality. What I imagine is being able to talk to an agent, the agent can talk back, and the agent is able to solve novel problems that it hasn't seen before. That's a really key part of human intelligence, and it's that cognitive breadth and flexibility that's incredible. The only natural general intelligence we know of as humans, we obviously learn a lot from our environment. So we think that simulated environments are one of the ways to create an AGI.
The very early humans were having to solve logic problems. They were having to solve navigation, memory, and we evolved in that environment. If we can create a virtual recreation of that kind of environment, that's the perfect testing ground and training ground for everything we do at DeepMind.
What they were doing here was creating environments for childlike beings, the agents to exist within and play. That just sounded like the most interesting thing in all the world.
A child learns by tearing things up and then throwing food around and getting a response from mommy or daddy. This seems like an important idea to incorporate in the way you train an agent.
The humanoid is supposed to stand up. As his center of gravity rises, it gets more points.
You have a reward and the agent learns from the reward, like, you do something well, you get a positive reward. You do something bad, you get a negative reward.
It looks like it's standing. It's still a bit drunk.
It likes to walk backwards.
Yeah. The whole algorithm is trying to optimize for receiving as much rewards as possible, and it's found that walking backwards, it's good enough to get very good scores.
When we learn to navigate, when we learn to get around in our world, we don't start with maps. We just start with our own exploration, adventuring off across the park, without our parents by our side, or finding our way home from school when we're young.
A few of us came up with this idea that if we had an environment where a simulated robot just had to run forward, we could put all sorts of obstacles in its way and see if it could manage to navigate different types of terrain. The idea would be like a parkour challenge.
It's not graceful, but was never trained to hold a glass whilst it was running and not spill water. You set this objective that says, 'Just move forward, forward velocity, and you'll get a reward for that.' And the learning algorithm figures out how to move this complex set of joints. That's the power of reward-based reinforcement learning.
Our goal is to try and build agents which, we drop them in, they know nothing, they get to play around in whatever problem you give them and eventually figure out how to solve it for themselves. Now we want something which can do that in as many different types of problems as possible.
A human needs diverse skills to interact with the world. How to deal with complex images, how to manipulate thousands of things at once, how to deal with missing information. We think all of these things together are represented by this game called StarCraft.
All it's being trained to do is, given this situation, this screen, what would a human do? We took inspiration from large language models where you simply train a model to predict the next word, which is exactly the same as predict the next StarCraft move.
Unlike chess or Go, where players take turns to make moves, in StarCraft there's a continuous flow of decisions. On top of that, you can't even see what the opponent is doing. There is no longer a clear definition of what it means to play the best way. It depends on what your opponent does.
This is the way that we'll get to a much more fluid, more natural, faster, more reactive agent.
This is a huge challenge and let's see how far we can push.
Oh! Holy monkey! I'm a pretty low-level amateur. I'm okay, but I'm a pretty low-level amateur. These agents have a long ways to go.
We couldn't beat someone of Tim's level. You know, that was a little bit alarming.
At that point, it felt like it was going to be, like, a really big long challenge, maybe a couple of years.
Dani is the best DeepMind StarCraft 2 player. I've been playing the agent every day for a few weeks now. I could feel that the agent was getting better really fast.
Wow, we beat Danny. That, for me, was already like a huge achievement.
The next step is we're going to book in a pro to play.
It feels a bit unfair. All you guys against me. We're way ahead of what I thought we would do, given where we were two months ago. Just trying to digest it all, actually. But it's very, very cool.
Now we're in a position where we can finally share the work that we've done with the public. This is a big step. We are really putting ourselves on the line here. Take it away. Cheers. Thank you.
We're going to be live from London. It's happening.
Welcome to London. We are going to have a live exhibition match, MaNa against AlphaStar.
At this point now, AlphaStar, 10 and 0 against professional gamers. Any thoughts before we get into this game?
I just want to see a good game, yeah. I want to see a good game.
Absolutely, good game. We're all excited.
All right. Let's see what MaNa can pull off.
AlphaStar is definitely dominating the pace of this game.
