Thanks Alister for that lovely introduction, and it's so great to be back at Cambridge. I always have a warm feeling when I sort of make my homecoming back to Cambridge. And specifically this lecture hall, as Alister reminded me, I think it is the first lecture hall I was in. It's always been my favorite lecture hall. I remember telling, and I see a lot of my old friends here from my Cambridge days, Aaron, I think about that one day maybe I'd come back to give a lecture in here and talk about announcing AGI and maybe a robot would walk on and astound everyone. I'm not going to do that today, to disappoint you, but maybe in a few years' time I'll come back again and I'll give that lecture. But you know, this is an amazing place. It's such an inspiring place, and I'm going to talk a little bit about how Cambridge has inspired my whole career actually, and hopefully is going to do the same for many of you as the students in the room.
So for me, my journey on AI started with games, and specifically chess, as Alister mentioned. So I was playing chess from the age of four years old, and very seriously for the England junior teams and things like that. And it got me thinking about thinking itself. You know, how does our mind come up with these plans, with these ideas? How do we problem solve, and how can we improve? Obviously when you're playing chess at a young age, you're trying to play competitively, you're trying to improve that process. And it was fascinating to me, perhaps more fascinating than even the games I was playing, was the actual mental processes behind it. And in fact, AI and can computers — I came across computers and AI for the first time in the context of chess and trying to use very early chess computers like the one on the right here. I think this was my first ever chess computer. They were physical boards where you actually had to press the squares down to move the pieces. And of course we were supposed to be using these chess computers to train opening theory and learn more about chess, but I remember being fascinated by the fact that someone had programmed this lump of inanimate plastic to actually play chess really well against you. And I was sort of really fascinated by how that was done and how someone could program something like that.
And I ended up experimenting myself in my early teenage years with an Amiga 500 computer, an amazing home computer back in the late '80s and early '90s, and building those kinds of AI programs myself to play games like Othello. And really that was my first taste of AI, and I was hooked from then on. And that's, you know, I decided from very early on that I would spend my entire career trying to push the frontiers of this technology. So then that led me to Cambridge, and my three years here were incredibly formative for me. And I went to a comprehensive school in North London. No one had ever got to sort of — no one had gone to Oxbridge in living memory. And the reason I wanted to come to Cambridge was all these inspiring stories that I'd heard about what happened at Cambridge, all these amazing people that I used to read about, their biographies and the work they'd done. Especially people like Crick and Watson in the top left there. And I remember particularly a film, The Race for the Double Helix, an amazing film from the '80s if you haven't seen it, with Jeff Goldblum, one of his early parts — with all the enthusiasm that he plays all of his parts, as he was Watson.
And they were having just such an amazing time discovering, roaming around Cambridge, working on things like DNA. And I thought, I want a piece of that. And I want to feel what it's like to be at the frontier of discovery, and what could be more exhilarating. And that film actually really brought it to life what that might be like. And then of course all of my heroes, my scientific heroes, a lot of them had gone through Cambridge, people like Alan Turing and Charles Babbage of course, and in the lecture hall that we now sit in. And even places like the Eagle pub, where if you start at Queens' College, one of the tours they give you on the first day is to go and show you the reputed sort of table where they were discussing the DNA structure. And you can't help but be inspired by that, and walking down King's Parade, I always felt like the intellectual giants of the past were almost speaking to you from the stones. And that's how I felt, you know, going for a late night burger at Gardenia's. But that was what was inspiring me, like all of these amazing people that had walked those same steps over hundreds of years, and that's the history that is sort of unrivaled here at Cambridge that I think we can still draw on and take inspiration from today.
