 Live from New York, it's theCUBE, covering machine learning everywhere. Build your ladder to AI, brought to you by IBM. Welcome back here to New York City as we continue at IBM's Machine Learning Everywhere. Build your ladder to AI along with Dave Vellante and John Walls, and it is now a great honor of ours to have, I think probably, and arguably the greatest chess player of all time. Gary Kasparov, now joins us. He's currently the chairman of the Human Rights Foundation, political activists in Russia as well, some time ago. But thank you for joining us. We really appreciate the time. Thanks for inviting me. Yeah, but we've been looking forward to this. Let's just, if you would, set the stage for us. Artificial intelligence, obviously, quite a hot topic. The maybe not conflict, the complementary nature of human intelligence. There are people on both sides of the camp, but you see them as being very complementary to one another. I think that's natural development in this industry that will bring together humans and machines because this collaboration will produce the best results. These, our abilities are complementary. So the humans will bring creativity, intuition, and other typical human qualities, like human judgment and strategic vision, while machines will add calculation, memory, and many other abilities that they've been acquiring lately. So there's room for both, right? Yes, I think it's inevitable because no machine will ever reach 100% perfection. So machines will be coming closer and closer, 90%, 92%, 90, through 95, but there's still room for humans because at the end of the day, even with this massive power, you have to guide it. You just have to evaluate the results. And at the end of the day, machine will never understand when it reaches the territory of diminishing returns. So it's very important for humans actually to identify. So what is the task? I think it's a mistake that is made by many pundits that they just try, they automatically transfer the machine's expertise from the closed systems into the open-ended systems. Because in every closed system, whether it's the game of chess, the game of God, video games like Dota, or anything else where humans already define the parameters of the problem, machines will perform phenomenally. But if it's an open-ended system, then a machine will never identify just what is the right question to be asked. Don't hate me for this question, but it's been reported, not if it's true enough, but at one point you said you would never lose to a machine. My question is, how capable can we make machines? First of all, is that true? Did you maybe underestimate the power of computers? And how capable do you think we can actually make machines? Look, in the 80s when the question was asked, I was much more optimistic because we saw very little at that time from machines that could make me, the world chairman at the time, worry about machines' capability of defeating me in the real chess game. Okay, I underestimated the pace of this development. I could see something was happening, was cooking, but I thought it would take longer for machines to catch up. But as I said in my talk here, is that we should simply recognize the fact that everything we do while knowing how we do that, machines will do better. Any particular task that humans perform, machine will eventually surpass us. So what I love about your story, I was telling you off camera about when we had Eric Benioffson and Andrew McAfee on it, you're the opposite of Samuel P. Langley to me. You know who Samuel P. Langley is? No, please. Samuel P. Langley, do you know who Samuel P. Langley is? He was the gentleman, that you guys all love this, that the government paid, I think it was $50,000 at the time, to create a flying machine. But the Wright brothers beat him to it. So what did Samuel P. Langley do after the Wright brothers succeeded? He quit. But after you lost to the machine, you said, you know what, I can beat the machine with other humans and created what is now the best chess player in the world is my understanding, it's not a machine, but it's a combination of machines in humans. Is that accurate? Yes, in chess actually we could demonstrate how the collaboration can work. And now in many areas, people rely on the lessons that have been revealed, learned from this what I call advanced chess. That in this team, human plus machine, the most important element of success is not the strengths of the human expert. It's not the speed of the machine, but it's a process. It's an interface. So how you actually make them work together. And in the future, I think that will be the key of success because we have very powerful machines, those AIs, intelligent algorithms, and all of them will require very special treatment. That's why also I use this analogy with the right few for Ferrari. We will have experts, operators, I even call them the shepherds, that will have to know exactly what are the requirements of this machine or that machine or that group of algorithms to guarantee that we'll be able, by our human input, to compensate for their deficiencies, not out of around. What led you to that response? Was it your competitiveness? Was it your vision of machines and humans working together? Oh, look, I thought I could last longer as the undefeated world champion. So in 1990, no, it's ironically, in 1997, it's when you just look at the game, at the quality of the game. And try to evaluate the deep blue real strengths. I think I was objective, I was stronger because today you can analyze these games with much more powerful computers. I mean, is there any chess app on your laptop? I mean, you can only compare with deep blue. It's just, yeah, that's natural progress. But as I said, it's not about solving the game. It's not about objective strengths. It's about your ability to actually perform at the board. And I just realized that while we could compete with machines for a few more years, and that's, it did take place. I played two more matches in 2003 with Israel and German program, not as publicized as IBM match. Both ended as a tie. And I think they were probably stronger than the blue, but I knew it would be