EP 3: Playing the Wrong Game

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0:36It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required compatibility and availability varies 18+. This is The Last Invention. I'm Gregory Warner. Kasparov has played knight B8 to D7, which is a move that his arch rival Anatoly Karpov... On a spring day in 1997, millions of people around the world, myself included, watched one of the most unusual chess matches in history.

1:16In one corner, weighing 176 pounds, considered by many the greatest player in the history of the game. A battle between the world's reigning champion, Garry Kasparov. And in the other corner, weighing 1.4 tons, the new and improved RS6000 SP supercomputer. And the IBM supercomputer created to beat him, Deep Blue. The battle of man against machine. First move of this epic sixth game has been played. Deep Blue has played E2 to E3. Now, the reason it was getting so much attention, even from a lot of people who usually are not into chess. For some, watching chess might be tantamount to watching paint drive.

1:58However, it's actually been very entertaining. Look at Gary, that jacket opened up a little bit there.

2:07Was the fact that Gary Kasparov wasn't just another world chess champion. He was the world champion. And at this time in history, he had never lost a single championship match. It's challenging Kasparov in any way. I mean, he's the pinnacle of chess. He's an incredible genius. And on the other side of the board, IBM had invested millions of dollars and years of research into creating Deep Blue. They had specially designed chips that could analyze up to 200 million different chess positions per second. And they said that they were finally ready to take on the very best human at this game that in many ways has become a symbol of human intelligence.

2:52And now there's all kinds of problems. Notice that this bishop on C6, the bishop can easily fall victim to what we call an overload tactic. And this game was intense. I remember watching it on TV at the time, looking at Kasparov, running his hand through his hair, getting up, walking around the room, coming back to the board, concentrating on each and every move he made. While the machine is just this glowing screen, very methodical, making its moves quickly and decisively. He looks disgusted, in fact. He looks just like he can't believe what's going on right now. He is an unhappy camper. And in the final match, our human champion fell into a trap.

3:37And whoa! Kasparov has resigned.

3:45In an absolutely stunning, stunning 19-mover, Kasparov has just simply stormed away. Machine didn't just beat man, but trounced him. Newsweek magazine called it the brain's last stand. The game of chess, supposedly a true test of human intellect, will never be the same again. Now for a while, it did feel like we were witnessing a moment of profound change. Call it a blow against humanity. The victory seemed to raise all those old fears of superhuman machines crushing the human spirit.

4:27but pretty quickly while it was a very impressive feat that the programmers at ibm had pulled off it wasn't actually transformative i mean didn't really change chess in the years since chess has only become more popular both in the amount of chess people are playing in the viewership of chess competitions human to human and in the world outside of chess this did not usher in an age of competition between humans and true thinking machines. And while some of us looked at this and shrugged or perhaps breathed a sigh of relief, there were those who looked at this moment and thought, we were playing the wrong game.

5:04Although I was fascinated by these early chess programs, that they could do that, I was also slightly disappointed by them. Because Deep Blue, even though it was a pinnacle of AI at the time, it did not seem intelligent. That way that Deep Blue beat Kasparov was nothing like how human chess players do it. I was actually more impressed with Kasparov's mind than I was with the machine, because he was this brute of a machine. All it can do is play chess, but it couldn't even play tic-tac-toe. And then Kasparov can play chess, but also can do all the other things that humans can do. So I thought, you know, doesn't that speak to the wonderfulness of the human mind?

5:42For today, the games and the gamers that brought us the AI revolution. And the small band of contrarian scientists determined to make an AI that didn't think like a machine.

6:01Okay, Annie Mills. Gregory Warner. Let's talk about games. I actually think one of the interesting ways of following the path of AI is, funny enough, through games. So I originally got into this connection between games and the history of AI through Liv Burry. My name is Liv Burry, and I used to be a professional poker player for a long time. My original background is actually in physics. She herself is very good at games. She's famous for being a world champion poker player. We met her in episode one, right? Yeah. She spent a good chunk of the last decade publicly advocating for AI safety. And that's in part because she's not just a game player, but she's a game theorist.

6:38Being able to solve a game is being able to understand a particular environment where you have different objectives, different scoring metrics, perhaps the environment can be changing, and being able to adapt in order to be the best at that goal. So as artificial intelligence gets better at game theory, essentially, in increasingly complex and increasingly real-life environments, then they are getting better at navigating the world. And that's really the trajectory we're seeing. When you hear people talk about AGI, this idea of artificial general intelligence, what they're really saying is an agent that is as good as a human generally is at navigating all of the different things in this world that we live in.