And then there's a picture of me and Aaron there, one of my best friends from Queens', obviously on the Mathematical Bridge there. And then finally, you know, when Alister mentioned obviously the Nobel Prize, and it was an honor of a lifetime to go and collect that in Stockholm in December. Amazing one week of activities, but my favorite activity was when you get to sign the Nobel book in the Nobel Foundation. And that's the book there, and one of my pictures. And I started sort of leafing through the book. You sign your name and then you leaf back and you wonder, is Crick in there, and of course he is, and then you go back further and then Einstein's signature is there and it's just mind-blowing really. And I started to spend an hour just photographing every page of the book. So you know, it's full circle for me of that picture and then really seeing that film in the late '80s.
So then, you know, in 2010 we started DeepMind in London as really at the time it was a kind of an Apollo program effort, is the way we thought of it, for trying to build artificial general intelligence. You know, AI that was truly general and could perform all the cognitive capabilities that humans are capable of. So it would be a truly general AI system. In fact, you know, the idea for that really comes from Turing and Turing machines. So something that's able to compute anything that is computable, as Turing showed with his Turing machines. And really that's been the foundation for me and one of the main things that I carried with me from the lectures here at Cambridge was all these theoretical underpinnings of computer science and computation theory that people like Turing and Shannon famously did in the '40s and the '50s. So we started in 2010, and it's amazing, like it's 15 years ago, which in some ways isn't that long ago, but when we started out DeepMind, almost nobody was working on AI, which is hard to believe today given that almost everyone seems to be working on AI today. In just a matter of just over a decade, things have accelerated incredibly, and obviously we've been part of that very exciting journey.
So our mission at DeepMind from the beginning was — we talk about building AI responsibly to benefit humanity, but the way we used to articulate it when we started out was in a two-step process. Step one: solve intelligence. Step two: use it to solve everything else. And this seemed very outlandish at the time in 2010, and you can imagine trying to pitch a venture capitalist on the basis of that mission. It seemed pretty crazy, but I still fundamentally believe in that today, and I think more and more people are realizing that AI built in this general way could have this kind of profound and transformative impact on almost any field, which is obviously the second part of that mission statement. And I think for me that involves accelerating scientific discovery itself and medicine, and advancing our understanding of the universe around us. So back when we started out, there were basically two ways — and in fact when I was studying here in the '90s, there were two ways to build AI, broadly speaking. There's the expert system way, which is you kind of pre-program an expert system directly with the solution.
Things like Deep Blue that beat Garry Kasparov at chess very famously in the '90s, actually while I was studying here, that would be the pinnacle example probably of an expert system. But the problem with these expert systems was, and why they never really scaled to full general intelligence, is they can't deal with the unexpected. If something unexpected happens that you didn't already cater for, there's nothing in the system that will allow it to deal with that. And they sort of were inspired by logic systems, and they were quite rigid and fragile and brittle because of that. Whereas the modern-day approaches are built on learning systems. So these are systems that are able to learn for themselves and learn directly from experience or data from first principles, and really inspired by more neuroscience ideas. And obviously the promise of these systems that we have today is that they can go beyond the knowledge potentially that we as the programmers or the system designers already know how to solve. And of course that's extremely valuable in areas like scientific discovery.
So we started in the early 2010s with games, of course, and I've used games many times in my life. First of all, to train my own mind, then I used to build games and AI for computer games, and then finally in a third way to train up our AI systems. And games are the perfect proving ground for AI systems. You can start with very simple games like Atari games from the '70s. This system DQN was the first time anyone had built an end-to-end learning system that could learn directly from raw data. So in this case, the raw pixels on the screen, and it's not told anything about the games or anything about what it's controlling. It's just told to maximize the score based on this video stream input, pixel stream input. So we were able to master all different Atari games in sort of around 2013. Then we took these systems and we scaled them up to really the grand challenge of games AI, which is can you create systems that can play the game of Go at world champion level or beyond.