just, it would be over, maybe a decade. But so how can we make chess relevant? And it was, for me, it was very natural. So I could see this immense power of calculations, brute force. And on the other side, so I could see us, just having qualities that machines will never acquire. So how about bringing it together and just using chess as a laboratory to find some of the most productive ways for human machine collaboration? Yeah, what was the difference in, I guess, processing power, basically or processing capabilities? You played the match. This is 1997, deep blue. You played the match on standard time controls, so which allow you or a player a certain amount of time. How much time did deep blue, did the machine take, or did it take its full time to make considerations, as opposed to what you exercised? Look, it's, when you say standard time control, I think you should explain to the audience that it's, at that time, it was seven hours game. Right. So we have other, it's what we call classical chess. We have rapid chess that is under one hour, and then you have bliss chess, which is five to 10 minutes. So that was a normal time control. It's worse for some mentioning that other computers, they were beating human players, myself included, in bliss chess, in a very fast form of chess. So we still thought that more time was more time. We could have sort of a bigger comfort zone, just to contemplate so the machine's plans and actually to create real problems that machine would not be able to solve. Again, more time helps humans, but at the end of the day, it's still about you, your ability, not to crack under pressure, because there's so many things that are just, you know, that could take you off of your balance, and machine doesn't care about it. So at the end of the day, machine has a steady hand. And steady hand wins. Because emotion doesn't play our order. It's not about absolute strength, but it's about guaranteeing that it will play at a certain level for the entire game, while human game, maybe at one point it could go a bit higher, but at the end of the day, when you look at the average, it's still lower. I played many world championship matches, and I analyzed these games, and games played at the highest level. And I can tell you that even the best games played by humans at the highest level, they include not necessarily big mistakes, but inaccuracies that are irrelevant when humans facing humans, because I make a mistake, a tiny mistake, then I can expect you to return the favor. Against the machine is just, that's it. So it's the humans cannot play at the same level throughout the whole game. It's the concentration, the vigilance, and that's required when we, after humans face humans. So there's a psychologically, if you have a strong machine, machine is good enough to play with a steady hand, the game is over. I want to point out too, just so we get the record straight for people might not be intimately familiar with your record. I mean, you were ranked number one in the world from 1986 to 2005 for all but three months. Yeah. Three months. Three decades. No, two decades. Well, 80s, 90s and on. Okay, yes. I'll give you that. Okay, okay. Yeah, it was just, it was, yeah. It's the, I mean, that is, that's unheard of. That's phenomenal. It's the, it's the, then just going back to your previous question about, you know, why I just look for some, so for new form of chess. It's a, this is one of the key lessons that I learned from my childhood. Thanks to my mother who spent her life, you know, just helping me to become who I am, who I was after my father died when I was seven. It's the, it's a lesson it's about, it's about always trying to make the difference. It's not just about winning, it's about making the difference. And it led me to just to kind of new motto in my professional life. That is, it's all about my own quality of the game. So as long as I'm challenging my own excellence, I will never be short of the opponents. And for me, this is, this defeat was just, you know, just now it's, it was just a kick, a push. So let's come up with something new. Let's find a new challenge. Let's find a way to turn this defeat, the lessons from this defeat into something more practical. Love it. I mean, I think in your book, you, you, I think it was, was it John Henry? Yeah. Right, the example, right. The famous example. John Henry of chess. That's what you want. I lost, but that's right. He won, but he lost. But the motivation wasn't competition. It was, it was advancing society and creativity. And so that's, I love it. The other thing, I just, quick aside, you mentioned, you know, performing under pressure. I think it was in the 1980s. It might have been the opening of your book. You talked about playing multiple computers. Yeah, it's, it's 1985. In 1985. And you were winning all of them. There was one close match, but the computer's name was Kasparov. And you said, I got to beat this one. Don't forget to think that it's rigged or I'm getting paid to do this show. Well done. No, it's, it's, it's, it's, it's, it's, yeah, it's, I always, I always mentioned this, this simultaneous exhibition I played in 1985 against 32 chess-playing computers. Because it's not, the important of this event was not just I won all the games, but nobody was surprised. And I have to, I have to admit that the fact that I could win all the games, all the games against these 32 chess-playing computers, they're only chess-playing machines, so that's they did nothing else. Probably boosted my confidence that I would never, I would never be defeated by, even by more powerful machines. Right. Well, I'd love it. That's why I asked the question, how far can we take machines? You know, we don't know, like you said. It's, they, wait, why should we bother? I mean, it's just, it's the, I see so many, so many new challenges that we will be able to take. And challenges we abandoned, like space exploration or deep ocean exploration, because they were too risky, because we couldn't actually calculate all the odds. Great, now we have AI. Let's say it's all about sort of, is increasing our, increasing risk. Because we could actually measure it against