7:25So on the one hand, games are this very practical kind of benchmark, right? Like just a way for computer scientists to test how their system is doing. But as the games get more and more complex, they are getting closer to that holy grail of a thinking machine. Yeah, that's the idea. And it goes back all the way to the 1950s to the first generation of AI researchers. Some of the earliest computer programs were built to try and play basic games. Tomorrow, a preview of the future as it begins to take shape in the laboratories of the world. One of the earliest ones came from the UK. It was a system that played tic-tac-toe, but because it was the UK, they called it Knots and Crosses.

8:10In his spare time, engineer D.W. Davis built an automatic Knots and Crosses machine that thinks for itself. By its own effort, it selects from the 6 ,045 alternatives the one that always wins. And then over the rest of the 20th century and into the 21st century, we saw games of increasingly more complexity be defeated by computers to the point of superhuman level. Fast forward to the 1970s, you've got an AI system that can play checkers. That man isn't playing checkers against a computer, is he? Sure, and it plays pretty well. Sometimes even better than the man who designed them. After that comes backgammon.

8:47Then in the 90s, you get the famous chess match. IBM's deep blue computer demolished the greatest chess player ever, Gary Kasparov, in the final and decisive game of their match. And then by the time you get to 2011, you've got an AI system that challenged humans to what was seen at the time as the most ambitious game yet. This is Jeopardy! the IBM Challenge. And now, here is the host of Jeopardy! Alex Trebek. So IBM, they're back again, and they're doubling down on the same strategy that they used with Deep Blue to win in chess. They've poured millions of dollars in years of research into winning Jeopardy because as they see it, this is an even more complicated challenge for an artificial intelligence.

9:38Language is an area where from the very beginning of the computer era, people kept expecting computers to do reasonably well at. They expected computers could talk. And so far, the computers have failed to deliver on this promise. On top of having to answer questions on any different subject, you know, history, pop culture, philosophy, this system will have to speak and understand language. A little over three years ago, the folks at IBM came to us with a proposal that they considered to be the next grand challenge in computing. And that was designing a computer system that could understand the complexities of natural language well enough to compete against Jeopardy's best players.

10:20Jeopardy and IBM, they hype up this big three-night showdown. So you are about to witness what may prove to be an historic competition. Where the AI system Watson is going to go head-to-head against two of the best players in Jeopardy history. Ladies and gentlemen, this is Watson. Let's play Jeopardy. Here we go.

10:46And at first, things start off a bit rocky for Watson, the human players, their neck and neck. Stylish elegance or students who all graduated in the same year. Watson. What is chic? No, sorry. What is class? Class, you got it. But by the second night of this three-night special... And any time you feel the pain, hey, this guy, refrain. Don't carry the world upon your shoulders. Watson. Who is Jude? Yes. Watson starts crushing the humans. Losing to him by one hundredth of a second. Watson. Who is Michael Phelps? Yes. Black hole's boundary from which matter cannot escape. Watson. What is Event Horizon?

11:28Yes. Watson. Who is Grendel? Yes. Watson. What is Glass Judgment? Correct. What is London? Correct. What is Stick? Stick is right. And with that, you add your lead. You're at five.

11:46And just like with Deep Blue beating the world's best chess player, there was this moment afterwards where it felt like we might really be witnessing some kind of milestone. IBM says the technology could help speed up medical diagnosis and other challenging. IBM, they put out this documentary saying this is going to be transformational. All these things are now going to be possible. Of course, this whole project is not ultimately about playing Jeopardy. It's about doing research and deep analytics and a natural language understanding. This is about taking the technology and applying it to solve problems people really care about.

12:25We're just so excited about all the things we can do with this. But again, just like with Deep Blue, the strategy was very good at winning a complicated game, but it failed to live up to the hype. It failed to lead to anything very useful outside of the world of that game. Okay, so you're saying that for decades, computer scientists were testing their AI systems against the world of games under the theory that as the games got more complex and as the skills they had to program in became more interesting, that somehow those skills would translate into the real world. And yet, none of these AI systems that can play these games can make the jump into real life.

13:12Yeah, they can't make that jump that Liv Burry was talking about. Their intelligence does not transcend into the real world. Exactly. And what is the theory for why? Well, it comes down to the strategy, to the way that these AI systems were built to win in these different games, which relies on a massive amount of engineering. Deep Blue, for example, was programmed, essentially hand-coded, with all the rules of chess, with the millions and millions of possible chess positions they might encounter coming from all the world's best chess books. Or the same with Watson. It's programmed on all these encyclopedias.