And Go, of course, is probably the most complex game that humans have ever invented. It's thousands of years old, so it's also the oldest game and one of the most elegant games. But one of the ways you can just see the complexity of Go is that there are 10 to the power 170 possible positions in Go. So that's way more than there are atoms in the observable universe. And the important point about that is that you cannot come up with a strategy in Go using brute force techniques. It would be impossible, it'd be totally intractable. So you have to do something much smarter. And we famously in basically 2016 won a million-dollar challenge match against a 10-times world champion, Lee Sedol, a legend of the game, a South Korean grandmaster, and was watched by 200 million people around the world. And not only did AlphaGo, our system, win that match, importantly it actually came up with new, original Go strategies, even though we've played Go for thousands of years and professionally for hundreds of years, it was still able to find never-seen-before strategies.
Most famously, this move 37 here in red in game two. If you watch the documentary on this, which is on YouTube, you'll see how surprised the best players in the world were. They were commentating on the game about this move. It was sort of an unthinkable move. And yet it decided game two in favor of AlphaGo, 100 moves later. So that told me about the potential for these types of systems to invent and discover new knowledge. So here, of course, we're just talking about game knowledge, but obviously my dream was to generalize this to all areas of scientific discovery.
So how do these systems work? We basically train up these neural networks through a system of self-play. So this is actually AlphaGo and also subsequent systems, AlphaGo Zero and AlphaZero, that generalized what we've done for Go to play any two-player game from scratch. And you start off with a version one of the system that doesn't really know anything about the game, just the rules, and it plays randomly. And you play, I'd say, 100,000 games against itself of this system, and that creates a new database of game positions from those 100,000 games. And from that, you train a second version, a slightly better version of the model, version two, that's trained to predict what the likely moves are to be played in any one position, and also who is more likely to win, which side, black or white, is likely to win from that position, and what percentage chance do they have of winning. And then you can use that version two — you play it against version one in a 100-game match-off, and then if it wins by a significant margin, so in this case 55% win rate, you replace the version one with version two, and you create a new database of games that are slightly higher quality, and then you learn a version three system. And if you do this and you repeat it around 17, 18 times, you go from playing randomly in the morning to 24 hours or less later, by version 17 or 18, you're stronger than world champion level. So it's quite an incredible process to see this self-improvement process playing out in a very, very short amount of time.
So if we think about what these neural networks are doing, is you're kind of reducing down this intractable search space of, you know, 10 to the power of 170 possibilities down to something that's much more tractable in a few minutes of compute time. And it's doing this by narrowing down using the neural network to efficiently guide the search mechanism. So if you think about this tree of possibilities, and each of the nodes in this tree is a Go position, then instead of looking at every possibility, you can actually use the neural network to guide you down just the most interesting and most useful lines to examine. So in this case, the ones in blue. And then after you've run out of thinking time, you pick the best line, the most promising line that you've seen thus far. So in this case, this particular line in purple.
So this then leads to — we then played not just Go, but any two-player perfect information game, and it was even able to discover new strategies and new styles of playing chess, which is kind of extraordinary given that chess computers were so strong already. So programs like Stockfish. And AlphaZero was able to beat Stockfish at the time in chess, which is almost impossible to do. And not only did it beat Stockfish — so AlphaZero here is in white playing against Stockfish who's black — but in this particular position, one of the most famous games that AlphaZero played is called the Immortal Zugzwang game. White is winning here because it favors mobility over material. So most chess computers favor material, and you'll see that black — those of you who play chess, you'll see that black has more material, but actually can't move any of its pieces. They're all stuck in the corner. And AlphaZero sacrificed material for this mobility. And actually for human grandmasters and top chess players, this is not only a very effective style, it's a very beautiful aesthetic style to play chess in.
So it's kind of amazing that AlphaZero was able to sort of discover this new way, this new dynamic way of playing. And in fact, some of the top chess players in the world commentated about this. So Garry Kasparov, my all-time favorite chess player, he said: 'The programs usually reflect priorities and prejudices of the programmer, but because AlphaZero learns for itself, I would say that its style reflects the truth.' And then the current world champion at the time, Magnus Carlsen, looked at these games and read about the books that were written about AlphaZero, and said: 'I've been influenced by one of my heroes recently, one of which is AlphaZero.' So he actually incorporated a lot of these ideas into his own game to dominate the chess scene for almost a decade now.