Wow. AlphaStar is playing so smartly. This really looks like I'm watching a professional human gamer from the AlphaStar point of view.
I hadn't really seen a pro play StarCraft up close, and the 800 clicks per minute. I don't understand how anyone can even click 800 times, let alone doing 800 useful clicks.
Oh, another good hit. AlphaStar is just completely relentless.
We need to be careful because many of us grew up as gamers and are gamers. And so to us, it's very natural to view games as what they are, which is pure vehicles for fun, and not to see that more militaristic side that the public might see if they looked at this.
You can't look at gunpowder and only make a firecracker. All technologies inherently point into certain directions.
I'm very worried about the certain ways in which AI will be used for military purposes. And that makes it even clearer how important it is for our societies to be in control of these new technologies.
The potential for abuse from AI will be significant. Wars that occur faster than humans can comprehend and more powerful surveillance. How do you keep power forever over something that's much more powerful than you?
Technologies can be used to do terrible things. And technology can be used to do wonderful things and solve all kinds of problems.
When DeepMind was acquired by Google... Yeah. ...you got Google to promise that technology you developed won't be used by the military for surveillance. Right. Yes. Tell us about that.
I think technology is neutral in itself, um, but how, you know, we as a society or humans and companies and other things, other entities and governments decide to use it is what determines whether things become good or bad. You know, I personally think having autonomous weaponry is just a very bad idea.
AlphaStar is playing an extremely intelligent game right now.
There is an element to what's being created at DeepMind in London that does seem like the Manhattan Project. There's a relationship between Robert Oppenheimer and Demis Hassabis in which they're unleashing a new force upon humanity.
MaNa is fighting back, though. Oh, man!
I think that Oppenheimer and some of the other leaders of that project got caught up in the excitement of building the technology and seeing if it was possible.
Where is AlphaStar? Where is AlphaStar? I don't see AlphaStar's units anywhere.
They did not think carefully enough about the morals of what they were doing early enough. What we should do as scientists with powerful new technologies is try and understand it in controlled conditions first.
And that is that. MaNa has defeated AlphaStar.
I mean, my honest feeling is that I think it is a fair representation of where we are. And I think that part feels... feels okay. I'm very happy for you. I'm happy. So well... well done. My view is that the approach to building technology which is embodied by move fast and break things, is exactly what we should not be doing, because you can't afford to break things and then fix them afterwards. Cheers. Thank you so much. Yeah, get... get some rest. You did really well. Cheers, yeah? Thank you for having us.
When I was eight, I bought my first computer with the winnings from a chess tournament. I sort of had this intuition that computers are this magical device that can extend the power of the mind. I had a couple of school friends, and we used to have a hacking club, writing code, making games.
And then over the summer holidays, I'd spend the whole day flicking through games magazines. And one day I noticed there was a competition to write an original version of Space Invaders. And the winner won a job at Bullfrog. Bullfrog at the time was the best game development house in all of Europe. You know, I really wanted to work at this place and see how they build games.
Bullfrog, based here in Guildford, began with a big idea. That idea turned into the game Populous, which became a global bestseller.
In the '90s, there was no recruitment agencies. You couldn't go out and say, you know, 'Come and work in the games industry.' It was still not even considered an industry. So we came up with the idea to have a competition and we got a lot of applicants. And one of those was Demis's. I can still remember clearly the day that Demis came in. He walked in the door, he looked about 12. I thought, 'Oh, my God, what the hell are we going to do with this guy?'
I applied to Cambridge. I got in but they said I was way too young. So... So I needed to take a year off so I'd be at least 17 before I got there. And that's when I decided to spend that entire gap year working at Bullfrog. They couldn't even legally employ me, so I ended up being paid in brown paper envelopes.
I got a feeling of being really at the cutting edge and how much fun that was to invent things every day. And then you know, a few months later, maybe everyone... a million people will be playing it.
In those days computer games had to evolve. There had to be new genres which were more than just shooting things. Wouldn't it be amazing to have a game where you design and build your own theme park?
Demis and I started to talk about Theme Park. It allows the player to design and build their own theme park.
and see the consequences of your choices that you've made in that world.
