This is a clip from a conversation with Garry Kasparov from Oct 2019. New full episodes once or twice a week and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=8RVa0THWUWw
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Garry Kasparov is considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, he dominated the chess world, ranking world number 1 for most of those 19 years. While he has many historic matches against human chess players, in the long arc of history he may be remembered for his match again a machine, IBM's Deep Blue. His initial victories and eventual loss to Deep Blue captivated the imagination of the world of what role Artificial Intelligence systems may play in our civilization's future. That excitement inspired an entire generation of AI researchers, including myself, to get into the field. Garry is also a pro-democracy political thinker and leader, a fearless human-rights activist, and author of several books including How Life Imitates Chess which is a book on strategy and decision-making, Winter Is Coming which is a book articulating his opposition to the Putin regime, and Deep Thinking which is a book the role of both artificial intelligence and human intelligence in defining our future.

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In my eyes, that is 1 of the most seminal moments in the history.

Again, I apologize for being romanticizing the notion, but in the history of our civilization, because humans as a civilization for centuries saw chess as the peak of what man can accomplish, of intellectual mastery, right?

And that moment when a machine could beat a human being was inspiring to just an entire, anyone who cares about science, innovation, an entire generation of AI researchers.

And yet, to you that loss, at least if reading your face, seemed like a tragedy, extremely painful.

When you look back at your psychology of that loss, why was it so painful?

Were you not able to see the seminal nature of that moment?

Or was that exactly why it was that painful?

As I already said, losing was painful, physically painful.

And the match I lost in 1997 was not the first match I lost to a machine.

Yeah, that makes all the difference to me.

It's just, now I lost and the reason I was so angry that I just, you know, I had suspicions that my loss was not just a result of my bad play.

So though I played quite poorly, you know, just when you started looking at the games today, I made tons of mistakes, but you know, I had all reasons to believe that, you know, there were other factors that had nothing to do with the game of chess.

We can analyze this match and this is with everything you said.

Is that considering chess, you know, as the sort of, as a pinnacle of intellectual activities was our mistake.

Because, you know, we just thought, oh, it's a game of the highest intellect and it's just, you have to be so intelligent and you could see things that the ordinary mortals could not see.

It's a game and all machines had to do in this game is just to make fewer mistakes, not to solve the game, because the game cannot be solved.

I mean, according to Koval Shannan, the number of legal moves is 10 to the 46th power, too many zeros, so just for any computer to finish the job, you know, in next few billion years, But it doesn't have to.

And I think that's this match actually, and what's happened afterwards with other games, with Go, with Shogi, with video games.

It's a demonstration that machines will always beat humans in what I call closed systems.

The moment you build a closed system, no matter how the system is called, chess, go, shogi, daughter, machines will prevail simply because they will bring down number of mistakes.

Machines don't have to solve it, They just have to, the way they outplay us, it's not by just being more intelligent.

It's just by doing something else, but eventually it's capitalizing on our mistakes.

When you look at the chess machines ratings today, and compare this to Magnus Carlsen, is the same as comparing Ferrari to Usain Bolt.

The gap is, I mean, by chess standards is insane.

It's like difference between Magnus and an ordinary player from an open international tournament.

It's not because machine understanding is better than Magnus Carlsen, but simply because it's steady.

And I think that is what we have to learn from 1997 experience and from further encounters with computers and sort of the current State of affairs was alpha 0, you were beating other machines.

The idea that we can compete with computers in so-called intellectual fields, it was wrong from the very beginning.

By the way, the 1997 match was not the first victory of machines over-

No, actually, I played against first decent chess computers from late 80s.

So I played with the prototype of Deep Blue called Deep Thought in 1989 to Rapid Chess Games in New York.

We played against new chess engines like Fritz and other programs.

And then it was Israeli program junior that appeared in 1995.

I lost 1 match against the computer chess engine in 1994, Rapid Chess.

Some people, you know, tend to forget about it, that I won the first match.