So we did all these landmark breakthroughs in games AI over the first, I guess, sort of decade of DeepMind's existence, but of course these were just the training ground for what we wanted to do and was just a means to an end. It wasn't the end in itself to play these games, much as I love games. It was to create these algorithms that could be generally useful for tackling real-world problems. So what we look for in real-world problems, not only scientific problems but actually industrial problems as well, we look for sort of three different criteria that make the problem suitable to be tackled by these types of AI systems and algorithms that we developed for playing games. Number one is we look for problems that can be described as massive combinatorial search spaces. So usually far too complex, far too many combinations to brute force the solution. But maybe there's some kind of structure that we can learn about with our neural networks that can guide that search very efficiently. Secondly, we look for problems that can be described with a clear objective function or some sort of metric that you can optimize against. So in games that's very easy. It's things like maximizing the score or winning the game. But actually there's a lot of real-world problems that you can boil down to a few metrics or few objective functions that you're trying to maximize. And then finally, of course, you need quite a lot of data or experience to learn from, and ideally an accurate and efficient simulator so you can generate more synthetic data to augment the real data that you have.
And it turns out that there are a lot of problems that can be couched in these terms if you're looking at the problem from this angle. And including many, many important problems in science. And the one that I always had in mind, actually, from my days of first coming across the problem here at Cambridge as an undergrad, is the protein folding problem, which I'll just quickly describe to you for those that don't know about biology and proteins. So proteins are incredibly important. They're the building blocks of life. Pretty much every function in the living body depends on proteins, from your neurons firing to your muscle fibers twitching. So really, proteins make life possible. And the protein folding problem is really easy to describe. It's basically — a protein is defined by its gene sequence, its genetic sequence, which then specifies an amino acid sequence, which in nature then folds up spontaneously into usually a very beautiful protein structure. So you go from this genetic sequence to a protein structure. And the reason the 3D structure is very important is it goes a long way to defining what function it has, what it does in the body. So it doesn't totally describe the function, but it has a big part to play in what it actually does in nature. So the protein folding problem is this problem of: can you predict the protein structure directly from this one-dimensional amino acid sequence? Can you predict that computationally, that incredible 3D structure from that sequence?
So why is this such a hard problem? Well, Levinthal, a famous protein researcher in the '60s, described this conjecture that became known as Levinthal's paradox, which is that he calculated there are roughly 10 to the 300 possible shapes that an average protein can take. And yet somehow in nature and in the body, these proteins fold up spontaneously in a matter of milliseconds. So that's the paradox. If there's so many possibilities, how does nature do this? And basically, how does physics achieve this? And that gives you hope that this must be tractable computationally in some reasonable amount of time because physics does solve this problem billions of times a second in the body. And furthermore, what attracted me to this problem was that there was a bi-annual competition called the CASP competition, which is like the Olympics for protein folding. And it happens every two years and it's run by some amazing people led by Professor John Moult at the University of Maryland. And it's been running since 1994, and it's a great competition because they work with experimentalists who painstakingly find these structures using very exotic and expensive equipment like electron microscopes. And they newly discovered structures that haven't been published yet, they put into the competition.
So the competition organizers know what the ground truth is, but the computational teams — hundreds of teams enter every competition every couple of years — and they try with their computational methods to predict those structures. And there's usually around 100 proteins that are in the competition. And then at the end of the summer they reveal what the true structures are and you can compare the predicted ones and their distances, the error in the predictions, to the real structures. So we entered AlphaFold 1 actually for the first time in 2018, and we started this AlphaFold project in 2016, actually pretty much the day after we got back from the AlphaGo match in Seoul, South Korea. We felt, and I felt, that we were ready, we had the techniques that were mature enough and ready to now be applied outside of games and to try and tackle really meaningful problems. We call them kind of root node problems because if they could be solved, they open up whole new branches and avenues of discovery that can be built on top. And protein folding was a prime example of that. So we started working on it in 2016. AlphaFold 1 was ready after a couple of years and we entered it into the CASP 13 competition.