this phenomenal power of AI that will help us to find the right path. So I want to follow up on some other commentary. Bryn Jolson and McAfee basically put forth the premise, look, machines have always replaced humans, but this is the first time in history they've replaced humans in terms of cognitive tasks. Okay, and they've also posited that, look, there's no question that it's affecting jobs. But they put forth the prescription, which I think as an optimist you would agree with, that it's about finding new opportunities. It's about bringing creativity in, complimenting the machines and creating new value. As an optimist, I presume you would agree with that. Absolutely, I'm always saying jobs do not disappear, they evolve because it's inevitable part of the technological progress. We come up with new ideas and every disruptive technology destroys some industries but creates new jobs. So basically we just have, we see jobs shifting from one industry to another, like from agriculture manufacturer, from manufacturer to other sectors, cognitive tasks, but now there will be something else. So I think the market will change, the job market will change quite dramatically. And again, I believe that we'll have to look for risky jobs. We'll have to start doing things that we abandoned 30, 40 years ago because we thought they were too risky. And so back to the book you were talking about, what deep thinking, we're machine learning ends or machine intelligence ends and human intelligence begins. You talked about courage, right? And we need fail safes in place, but you also need that human element of courage, like you said, to accept risk and take risk. Yes, but now it probably will be easier, but also, as I said, the machines will force a lot of talent actually to move into other areas that were not as attractive because there were other opportunities. So there's so many, what I call, wrought cognitive tasks that are just still financially attractive. I hope AI will close me in the loops and we'll see talent moving into areas where we just have to open new horizons. I think it's very important just to remember that it's the technological progress, especially when you're talking about disruptive technology, it's more about unintended consequences. Our flight to the moon was just, psychologically it's important, the space race, the Cold War, but it was about also GPS, about our planet. I mean, so many side effects that in the 60s were not yet appreciated, but eventually created the world where we live now. So I don't know the consequences of us flying to Mars. Maybe it's something will happen in one of the asteroids, we'll just, I don't know whether, we'll find sort of a new substance that will replace our fossil fuel. What I know, it will happen, because it's just, when you look at the human history, it's just the always great exploration. They ended up with unintended consequences as the main result, not what was originally planned as the number one goal. We've been talking about where innovation comes from today. It's a combination of, I put it out there, a combination of data plus being able to apply artificial intelligence, and of course there's cloud economics as well. Essentially, well, is that reasonable? And I bring it, I think about something that you've said I believe in the past, is that you didn't have the advantage of seeing deep blues moves, but it had the advantage of studying your moves. You didn't have all the data. It had the data. So how does data fit in to the future? Data, data is vital. Data is fuel, and that's what I think we need. We need to find some of the most effective ways of collaboration between humans and machines. Machine can mine the data. I mean, for instance, IBM Watson, just this was a breakthrough in instantly mining data and human language. And now we could see just even just more effective tools to help us to mine the data. But at the end of the day, it's why are we doing that? So what's the purpose? So what does matter to us? So why do we want to mine these data? Why do we want to do it here and not there? So it seems just at first sight that the human responsibilities are shrinking. I think exactly the opposite. We just don't have to move too much, but just by the tiny shift, just a percentage of a degree of an angle could actually make huge difference. Yes, when this, okay, if the weapon is a bullet, it reaches the target. So the same with AI. More power actually offers opportunities to start just making tiny adjustments that could have massive consequences. Open up a big aperture. That's why you like augmented intelligence. I think artificial is sci-fi. What's artificial about it? I don't understand. But it's artificial, it's easy sell because it's sci-fi, but augmented is just what it is. Because our intelligent machines, they're making us smarter. So same way as the technology in the past made us stronger and faster. It's not artificial horsepower. Right, right. I mean, it's created from something. Exactly, but it's just it's created from something. And even if they could just know the machines can adjust their own code, fine. So they still will be confined within the parameters of the tasks. So they cannot go beyond that. Because again, they can only answer questions. They can only give you answers. So we provide the questions. So it's very important to recognize that we will be in the leading role. That's why I use the term shepherds. How do you spend your time these days? You're obviously writing, you're speaking. Writing, speaking, traveling around the world. So because I just, I have to show up at many, many conferences and it's the AI and I was very hot topic. I'm also, as you mentioned, I'm the chairman of Human Rights Foundation. And I just have my responsibilities to help people who are just dissidents around the world. So who are fighting for their principles and for freedom. Our organization runs the largest dissident gathering in the world. It's the Oslo Freedom Forum. We have the 10th anniversary, 10th event this May. That has been a pleasure. Gary Kessbar. Thank you. Great to be here live on theCUBE, back with more from New York City, right after this.