But we made a very important psychological mistake thinking that the reason we lost Blitz matches, 5 minutes games, The reason we lost some of the rapid chess matches, 25 minutes chess, because we didn't have enough time.

If you play a longer match, we will not make the same mistakes.

So yeah, we had more time, but we still make mistakes.

And machines will always be steady and consistent compared to humans' instabilities and inconsistencies.

And today we are at the point where, yes, nobody talks about humans playing against machines.

Now machines can offer handicap to top players and still will be favored.

I think we're just learning that it's no longer human versus machines.

just after leaking my wounds and spending 1 year in just, you know, ruminating so what's happened in this match.

And I knew that though we still could play against the machines.

Though these machines were not weaker, at least, actually probably stronger than Deep Blue.

And by the way, Today, chess app on your mobile phone is probably stronger than DeBlue.

I'm not speaking about chess engines that are so much superior.

And by the way, when you analyze games we played against DeBlue in 1997 on your chess engine, they'll be laughing.

And it also shows that's how chess changed because chess commentators, they look at some of our games like game 4, game 5, brilliant idea.

Now you ask Stockfish, you ask Houdini, you ask Commodore, all the leading chess engines.

Within 30 seconds, they will show you how many mistakes both Gary and D.

Blue made in the game that was trumpeted as a great chess match in

Well, okay, so you've made an interesting, if you can untangle that comment.

So now in retrospect, it was a mistake to see chess as the peak of human intellect.

Nevertheless, that was done for centuries.

By the way, in Europe, because you move to the far East, they will go there.

Again, some of the games like, you know, are board games.

So if I push back a little bit, so now you say that, okay, but it was a mistake to see chess as the epitome, and now there's other things maybe, like language, like conversation, like some of the things that in your view is still way out of reach of computers but inside humans.

Do you think, can you talk about what those things might be?

And do you think just like chess that might fall?

Soon with the same set of approaches, if you look at AlphaZero, the same kind of learning approaches as the machines grow in size?

It's about, again, it's about understanding the difference between closed system and open-ended system.

So you think that key difference, so the board games are closed in terms of the rules, the actions, the state space, everything is just constrained.

You think once you open it, the machines are lost?

Not lost, but again, the effectiveness is very different because machine does not understand the moment it's reaching territory of diminishing returns.

It's the, to put it in a different way, machine doesn't know how to ask right questions.

It can ask questions, but it will never tell you which questions are relevant.

So it's like about the, it's a direction.

So I think it's in human-machine relations, we have to consider our role.

And many people feel uncomfortable that the territory that belongs to us is shrinking.

Eventually, we'll belong to the last few decimal points, but it's like having a very powerful gun.

All you can do there is slightly, you know, alter direction of the bullet, maybe, you know, 0.1 degree of this angle.

But that means a mile away, 10 meters of target.

So that's, we have to recognize that is a certain unique human qualities that machines in a foreseeable future will not be able to reproduce.

And, and the effectiveness of this cooperation, collaboration depends on our understanding what exactly we can bring into the game.

So the greatest danger is when we try to interfere with machine superior knowledge.

So that's why I always say that sometimes you'd rather have, by reading these pictures in radiology, You may probably prefer an experienced nurse rather than having a top professor, because she will not try to interfere with machines' understanding.

So it's very important to know that if machines know how to do better things in 95%, 96% of the territory.

We should not touch it because it's happened.

It's like in chess, recognize, they do it better.

I mean, AlphaZero, It's actually a first step into what you may call AI, because everything that's being called AI today, it's just, it's 1 or another variation of what Claude Shannon characterized as a brute force.

It's a type A machine, whether it's Deep Blue, whether it's Watson, and all these things, the modern technologies that are being trumpeted as AI, it's still brute force.

They keep improving the way to process human generated data.

Now AlphaZero is the first step towards machine produced knowledge, which is By the way, it's quite ironic that the first company that championed that was IBM.