And you can see for the decade prior, bar charts are showing the winning score of the winning team in the hardest category, actually, the hardest proteins that were being predicted. And you can sort of think of it as a percentage accuracy of how many of these amino acids have you got in the right position within a certain tolerance, within the sort of width of an atom you need to predict it within. And you can see there was sort of not much progress for a decade, and we were stuck at this 60 points level. And if you got to 90, you would be within the width of an atom, so you'd be atomic accuracy. And that's what we were told by experimentalists was the accuracy you'd had to reach so that it was competitive with experimental methods, so that experimentalists could actually rely on these predictions rather than having to necessarily do the laborious, painstaking work to find that structure. And just as a rule of thumb, my biologist friends would always tell me that it'd take a PhD student their entire PhD, so four to five years, to find the structure of just one protein. And there are 200 million proteins known to science and 20,000 proteins in the human proteome.
So with AlphaFold 1, we were able to win this competition and be better by almost 50% than the next best system. And AlphaFold 1 for the first time introduced machine learning techniques as the main component of the system for the first time. But it was not enough to reach this atomic accuracy. We actually had to go back to the drawing board with what we'd learned and re-architect from scratch for AlphaFold 2, using all the learnings we had from AlphaFold 1, to finally reach this atomic accuracy. And that led the organizers to declare that the problem had been solved in the end of 2020.
So this is an example of how AlphaFold works visually. So you can see on the left-hand side here is a very complex protein. The ground truth is in green, the predicted structure is in blue, and you can see how closely the blue overlaps the green. And then on the right-hand side, you can see how AlphaFold 2 works. It sort of builds up that structure in an iterative process. It kind of recycles itself actually over 192 steps, and then starts as a scrunched ball of protein matter, amino acids, and then it builds out more and more plausible structure. At the end, it sort of refines the last parts of it until it has the finished prediction. We immediately then tried to — because AlphaFold is so accurate, but it's not just accurate, it's also extremely fast. It's able to fold proteins in a matter of seconds, an average protein. We realized quite quickly that we could actually fold all 200 million proteins known to science.
Over the course of a year, we used a lot of computers on the Google Cloud to fold all of them, and then put them out freely on a database with our colleagues at EMBL-EBI just up the road at the Sanger Center just outside of Cambridge. And we provided that for free, unrestricted access to anyone in the world to use it. And that 200 million proteins, if you think about how long it takes to do that experimentally, four to five years — it's kind of like a billion years of PhD time done in one year. So it's kind of amazing to think about how much science could be accelerated. And it opened up whole new avenues of exploration because many of these structures, especially for the less well-studied organisms like certain types of plants, they're very important for science and for agriculture research, but almost none of those structures will have been found and be available. So now those are all available. And also 200 million — you can look at them at an aggregate level and sort of look at structures across species, and kind of meta-structures, and see what the commonalities are through evolution. So there's really interesting new avenues and branches of structural biology now that are being explored.
And of course, we thought about safety from the beginning, and we take our responsibility very seriously at the forefront of AI. And in this case, we consulted with over 30 biosecurity and bioethics experts to make sure that what we were putting out into the world — that the benefits of that far outweighed any risks associated with it. And now I'm very proud to say that over two million researchers are using it, from pretty much every country in the world. And it's been cited over 30,000 times now, and it's become a standard tool in biology research now. And many of you in the audience who are PhD students, hopefully you're using it and making use of it, and it's just part of the standard canon now that is used for biology research.