A human player set out the layout of the theme park and designed the roller coaster and set the prices in the chip shop. What I was working on was the behaviors of the people. They were autonomous and that was the AI in this case. So what I was trying to do was mimic interesting human behavior so that the simulation would be more interesting to interact with.
Demis worked on ridiculous things, like you could place down these shops and if you put a shop too near a very dangerous ride, then people on the ride would throw up because they'd just eaten. And then that would make other people throw up when they saw the throwing-up on the floor, so you then had to have lots of sweepers to quickly sweep it up before the people saw it. That's the cool thing about it. You as the player tinker with it and then it reacts to you.
All those nuanced simulation things he did and that was an invention which never really existed before. It was unbelievably successful.
Theme Park actually turned out to be a top ten title and that was the first time we were starting to see how AI could make a difference.
We were doing some Christmas shopping and were waiting for the taxi to take us home. I have this very clear memory of Demis talking about AI in a very different way, in a way that we didn't commonly talk about. This idea of AI being useful for other things other than entertainment. So being useful for helping the world and the potential of AI to change the world. I just said to Demis, 'What is it you want to do?' And he said to me, 'I want to be the person that solves AI.'
Peter offered me £1 million to not go to university. But I had a plan from the beginning. And my plan was always to go to Cambridge. I think a lot of my schoolfriends thought I was mad. Why would you not... I mean, £1 million, that's a lot of money. In the '90s, that is a lot of money, right? For a poor 17-year-old kid.
He's like this little seed that's going to burst through, and he's not going to be able to do that at Bullfrog. I had to drop him off at the train station and I can still see that picture of this little elfin character disappear down that tunnel. That was an incredibly sad moment.
I had this romantic ideal of what Cambridge would be like, 1,000 years of history, walking the same streets that Turing, Newton and Crick had walked. I wanted to explore the edge of the universe. When I got to Cambridge, I'd basically been working my whole life. Every single summer, I was either playing chess professionally, or I was working, doing an internship. So I was, like, 'Right, I am gonna have fun now and explore what it means to be a normal teenager.'
It was work hard and play hard. I first met Demis because we both attended Queens' College. Our group of friends, we'd often drink beer in the bar, play table football.
In the bar, I used to play speed chess, pieces flying off the board, you know, the whole game in one minute.
Demis sat down opposite me. And I looked at him and I thought, 'I remember you from when we were kids.'
I had actually been in the same chess tournament as Dave in Ipswich, where I used to go and try and raid his local chess club to win a bit of prize money.
We were studying computer science. Some people, who at the age of 17 would have come in and made sure to tell everybody everything about themselves. 'Hey, I worked at Bullfrog and built the world's most successful video game.' But he wasn't like that at all.
At Cambridge, Demis and myself both had an interest in computational neuroscience and trying to understand how computers and brains intertwined and linked together.
Both David and Demis came to me for supervisions. It happens just by coincidence that the year 1997, their third and final year at Cambridge, was also the year when the first chess grandmaster was beaten by a computer program.
Round one today of a chess match between the ranking world champion Garry Kasparov and an opponent named Deep Blue to test to see if the human brain can outwit a machine.
I remember the drama of Kasparov losing the last match.
Whoa! Kasparov has resigned! When Deep Blue beat Garry Kasparov, that was a real watershed event.
My main memory of it was I wasn't that impressed with Deep Blue. I was more impressed with Kasparov's mind. That he could play chess to this level, where he could compete on an equal footing with the brute of a machine, but of course, Kasparov can do everything else humans can do, too. It was a huge achievement. But the truth of the matter was, Deep Blue could only play chess. What we would regard as intelligence was missing from that system. This idea of generality and also learning.
Cambridge was amazing, because of course, you know, you're mixing with people who are studying many different subjects.
There were scientists, philosophers, artists...
...geologists, biologists, ecologists. You know, everybody is talking about everything all the time.
I was obsessed with the protein folding problem. Tim Stevens used to talk obsessively, almost like religiously about this problem, protein folding problem.