It's the program that played in all the AlphaZero types, so just trying to come up with own strategies.

But because of success of Deep Blue, this project had been not abandoned, but just it was put on hold.

And now we just, you know, it's, you know, it's everybody talks about, about this, the machines generated knowledge, so as revolutionary, and it is, but there's still, you know, many open-ended questions.

Many ideas that AlphaZero generated in chess were quite intriguing.

So I looked at these games not just with interest, but it was quite exciting to learn how machine could actually juggle all the pieces and just play positions with a broken material balance, sacrificing material, always being ahead of other programs, 1 or 2 moves ahead by foreseeing the consequences, not over calculating because machines, other machines were at least as powerful in calculating, but it's having this unique knowledge based on discovered patterns.

Almost something that feels like intuition.

Now, the simple question, if AlphaZero faces superior point, let's say another powerful computer accompanied by a human who could help just to discover certain problems.

Because I already, I look at many AlphaZero games.

I visited their lab, spoke to Demis Kasabis and his team, and I know there's certain weaknesses there.

Now, if these weaknesses are exposed, then the question is, how many games will it take for AlphaZero to correct it?

Even if it keeps losing, it's just, because the whole system is based.

So it's now, imagine, so this is, you can have a human by just making a few tweaks.

And as long as we recognize what is our role, where we can play sort of the most valuable part in this collaboration, so it will help us to understand what are the next steps in human-machine collaboration.


Speaker 0: You're lost to IBM Deep Blue in 1997. In my eyes, that is 1 of the most seminal moments in the history. Again, I apologize for being romanticizing the notion, but in the history of our civilization, because humans as a civilization for centuries saw chess as the peak of what man can accomplish, of intellectual mastery, right? And that moment when a machine could beat a human being was inspiring to just an entire, anyone who cares about science, innovation, an entire generation of AI researchers. And yet, to you that loss, at least if reading your face, seemed like a tragedy, extremely painful.

Like you said, physically painful. Why? When you look back at your psychology of that loss, why was it so painful? Were you not able to see the seminal nature of that moment? Or was that exactly why it was that painful?

Speaker 1: As I already said, losing was painful, physically painful. And the match I lost in 1997 was not the first match I lost to a machine. It was the first match I lost, period. Yeah. That's...

Speaker 0: Oh, wow. Oh, wow.

Speaker 1: Yeah, it's...

Speaker 0: Right.

Speaker 1: Yeah, that makes all the difference to me. Yes. First time I lost. It's just, now I lost and the reason I was so angry that I just, you know, I had suspicions that my loss was not just a result of my bad play. Yes.

So though I played quite poorly, you know, just when you started looking at the games today, I made tons of mistakes, but you know, I had all reasons to believe that, you know, there were other factors that had nothing to do with the game of chess. And that's why I was angry. But look, it was 22 years ago. It's more than the bridge. We can analyze this match and this is with everything you said.

I agree with probably 1 exception. Is that considering chess, you know, as the sort of, as a pinnacle of intellectual activities was our mistake. Because, you know, we just thought, oh, it's a game of the highest intellect and it's just, you have to be so intelligent and you could see things that the ordinary mortals could not see. It's a game and all machines had to do in this game is just to make fewer mistakes, not to solve the game, because the game cannot be solved. I mean, according to Koval Shannan, the number of legal moves is 10 to the 46th power, too many zeros, so just for any computer to finish the job, you know, in next few billion years, But it doesn't have to.

It's all about making fewer mistakes. And I think that's this match actually, and what's happened afterwards with other games, with Go, with Shogi, with video games. It's a demonstration that machines will always beat humans in what I call closed systems. The moment you build a closed system, no matter how the system is called, chess, go, shogi, daughter, machines will prevail simply because they will bring down number of mistakes. Machines don't have to solve it, They just have to, the way they outplay us, it's not by just being more intelligent.