It's been amazing to see what other researchers have done with all of this technology and all these structures. I've just called out six of my favorite examples. People at the University of Portsmouth, a research group, are using it to tackle plastic pollution in the environment, trying to design new enzymes, which are types of proteins, that can digest plastic. We're working with the Fleming Center on antibiotic resistance, neglected diseases like tropical diseases that affect the poorer parts of the world. We work with the Drugs for Neglected Diseases Institute. And here's a good example of where we can accelerate research in those areas where, whether it's malaria, leishmaniasis, Zika virus, a lot of those structures are not known. But now they can go straight to drug discovery because they have a lot of the information about the structures for those viruses and bacteria. And then there's been a lot of fundamental research being done on things like finding the structure of the nuclear pore complex, which is a very important protein that lets nutrients in and out of the nuclear pore of the cell. There's amazing work at the Broad Institute done with drug delivery, sort of designing molecular syringes, redesigning proteins that can deliver drugs targeted to a particular part of the body. And it's even being used in things like looking at mechanisms of fertility. So the amount of things that it's being used for is sort of almost every area of biology and medical research now is making use of AlphaFold.
We've continued in the last few years to develop more improvements and developments in the systems. We released AlphaFold 3 earlier this year for academics to use, and we've extended now AlphaFold 3 to deal with interactions. So you can think of AlphaFold 2 as a picture of the static protein structure, but really biology is a dynamic process. So you need to understand how different biological elements interact with each other. So of course, proteins with other proteins, but also proteins with other molecules important to life, things like DNA and RNA, and also ligands — so small molecules, which, you know, things like drug compounds. How does the protein bind with that compound? And then we have a separate set of work, AlphaFold, which is doing the reverse of AlphaFold but making use of still of the AlphaFold techniques, where if you want to design a novel protein, maybe that doesn't exist in nature for a particular job, a particular function, what is the amino acid sequence and the genetic sequence that will give you that structure? So it's kind of like running it in reverse and trying to design new structures that will do novel things, and again could be extremely useful for designing drugs and things like antibiotics and antibodies.
So taking a step back then, what is — or you know, if I look at all the work
We've done in the last 15 years, what's the implications for science and also machine learning? And if you think about what we've done with first of all our games work and then now with the scientific work that we've been working on of which AlphaFold is our best example, it's all about making this search tractable. You have this incredibly complex problem, there's many many possible solutions to the problem, and you've got to find the optimal solution, kind of needle in the haystack of that enormous combinatorial search space. And you can't do it by brute force, so you have to learn this neural network model so it sort of learns about the topology of the problem so that you can efficiently guide the search to maximize or find the optimal solution to the objective that you have in mind. And I think this is an incredibly general way and general solution, an incredibly general way to approach a whole myriad of problems. And so, we think about back to the Go example, so we're trying to use these systems to find the best Go move, but you could also change those nodes to be chemical compounds, and now you're trying to find the best molecule in chemistry space, in chemical space. And the best molecule, you know, this is the beginning of drug design. And that will bind specifically to the target you're interested in, but nothing else. So, reduces the side effects and the toxicity of that compound. And it's very similar techniques that we're using in order to design these molecules now as the next steps as we move more and more into drug discovery.
So, I think in biology at least, I feel like we're entering a new era now of what I like to call digital biology. So, you know, I think of biology in its most fundamental level as an information processing system, you know, that's trying to resist entropy around it. And I think that's basically what life is. Of course, it's a phenomenally complex and emergent information processing system. And I think that's where AI comes in. Just like maths and the maths that I learned in this room was the perfect description language for physics and physical phenomena, I think that AI is potentially the perfect description language for biology. It's perfect for dealing with the complexities of the emergent behaviors and interactions that you get in a dynamic system like biology. And I think AlphaFold is a proof point of that. And I hope when we look back in 10 years time, it won't be an isolated breakthrough, but actually have sort of heralded in this new era, golden era of digital biology. And we're trying to progress that ourselves. We started a new spin-out company Isomorphic Labs to build on our AlphaFold technology and move more into the chemistry space that I was just talking about and actually try and reimagine drug discovery from first principles with AI.