13:49And then during the game, they're essentially just running algorithms to try and retrieve the possible right answer or the possible right move as fast as they can. And do you remember what were you thinking back in 1997 when Garry Kasparov gets beat by IBM's Deep Blue and there's all this excitement about what's going to happen next? Nothing much because we knew that it was just brute force search, which is a classical AI technique that is very unlike human intelligence. But it turns out that all of this time that all the attention was being paid to Watson and Deep Blue, there were some AI researchers like Joshua Bengio.

14:26That way that Deep Blue beat Casper up was nothing like how human chess players do it. and this small band of AI researcher outsiders who were essentially shaking their heads, saying that these AI systems, they are not doing what anyone would consider thinking, and they thought that they had a better way. Would you describe yourself as a bit of a contrarian? I'm tempted to disagree with that, but I think you might be right. And one of them that would turn out to be the most consequential was a guy named Jeffrey Hinton. Well, let's just get into it. First off, can you just introduce yourself? What's your name and what title do you go by these days?

15:10My name's Jeffrey Hinton. I've been doing research on neural networks since 1972. That's a little over 50 years. And for a long time, this was regarded as crazy. And more recently, it's turned out it works much better than symbolic AI. This is the same Jeffrey Hinton who quit his job at Google in 2023 very publicly to warn the world about the existential risk of AI. Yes, it is. He is now a Nobel Prize winner for his work on artificial intelligence. Many call him the godfather of AI. But before he quit his job at Google, before he even had his job at Google, for much of Hinton's career, his ideas, his strategies, his approach was resoundingly rejected by almost all of his peers.

16:01I had a very smart student wanted to do graduate work with me. And one of the other professors in my department told him, oh, don't work with Hinton. That'll be the end of your career. It's a dead end. I'm sorry to laugh. I just know what happens at the end of the story makes it ridiculous. But what did that feel like at the time? My view is you shouldn't give up on an idea that goes against the grain until you understand why it's wrong. So Hinton, he initially started as kind of ostracized by the AI community because he was working on the approach that most people thought was a dud. Unsurprisingly, Hinton's name and his backstory came up in almost every conversation that I had for the series, including with the author Karen Howe.

16:46He just felt very strongly about it, in part because he originally started studying AI, not because he wanted to recreate human intelligence, but actually because he wanted to understand human intelligence better. So he was interested in it from the perspective of if we successfully create intelligent systems in computers, that will enable us to better understand our own intelligence. And one of the things that's so cool about Hinton is that he always studied artificial intelligence alongside brain science because he had this deep-seated belief that the two were intrinsically linked. And so he was coming from a more neuroscience background, and he strongly felt that if we can create software that mimics the processing power of the brain, surely we will be able to get to some kind of intelligent system.

17:38So the idea is that the path to intelligence, real intelligence, to get the machine to think like a human brain, it needs to be structured like a brain. Like the same way we have a bunch of neurons in our brains all talking to each other, that's what they're going to kind of design for this computer. Yes, this is how you get the approach called neural networks or neural nets, which is basically that. It's an AI system with all these different layers and layers of artificial neurons, and they fire and they change in a way that kind of mimics or mirrors the way that neurons fire in our brains. So my aim has always been to understand how the brain works.

18:14But in our attempts to understand how the brain works, we've developed this technology, which is amazing. I thought, wow, this is really cool. Why don't we take inspiration from human brains to figure out how to do AI? Joshua Bengio is, like Hinton, now one of the most decorated and celebrated AI researchers in his field. He's won the Turing Award. He's actually the most cited living scientist on Earth right now. But like Hinton, most of his career was full of rejection. My papers got rejected because it were about neural nets. And my students didn't want to work on neural nets because they were afraid they wouldn't get a job.

18:54And why were you so committed to this? Why not just follow the mainstream AI models? Some scientists, at least I and many others I know, have an emotional relationship with ideas. You get really excited about something and you feel strongly that this is the path. If you want to be honest, you know you can't be sure, but still you have this strong feeling. These emotions is what allowed us to go through the times when it was maybe difficult to work on these topics. Walk me through like the 80s into the 90s. What was it like to study this? Did it feel like it was fringe? Like what language should we use to accurately describe what you were up to?