It's just by doing something else, but eventually it's capitalizing on our mistakes. When you look at the chess machines ratings today, and compare this to Magnus Carlsen, is the same as comparing Ferrari to Usain Bolt. The gap is, I mean, by chess standards is insane. 3,400, 3,500 to

Speaker 0: 2,800, 2,850

Speaker 1: on Magnus. It's like difference between Magnus and an ordinary player from an open international tournament. It's not because machine understanding is better than Magnus Carlsen, but simply because it's steady. Machine has steady hand. And I think that is what we have to learn from 1997 experience and from further encounters with computers and sort of the current State of affairs was alpha 0, you were beating other machines.

The idea that we can compete with computers in so-called intellectual fields, it was wrong from the very beginning. By the way, the 1997 match was not the first victory of machines over-

Speaker 0: Over grandmasters. Over grandmasters.

Speaker 1: No, actually, I played against first decent chess computers from late 80s. So I played with the prototype of Deep Blue called Deep Thought in 1989 to Rapid Chess Games in New York. I won handily to both games. We played against new chess engines like Fritz and other programs. And then it was Israeli program junior that appeared in 1995.

Right,

Speaker 0: right, I remember.

Speaker 1: Yeah, so there were several programs. I lost a few games in Blitz. I lost 1 match against the computer chess engine in 1994, Rapid Chess. So I lost 1 game to D. Blue in 1996 match, the match I won.

Some people, you know, tend to forget about it, that I won the first match. Yes. But we made a very important psychological mistake thinking that the reason we lost Blitz matches, 5 minutes games, The reason we lost some of the rapid chess matches, 25 minutes chess, because we didn't have enough time. If you play a longer match, we will not make the same mistakes. Nonsense.

So yeah, we had more time, but we still make mistakes. And machine also has more time. And machines will always be steady and consistent compared to humans' instabilities and inconsistencies. And today we are at the point where, yes, nobody talks about humans playing against machines. Now machines can offer handicap to top players and still will be favored.

I think we're just learning that it's no longer human versus machines. It's about human working with machines. That's what I recognized in

Speaker 0: 1998,

Speaker 1: just after leaking my wounds and spending 1 year in just, you know, ruminating so what's happened in this match. And I knew that though we still could play against the machines. I had 2 more matches in

Speaker 0: 2003,

Speaker 1: playing both Deep Freed and Deep Junior. Both matches ended as a tie. Though these machines were not weaker, at least, actually probably stronger than Deep Blue. And by the way, Today, chess app on your mobile phone is probably stronger than DeBlue.

Speaker 0: Than DeBlue.

Speaker 1: I'm not speaking about chess engines that are so much superior. And by the way, when you analyze games we played against DeBlue in 1997 on your chess engine, they'll be laughing. And it also shows that's how chess changed because chess commentators, they look at some of our games like game 4, game 5, brilliant idea. Now you ask Stockfish, you ask Houdini, you ask Commodore, all the leading chess engines. Within 30 seconds, they will show you how many mistakes both Gary and D.

Blue made in the game that was trumpeted as a great chess match in

Speaker 0: 1997. Well, okay, so you've made an interesting, if you can untangle that comment. So now in retrospect, it was a mistake to see chess as the peak of human intellect. Nevertheless, that was done for centuries. So let me-

Speaker 1: By the way, in Europe, because you move to the far East, they will go there. They had shrunk.

Speaker 0: Games, games.

Speaker 1: Again, some of the games like, you know, are board games. Yes. Yeah, I agree.

Speaker 0: So if I push back a little bit, so now you say that, okay, but it was a mistake to see chess as the epitome, and now there's other things maybe, like language, like conversation, like some of the things that in your view is still way out of reach of computers but inside humans. Do you think, can you talk about what those things might be? And do you think just like chess that might fall? Soon with the same set of approaches, if you look at AlphaZero, the same kind of learning approaches as the machines grow in size?

Speaker 1: No, no, it's not about growing in size. It's about, again, it's about understanding the difference between closed system and open-ended system.