And right now it takes an average of 10 years for a drug to be developed and it's extraordinarily expensive. It costs billions and billions of dollars. And so, I'm thinking why can't we use these techniques to reduce that down from years to months, maybe even one day weeks, just like we reduced down the discovery of protein structures from potentially years down to now minutes and seconds. And so we think of this as a kind of doing science at digital speed. So trying to bring the best of what we do in the technology area to the natural sciences. And my dream one day is to be able to create maybe a kind of virtual cell, a computational cell perhaps of something very simple like a yeast cell and that you can actually run experiments in silico on it and the predictions that you get out of the virtual cell will actually tell you and form your real world experiments in the lab and you can reduce down a lot of the search that's done in the wet lab and actually more use the wet lab for validation steps rather than the very expensive and slow search process.
Of course AI and we've been using AI not just in biology but it can be used for science, mathematics, medicine more generally and we've had a whole range of breakthroughs not just in the biological sciences but from things in health like identifying eye disease from retinal scans, discovering new materials, helping with plasma containing fusion reactors, faster algorithms so AI discovering better algorithms for itself like faster matrix multiplication, doing weather prediction and even helping with quantum computers and error correction in quantum computing. And that's just a small example of some of the work we've been doing in the last two three years and I think AI will be almost applicable to pretty much every field and I always encourage actually universities to start thinking very seriously about multidisciplinary work where you apply AI to the right questions in a particular specialist field and I think there's many many advances to be made over the next five to 10 years by doing that.
So I'll just end then with a little bit of a more general view about not just AI for science, but the path to AGI and how close we are to that and our more general work on the original mission of AGI. And we've been making a lot of advances in all areas of general understanding of the world. We sometimes call them world models. So, we're particularly proud of our new video model called VO2, which was just released at the end of last year. It's state of the art video generation and it's able to generate these videos just from a text description, right? Or a single static image. So, you know, and actually although some of these videos may not seem that impressive, if you think about this chopping the tomato one, this is like the Turing test for video models because usually you get, you know, the tomato comes magically back together or you're chopping through the fingers or the knife moves off somewhere. And it's actually if you think about what the systems had to do to really understand the physics of the world or this or the bubbles around this blueberry here. Right? It's just generating that from text, you know, blueberries dropping into a glass of water. And it's doing all the physics of this correctly, all the motion of you know, these little cartoon characters or the bee here. It's kind of mind-blowing really and I think even if you told me five years ago that this would be possible without sort of building in some special understanding of physics or something, I would have told you that that seems unlikely that that would be possible. Then yet somehow these learning systems are able to learn about real-world physics just from watching, you know, many many YouTube videos. And it's pretty crazy that that's possible.
So, we've done that. We've gone a step further with Genie 2, which of course bringing my games back in here and this is taking those video models a step further. So, now with a text instruction you can generate a whole game. So, you know, here we at the bottom here we said generate a playable world as a robot in a futuristic city, and then it just comes up with this, and you can control it with the, you know, QWE keys and the arrow keys. At the moment it's only consistent for a few seconds, but we're working to extend that so that the consistency of the game world lasts for many minutes. And so then you've really got what I would call a world model, a really understanding of the real world. And how interactions in that real world work and the physics of the real world work.
Of course we've been working very hard on the safety aspects of this. And from very from the very beginning in 2010, we were working on planning for success even though almost nobody was working on AI back then. We imagined that it would be a 20-year mission, and actually amazingly we're sort of on track 15 years in. And we were sort of planning for success. If we were to build these kinds of transformative systems and technologies, it would come with a lot of responsibility as well to make sure they get deployed in a safe and responsible way. And one of the systems, technology we built is called SynthID, which invisibly watermarks actually using an AI system, an adversarial AI system, slightly adjust the pixels or the text or the audio imperceptibly to the human ear or eye, but it can be detected by a detection system that these were synthetically generated images, whether that's audio, image, or video. And of course it's going to become increasingly important as these technologies become widely deployed that we're able to easily distinguish between synthetically generated images and real images.