19:38Fringe is quite good. There was a period, even quite late on, even in the 2000s, when people were saying things like, this paper's about neural networks, it shouldn't be submitted to a machine learning conference. Like it's not even worth submitting. We don't want that kind of stuff in machine learning. It's obvious nonsense, and machine learning shouldn't pay any attention to it. Here is what I'm not getting, right? If AI, from its origins, from the beginning of the term in 1956, even earlier with Alan Turing, the whole idea of AI was to mimic human intelligence. The brain is our thinking organ, and this camp had a way to mimic the brain.

20:17So why was that idea, why were they out in the cold? Part of the reason is almost a philosophical resistance to this idea among AI researchers. Because from that 1956 summer program where that debate emerged between the symbolists who want to make expert systems and the connectionists who want to make these AI toddlers, AI babies, the expert system side just totally dominated partly because the systems they made were just better at doing things that looked like intelligence. For decades, higher intelligence, as in what mathematicians do or physicists or people who play chess and so on and win tournaments, that was considered the peak of human intelligence.

21:09Like an AI system that can beat a chess master, an AI system that can stomp two nerds in jeopardy. That must be smart. That is what intelligence looks like to us. And Bingio and Hinton and their side, they're over there saying, no, no, no, intelligence is a toddler. Intelligence is a four-year-old. Wait a second. We need to build the foundations first. And the foundations for human intelligence is the intelligence of a one-year-old. And you don't spoon feed a one-year-old with mathematical formulae, right? You let him experience life. You show him things. But they also had a couple of very serious technical obstacles in their way as well.

21:53For many, many years, most people claimed it was crazy for not completely unrealistic reasons. The biggest one was that for decades, critics of Hinton and this neural net approach, they would say, if your system is going to learn on its own, learn its own patterns, what are you going to do when it learns the wrong thing? They said a big network of brain cells with random connection strengths in them will never learn to do anything interesting. If you try just tinkering with the connection strengths to make it behave better, you'll get stuck in what's called a local optimum. And the metaphor that Hinton uses for this problem is to imagine a hiker on a huge mountain range with a simple mission, climb to the tallest peak.

22:38And the hiker has one rule, always go up. And this works fine all the way until they reach the top of a smaller mountain peak. and at that point, every direction that they can go is down, right? And so from the hiker's perspective, it thinks it's on top of the world when in reality, there's a much larger peak nearby. It's like a mountain range where you get trapped on a peak and you can't get to the higher peaks because you have to go downhill to get to the higher peaks. And if you just try going uphill, you'll be trapped on this local peak and you'll never really get anywhere. So it's like the hiker has learned the terrain, It's figured out the mountain.

23:18It's actually been able to climb all this distance, but it cannot figure out how to go back down the path in order to take the right trail to the even higher peak. Yes, to retrace its steps and to find a different solution, so to speak, to correct an error. And this was a huge problem. And one of the reasons that Hinton and Bengio are referred to as godfathers of AI, one of the reasons that they are legends in their field is because despite all of the naysayers, they continued to go back to their labs. They kept doing their research, trying to solve problems like this. And finally they did. And it's a crazy story.

23:58They used this old, largely forgotten algorithmic system called backpropagation. And they were able to give these neural nets a way to metaphorically retrace their steps, go back to where they started from and start climbing again. In a sense, the machine could now learn from its mistakes. Got it. So it's like backpropagation is like a math way of saying, hey, go back and correct your error. Like the network can now escape the small hills, find its way to scale up to the real mountains, so to speak. And this would become revolutionary in theory. But the trouble was when they discovered it, they were still running into two other very persistent problems.

24:43One of them was that they just needed an insane amount of computing power. One aspect is computers were small and slow relative to what they are now. If you tried using neural networks, you couldn't get them to do much. You can imagine a digital brain firing with digital neurons, trying to not only learn patterns, but go back and learn from its mistakes. That's going to take a lot more computing power than you could get from a 1980s or 1990s IBM, right? And you also need just an insane amount of data for this AI system to be combing through and learning inside of and making mistakes and learning again.

25:22And therefore, for years, they continued to live on the fringes of AI research to watch as IBM, Watson, Deep Blue get all the money, get all the attention. And then came the year 2012, when they finally had their chance to completely flip this dynamic. A huge breakthrough came at Jeff Hinton's lab in 2012. And that came in the form of a game called ImageNet. The ImageNet Challenge. I talked about this with the writer Jasmine Sun, who's working on a book about AI right now. Which is this grand challenge with a huge data set of images. And with the Wall Street Journal's Keechegi. This contest that had been running for many years.