Speaker 0: So you think that key difference, so the board games are closed in terms of the rules, the actions, the state space, everything is just constrained. You think once you open it, the machines are lost?

Speaker 1: Not lost, but again, the effectiveness is very different because machine does not understand the moment it's reaching territory of diminishing returns. It's the, to put it in a different way, machine doesn't know how to ask right questions. It can ask questions, but it will never tell you which questions are relevant. So it's like about the, it's a direction. So I think it's in human-machine relations, we have to consider our role.

And many people feel uncomfortable that the territory that belongs to us is shrinking. I'm saying, so what? Eventually, we'll belong to the last few decimal points, but it's like having a very powerful gun. All you can do there is slightly, you know, alter direction of the bullet, maybe, you know, 0.1 degree of this angle. But that means a mile away, 10 meters of target.

So that's, we have to recognize that is a certain unique human qualities that machines in a foreseeable future will not be able to reproduce. And, and the effectiveness of this cooperation, collaboration depends on our understanding what exactly we can bring into the game. So the greatest danger is when we try to interfere with machine superior knowledge. So that's why I always say that sometimes you'd rather have, by reading these pictures in radiology, You may probably prefer an experienced nurse rather than having a top professor, because she will not try to interfere with machines' understanding. So it's very important to know that if machines know how to do better things in 95%, 96% of the territory.

We should not touch it because it's happened. It's like in chess, recognize, they do it better. See where we can make the difference. You mentioned AlphaZero. I mean, AlphaZero, It's actually a first step into what you may call AI, because everything that's being called AI today, it's just, it's 1 or another variation of what Claude Shannon characterized as a brute force.

It's a type A machine, whether it's Deep Blue, whether it's Watson, and all these things, the modern technologies that are being trumpeted as AI, it's still brute force. All they do is they do optimization. They keep improving the way to process human generated data. Now AlphaZero is the first step towards machine produced knowledge, which is By the way, it's quite ironic that the first company that championed that was IBM. Oh, it's in backgammon.

Speaker 0: Interesting, in backgammon.

Speaker 1: Yes, you should look at IBM. It's a new gammon. It's the

Speaker 0: scientist called Cesaro.

Speaker 1: He's still working at IBM. They had in the early 90s. It's the program that played in all the AlphaZero types, so just trying to come up with own strategies. But because of success of Deep Blue, this project had been not abandoned, but just it was put on hold. And now we just, you know, it's, you know, it's everybody talks about, about this, the machines generated knowledge, so as revolutionary, and it is, but there's still, you know, many open-ended questions.

Yes, AlphaZero generates its own data. Many ideas that AlphaZero generated in chess were quite intriguing. So I looked at these games not just with interest, but it was quite exciting to learn how machine could actually juggle all the pieces and just play positions with a broken material balance, sacrificing material, always being ahead of other programs, 1 or 2 moves ahead by foreseeing the consequences, not over calculating because machines, other machines were at least as powerful in calculating, but it's having this unique knowledge based on discovered patterns. After playing 60 million games.

Speaker 0: Almost something that feels like intuition.

Speaker 1: Exactly, but there's 1 problem. Now, the simple question, if AlphaZero faces superior point, let's say another powerful computer accompanied by a human who could help just to discover certain problems. Because I already, I look at many AlphaZero games. I visited their lab, spoke to Demis Kasabis and his team, and I know there's certain weaknesses there. Now, if these weaknesses are exposed, then the question is, how many games will it take for AlphaZero to correct it?

The answer is hundreds of thousands. Even if it keeps losing, it's just, because the whole system is based. So it's now, imagine, so this is, you can have a human by just making a few tweaks. So humans are still more flexible. And as long as we recognize what is our role, where we can play sort of the most valuable part in this collaboration, so it will help us to understand what are the next steps in human-machine collaboration.

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Garry Kasparov: IBM Deep Blue, AlphaZero, and the Limits of AI in Open Systems | AI Podcast Clips