So, AI's been, you know, this incredible potential to help with our greatest challenges, from climate to health. But obviously this is going to affect everyone. So I think it's really important that we engage not just it's not just the technologists deciding this, but that we engage with a wide range of stakeholders from society. So I've been really pleased in the last couple of years that one of the consequences of AI becoming mainstream is that many governments have gotten interested in it and many parts of all parts of society. And I think it's been great to see these international summits. Actually the UK hosted the first one in Bletchley Park a couple of years ago bringing together heads of government with academia and civil society to discuss these technologies, how to put the right guardrails on it, how to make sure we embrace the opportunities, but we mitigate the risks that are coming down the line. And I think that's going to become increasingly important given the exponential improvement that we're seeing with these technologies. So my short answer for this is to say, you know, a lot of Silicon Valley, the kind of mantra in Silicon Valley is like move fast and break things. And of course that's created a lot of advances, a lot of the technologies we all use every day today, but I think it's not appropriate in my opinion for this type of transformative technology. I think instead, you know, we should be trying to use the scientific method and approach it with a kind of humility and respect that this kind of technology deserves. And you know, we don't know a lot of things. There are a lot of unknowns around how this technology is going to develop. It's so new. And I think with exceptional sort of care and foresight we can get all the benefits and minimize the downsides of this, but I think only if we start the research and the debate about that now.
So just to end then, we're now building our own big multimodal models that try and take the best of all of these different models I've shown you and put it into one system. We call it the Gemini series. Our latest one's Gemini 2.0 that some of you may have tried, which is state-of-the-art across many leading benchmarks. We're using it to further that I'm very excited about the next generation of assistants. I call it universal assistance. We call it Project Astra, where actually you have it on your phone or some other devices, maybe glasses, and it starts being an assistant that you can take around with you in the real world, and it helps you in everyday life to enrich your life or to make you more productive. And the next step then in AI is combining what I've shown you with AlphaGo, these kinds of agent-based models that are able to efficiently search through and find a good solution to a problem in a limited domain, in this case in games. But we want to actually build those types of search systems and planning systems on top of much more general models like Gemini, these world models that understand how the real world works, and then can plan and achieve things in the real world. And of course that's key to things like robotics working, which I think in the next 2-3 years is going to be a huge area that's going to have massive advances in it.
So I'll just finish then by just having a slight conjecture about what does this all mean if we think back to Turing and all of the work that he did to lay down the foundations of computer science. And I think that if you see the work that we've done, I see myself as a kind of Turing's champion in a way. Like how far can the Turing machines and this idea of classical computing go? And I feel like probably one of the lectures that I took in this room was one of my favorite things to think about is the P equals NP problem, which is a famous problem in computer science of, you know, what sorts of problems are tractable on classical systems. And there's obviously a lot of great work going on in quantum computing systems, mainly here in Cambridge and also at Google we have one of the top quantum computing groups in the world. And there's a lot of things that are thought to require quantum computing to solve. A lot of real world systems that we would like to understand and model. And my conjecture is that actually classical Turing machines, basically classical machines that these types of AI systems are built on, can do a lot more than we previously gave them credit for. And if you think about AlphaFold protein folding, you know, proteins are quantum systems, they operate at the atomic scale and one might think you need quantum simulations to actually be able to find the structures of proteins. And yet we were able to approximate those solutions with our neural networks.
And so I think, you know, one potential idea here is that any pattern that can be generated or found in nature, so it has some real structure, physical structure, can be efficiently discovered and modeled by one of these classical learning algorithms like AlphaFold. And if that turns out to be true, I think that has all sorts of implications for quantum mechanics and actually fundamental physics, which is something that I hope to explore and many of my colleagues hope to explore maybe with the help of these classical systems as well to help us uncover what the true nature of reality might be. And that leads me back to the whole reason why I started my path on AI many, many years ago is that I always believed that AGI built in this way could be the ultimate general purpose tool to understand the universe around us and our place in it. Thank you.
