Demis Hassabis

Yes. Well, look, of course we started DeepMind in 2010 before anyone was working on this in industry and there was barely any work on it in academia. And we partially named the company DeepMind, the deep part because of deep learning. It was also a nod to deep thought in Hitchhiker's Guide to the Galaxy and Deep Blue and other AI things.

Yes. Well, look, of course we started DeepMind in 2010 before anyone was working on this in industry and there was barely any work on it in academia. And we partially named the company DeepMind, the deep part because of deep learning. It was also a nod to deep thought in Hitchhiker's Guide to the Galaxy and Deep Blue and other AI things.

Yes. Well, look, of course we started DeepMind in 2010 before anyone was working on this in industry and there was barely any work on it in academia. And we partially named the company DeepMind, the deep part because of deep learning. It was also a nod to deep thought in Hitchhiker's Guide to the Galaxy and Deep Blue and other AI things.

But it was mostly around the idea we were better on these learning techniques Deep learning and hierarchical neural networks, they've just sort of been invented in seminal work by Jeff Hinton and colleagues in 2006. So it's very, very new.

But it was mostly around the idea we were better on these learning techniques Deep learning and hierarchical neural networks, they've just sort of been invented in seminal work by Jeff Hinton and colleagues in 2006. So it's very, very new.

But it was mostly around the idea we were better on these learning techniques Deep learning and hierarchical neural networks, they've just sort of been invented in seminal work by Jeff Hinton and colleagues in 2006. So it's very, very new.

And reinforcement learning, which has always been a speciality of DeepMind, and the idea of learning from trial and error, learning from your experience, and making plans and acting in the world. And we combine those two things, really, we sort of pioneered doing that, and we called it deep reinforcement learning, these two approaches.

And reinforcement learning, which has always been a speciality of DeepMind, and the idea of learning from trial and error, learning from your experience, and making plans and acting in the world. And we combine those two things, really, we sort of pioneered doing that, and we called it deep reinforcement learning, these two approaches.

And reinforcement learning, which has always been a speciality of DeepMind, and the idea of learning from trial and error, learning from your experience, and making plans and acting in the world. And we combine those two things, really, we sort of pioneered doing that, and we called it deep reinforcement learning, these two approaches.

And deep learning to kind of build a model of the environment or what you were doing, in this case, a game, and then the reinforcement learning to do the planning and the acting and actually accomplish it, be able to build agent systems that could accomplish goals. In the case of games is maximizing the score, winning the game.

And deep learning to kind of build a model of the environment or what you were doing, in this case, a game, and then the reinforcement learning to do the planning and the acting and actually accomplish it, be able to build agent systems that could accomplish goals. In the case of games is maximizing the score, winning the game.

And deep learning to kind of build a model of the environment or what you were doing, in this case, a game, and then the reinforcement learning to do the planning and the acting and actually accomplish it, be able to build agent systems that could accomplish goals. In the case of games is maximizing the score, winning the game.

And we felt that that was actually the entirety of what's needed for intelligence. And the reason that we sort of were pretty confident about that is actually from using the brain as an example, right? Basically, those are the two major components of, of how the brain works. You know, your, your, the brain is a neural network.

And we felt that that was actually the entirety of what's needed for intelligence. And the reason that we sort of were pretty confident about that is actually from using the brain as an example, right? Basically, those are the two major components of, of how the brain works. You know, your, your, the brain is a neural network.

And we felt that that was actually the entirety of what's needed for intelligence. And the reason that we sort of were pretty confident about that is actually from using the brain as an example, right? Basically, those are the two major components of, of how the brain works. You know, your, your, the brain is a neural network.

It's a pattern matching and, and structure finding a system, but then it also has reinforcement learning and this idea of planning and learning from trial and error and trying to maximize reward, which is actually in the, in the human brain and the animal brain, a mammal brain is the dopamine system, uh, implements that a form of reinforcement learning called TD learning.

It's a pattern matching and, and structure finding a system, but then it also has reinforcement learning and this idea of planning and learning from trial and error and trying to maximize reward, which is actually in the, in the human brain and the animal brain, a mammal brain is the dopamine system, uh, implements that a form of reinforcement learning called TD learning.

It's a pattern matching and, and structure finding a system, but then it also has reinforcement learning and this idea of planning and learning from trial and error and trying to maximize reward, which is actually in the, in the human brain and the animal brain, a mammal brain is the dopamine system, uh, implements that a form of reinforcement learning called TD learning.

So that gave us confidence that if we pushed hard enough in this direction, even though no one was really doing that, that eventually this should work, right? Because we have the existence proof of the human mind. And of course, that's why I also studied neuroscience, because when you're in the desert, like you say, you need any source of water or any evidence that you might get out of the desert.

So that gave us confidence that if we pushed hard enough in this direction, even though no one was really doing that, that eventually this should work, right? Because we have the existence proof of the human mind. And of course, that's why I also studied neuroscience, because when you're in the desert, like you say, you need any source of water or any evidence that you might get out of the desert.