# DeepMind's Demis Hassabis on the future of AI

Segment00:05:00 – 00:10:00

Full episode transcript, trimmed to training segment.

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[0005:35] Steven Johnson: Um, like most 14 years olds do. Most 14-year-olds are just like, “I'll just call up the CEO.”

[0005:38] Demis Hassabis: I think yeah. Yeah. So, so he was fast, you know, fascinated by what I was doing. And then, uh, rapidly I ended up taking, you know, some time off between school and universities. And I used that time to program Theme Park, like you said. And actually at the time, in the mid-nineties, it was the golden era of games, uh, design and fantastic creativity going on, but also a lot of the best technology was being developed as part of games, graphics, technology, but also AI.

And, um, all the games I've written, including the two you mentioned, Theme Park and Black and White, have all had AI as the core gameplay component so that the game actually sort of reacts to you and the way that you play as an individual.

[0006:18] Steven Johnson: It's such an interesting history, the, those simulation games. I think when, when you're dealing with, you know, managing resources, trying to set goals for yourself, trying to deal with, you know, multiple layers of the, of the simulation…

You know, kind of starting with Sim City and then going through games like Theme Park and, and Black and White, to me, it's an argument I've been making for many, many years that, that those should be taught in schools. I mean, it's an incredibly rich way of thinking and it's very different from the kind of thinking you do when you read a novel or the kind of thinking you do when you solve a math problem.

Uh, but it's actually, it aligns with a lot of the kind of thinking that one has to do in life. Probably more, more than some of those other fields.

[0006:56] Demis Hassabis: I agree. I agree. Yeah, I totally agree. And, and, and actually, I mean, I think, first of all, chess should be taught as part of the school curriculum, I think ‘cause it teaches you phenomenal skills you don't learn, I think, that are generalizable and transferable to other parts of life, like planning and visualization and, um, but also I agree with you with these types of simulation games.

You can call them sandboxes even. So the idea is, you know, there's almost like a playpen for your creativity as a gamer. So very different from normal games where the game leads you by the hand through it. Um, and Theme Park, you know, the idea behind that was you designed your own Disney World. And, uh, thousands of little people, AI people, came into your theme park and played on the rides, and, and depending on how well you designed the theme park, they were happy or less happy.

And then of course, if they were happy, you could charge them more in the burger stands and the, for the cokes and, and balloons and other things. So the whole economics mold onto there. So yeah, it's, it was, it was a really interesting and formative experience for me, I would say, and not only professionally, but also demonstrating to me the power of AI.

Uh, and in those days this was just sort of fairly sort of traditional AI right? But obviously, uh, uh, deployed within a game of finite state machines and other things. Not like the kind of AI we build today, but it was still amazing to me how much enjoyment people got from interacting with a game like that, that, um, had AI at its core.

[0008:15] Steven Johnson: We're gonna get into this in more detail, but Deep, DeepMind has a long history in involving algorithms that has developed to play games. Um, but as far as I know, none of them have been simulation games, right? I mean, it's kind of Space Invaders and Q*bert and StarCraft and things like that. But, but there isn't any simulated Black and White players, uh, in the, in, in, in the cannon over there at DeepMind.

Is there a reason for that? I mean, in a way it's, it's, it's kind of the, the archetypal vision of a future AI that we have in our heads that, you know, we would have some artificial intelligence that will manage the city for us very effectively. So that presumably is where we want to go. But, but you haven't done that yet, right?

[0008:58] Demis Hassabis: No. No, you're right. That's an interesting observation. And actually, there's almost three chapters in my life of games being important to my, my life and career. One is the chess, uh, and the sort of my youth. Then there was designing and, and writing professional video games. And then finally this third chapter of using games at DeepMind from the beginning as part of the thesis of DeepMind and simulations as a training ground for AI systems.

A very convenient training ground for many reasons. Obviously, you can run millions of simulations at once in the cloud. You don't have to deal with things like real robotics, which is, you know, often you end up worrying about the hardware, breaking the motors and other things. So, it was something that was, uh, I thought was the perfect training ground for AI systems to make quick progress.

And of course, the other nice thing about games is, um, you know, game designers and games companies have spent thousands of person-years making these things and they're challenging for human players, right? That was obviously, that's obviously their challenging and fun for human players to play. And you can kind of go up the stack of difficulty even in computer games.

So we started, kind of famously now, with Atari games, you know, probably the earliest computer games that sort of, you know, became into the mainstream from, from the seventies and eighties. Space Invaders, Pong, these classic games. And that was difficult enough already for us back in 2013, 2012. I remember we were, we couldn't, we couldn't win a point at Pong.