>> [Narrator] 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, I'm John Walls. It is now a great honor of ours to have I think probably and arguably the greatest chess player of all time, Garry Kasparov now joins us. He's currently the chairman of the Human Rights Foundation, political activist in Russia as well some time ago. Thank you for joining us, we really appreciate the time, sir. >> Thank you for inviting me. >> 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. Our abilities are complementary. The humans will bring creativity and intuition and other typical human qualities like human judgment and strategic vision while machines will add calculation, memory, and many other abilities that they have been acquiring quickly. >> So there's room for both, right? >> Yes, I think it's inevitable because no machine will ever reach 100% perfection. Machines will be coming closer and closer, 90%, 92, 94, 95. But there's still room for humans because at the end of the day even with this massive power you have guide it. You have to evaluate the results and at the end of the day the machine will never understand when it reaches the territory of diminishing returns. 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 automatically transfer the machine's expertise for the closed systems into the open-ended systems. Because in every closed system, whether it's the game of chess, the game of gall, video games like daughter, 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 machine will never identify what is the sort of the right question to be asked. >> Don't hate me for this question, but it's been reported, now I don't know if it's true or not, that at one point you said that 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? How capable to 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, world champion at the time, worry about machines' capability of defeating me in the real chess game. I underestimated the pace it was developing. I could see something was happening, was cooking, but I thought it would take longer for machines to catch up. 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 human perform, machine will eventually surpass us. >> What I love about your story, I was telling you off-camera about when we had Erik Brynjolfsson and Andrew McAfee on, 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 will 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 and humans. Is that accurate? >> Yes, in chess actually, we could demonstrate how the collaboration can work. Now in many areas people rely on the lessons that have been revealed, learned from 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. In the future I think that will be the key of success because we have very powerful machine, those AIs, intelligent algorithms. All of them will require very special treatment. That's why also I use this analogy with the right fuel for Ferrari. We will have expert operators, I 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 the other way around. >> What let you to that response? Was it your competitiveness? Was it your vision of machines and humans working together? >> I thought I could last longer as the undefeated world champion. Ironically, 1997 when you just look at the game and 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 any chess app on your laptop. I mean you cannot really compare with Deep Blue. 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. I just realized while we could compete with machines for few more years, and that's great, it did take place. I played two more matches in 2003 with German program. Not as publicized as IBM match. Both ended as a tie and I think they were probably stronger than Deep Blue, but I knew it would just be over, maybe a decade. How can we make chess relevant? For me it was very natural. I could see this immense power of calculations, brute force. On the other side I could see us having qualities that machines will never acquire. How about bringing together and using chess as a laboratory to find the most productive ways for human-machine collaboration? >> What was the difference in, I guess, processing power basically, or processing capabilities? You played the match, this is 1997. You played the match on standard time controls 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? >> Well it's the standard time control. I think you should explain to your audience at that time it was seven hours game. It's what we call classical chess. We have rapid chess that is under one hour. Then you have blitz chess which is five to ten minutes. That was a normal time control. It's worth mentioning that other computers they were beating human players, myself included, in blitz chess. In the very fast chess. We still thought that more time was more time we could have sort of a bigger comfort zone just to contemplate 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 your ability not to crack under pressure because there's so many things that could take you off your balance, and machine doesn't care about it. At the end of the day machine has a steady hand, and steady hand wins. >> Emotion doesn't come into play. >> It's not about apps and 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 average it's still lower. I played many world championship matches and I analyze the games, games played at the highest level. 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, tiny mistake, then I can expect you to return the favor. Against the machine it's just that's it. Humans cannot play at the same level throughout the whole game. The concentration, the vigilance are now required when humans face humans. Psychologically when you have a strong machine, machine's good enough to play with a steady hand, the game's over. >> I want to point out too, just so we get the record straight for people who might not be intimately familiar with your record, you were ranked number one in the world from 1986 to 2005 for all but three months. Three months, that's three decades. >> Two decades. >> Well 80s, 90s, and naughts, I'll give you that. (laughing) That's unheard of, that's phenomenal. >> Just going back to your previous question about why I just look for some new form of chess. It's one of the key lessons I learned from my childhood thanks to my mother who spent her live just helping me to become who I am, who I was after my father died when I was seven. It's about always trying to make the difference. It's not just about winning, it's about making a difference. It led me to kind of a new motto in my professional life. That is it's all about my own quality of the game. As long as I'm challenging my own excellence I will never be short of opponents. For me the defeat 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 I think, was it John Henry, the famous example. (all men speaking at once) >> He won, but he lost. >> Motivation wasn't competition, it was advancing society and creativity, so I love it. Another thing I just want, a quick aside, you mentioned performing under pressure. I think it was in the 1980s, it might have been in the opening of your book. You talked about playing multiple computers. >> [Garry] Yeah, in 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've got to beat this one because people will think that it's rigged or I'm getting paid to do this. So well done. >> It's I always mention this exhibition I played in 1985 against 32 chess-playing computers because it's not the importance of this event was not just I won all the games, but nobody was surprised. I have to admit that the fact that I could win all the games against these 32 chess-playing computers they're only chess-playing machine so they did nothing else. Probably boosted my confidence that I would never be defeated even by more powerful machines. >> Well I love it, that's why I asked the question how far can we take machines? We don't know, like you said. >> Why should we bother? I see so many new challenges that we will be able to take and challenges that we abandoned like space exploration or deep ocean exploration because they were too risky. We couldn't actually calculate all the odds. Great, now we have AI. It's all about increasing our risk because we could actually measure against this phenomenal power of AI that will help us to find the right pass. >> I want to follow up on some other commentary. Brynjolfsson and McAfee basically put forth the premise, look machines have always replaced humans. But this is the first time in history that they have replaced humans in the terms of cognitive tasks. They also posited 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, complementing 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. It's an 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 see jobs shifting from one industry to another. Like from agriculture, manufacture, from manufacture to other sectors, cognitive tasks. But now there will be something else. I think the market will change, the job market will change quite dramatically. Again I believe that we will have to look for riskier jobs. We will have to start doing things that we abandoned 30, 40 years ago because we thought they were too risky. >> Back to the book you were talking about, deep thinking or machine learning, or machine intelligence ends and human intelligence begins, you talked about courage. We need fail safes in place, but you also need that human element of courage like you said, to accept risk and take risk. >> Now it probably will be easier, but also as I said the machine's wheel will force a lot of talent actually to move into other areas that were not as attractive because there were other opportunities. There's so many what I call raw cognitive tasks that are still financially attractive. I hope and I will close many loops. We'll see talent moving into areas where we just have to open new horizons. I think it's very important just to remember it's the technological progress especially when you're talking about disruptive technology. It's more about unintended consequences. The fly to the moon was just psychologically it's important, the Space Race, the Cold War. But it was about also GPS, about so many side effects that in the 60s were not yet appreciated but eventually created the world we have now. I don't know what the consequences of us flying to Mars. Maybe something will happen, one of the asteroids will just find sort of a new substance that will replace fossil fuel. What I know, it will happen because when you look at the human history there's all this 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 a by-product 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? I think about something you said, I believe, in the past that you didn't have the advantage of seeing Deep Blue's moves, but it had the advantage of studying your moves. You didn't have all the data, it had the data. How does data fit into the future? >> Data is vital, data is fuel. That's why I think we need to find some of the most effective ways of collaboration between humans and machines. Machines can mine the data. For instance, it's a breakthrough in instantly mining data and human language. Now we could see even more effective tools to help us to mine the data. But at the end of the day it's why are we doing that? What's the purpose? What does matter to us, so why do we want to mine this data? Why do we want to do here and not there? It seems at first sight that the human responsibilities are shrinking. I think it's the opposite. We don't have to move too much but by the tiny shift, just you know percentage of a degree of an angle could actually make huge difference when this bullet reaches the target. The same with AI. More power actually offers opportunities to start just making tiny adjustments that could have massive consequences. >> Open up a big, that's why you like augmented intelligence. >> I think artificial is sci-fi. >> What's artificial about it, I don't understand. >> Artificial, it's an easy sell because it's sci-fi. But augmented is what it is because our intelligent machines are making us smarter. Same way as the technology in the past made us stronger and faster. >> It's not artificial horsepower. >> It's created from something. >> Exactly, it's created from something. Even if the machines can adjust their own code, fine. It still will be confined within the parameters of the tasks. They cannot go beyond that because again they can only answer questions. They can only give you answers. We provide the questions so it's very important to recognize that it is 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 because I have to show up at many conferences. The AI now is a very hot topic. Also as you mentioned I'm the Chairman of Human Rights Foundation. My responsibilities to help people who are just dissidents around the world who are fighting for their principles and for freedom. Our organization runs the largest dissident gathering in the world. It's called the Freedom Forum. We have the tenth anniversary, tenth event this May. >> It has been a pleasure. Garry Kasparov, live on theCube. Back with more from New York City right after this. (lively instrumental music)