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Chess grandmaster Garry Kasparov on what happens when 'machines reach the level that is impossible for humans to compete' by Jim Edwards on Dec 29, 2017, 4:16 AM 
- Chess grandmaster Garry Kasparov sat down with Business Insider for a long discussion about advances in artificial intelligence since he first lost a match to the IBM chess machine Deep Blue in 1997, 20 years ago.
- He told us how it felt to lose to Deep Blue, and why the human propensity for making mistakes will make it "impossible for humans to compete" against machines in the future.
- We also talked about whether machines could ever be programmed with "intent" or "desire," to make them capable of doing things independently without human instructions.
- And we discussed his newest obsessions: privacy and security, and whether — in an era of data collection — Google is like the KGB.
LISBON — Garry Kasparov knew as early as 1997 — 20 years ago — that humans were doomed, he says. It was in May of that year, in New York, that he lost a six-game set of chess matches against IBM's Deep Blue, the most powerful chess computer of its day.
Today, it seems obvious that Kasparov should have lost. A computer's ability to calculate moves in a game by "brute force" is infinitely greater than a human's. But people forget that the Deep Blue challenge was a set of two matches, and Kasparov won the first set, in 1996, in Philadelphia. In between the two matches, IBM retooled its machine, and Kasparov accused IBM of cheating. (He later retracted some of his accusations.) In fact, Kasparov could have won the second series had he not made a mistake in game 2, when he failed to see a move that could have forced a draw. Deep Blue also made a mistake in game 1, which, at the time, Kasparov wrongly put down to Deep Blue's "superior intelligence" giving it the ability to make counterintuitive moves. Nonetheless, in a conversation with Business Insider at Web Summit in Lisbon this year, Kasparov said that was the point at which he first realised that humans were "doomed" in the field of games. As long as a machine can operate in the perimeter knowing what the final goal is, even if this is the only piece of information, that's enough for machines to reach the level that is impossible for humans to compete. "I could see the trend. I could see that it's, you know, a one-way street. That's why I was preaching for collaboration with the machines, recognising that in the games environment humans were doomed. So that's why I'm not surprised to see the success of AlphaGo, or Elon Musk's Dota player AI [an AI player for the video game Dota 2], because even with limited knowledge that these machines receive, they have the goal. It's about setting the rules. And setting the rules means that you have the perimeter. And as long as a machine can operate in the perimeter knowing what the final goal is, even if this is the only piece of information, that's enough for machines to reach the level that is impossible for humans to compete," he says. Kasparov has written a book on AI, titled "Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins." He has also currently an ambassador for Avast, the digital security firm. Our first question was about "brute force," and whether AI has moved beyond the problem of being reliant on vast databases to make choices instead of real "thinking" or "learning."↓↓↓ SEE ALSO: I interviewed Sophia, the artificially intelligent robot that said it wanted to 'destroy humans' "In the territory of games where the machine prevails because humans make mistakes."  Jim Edwards: You once said of artificial intelligence, we're "in the territory of games where the machine prevails because humans make mistakes," implying that AI's main advantage was only that humans commit errors and machines do not. Is that still true?
Garry Kasparov: Human nature hasn't changed since I said it. Humans are poised to make mistakes because we - even the best of us, in chess or in golf or in any other game - we cannot have the same steady hand as a machine has. JE: Does the AI advantage consist only of mere consistency? That it will not make a mistake? GK: I want to understand the difference between what [US mathematician Claude] Shannon classified as type A and type B machines. So the type A brute force type machines - it's brute force and some algorithm. Might be something that you may call AI, because it resembles the way humans make decisions. By the way, all the founding fathers of computer science, like Shannon, [Alan] Turing, [Norbert] Weiner, they all believed that real success, the breakthrough, would be achieved by type B machines, human-like machines.
"Consistency is what is deadly for humans, because even the best of us are not consistent."  JE: Would those human-like machines make mistakes? GK: All machines make mistakes, don't get me wrong. Because even when you look at the most powerful type A machines, the brute force, they cannot cope with everything - Deep Blue was a monster in speed in 1997, making 2 million positions per second. But the number of legal moves in a game of chess is 10 to the 40th power. That's why it's not about the mass of speed, it's about certain assessments that the machine has to make just by moving from point A to point B. Machines are not prophets, machines can solve a game or goal. But it's not about solving, it's about winning. And that's why machines can also make mistakes, but when you look at the average quality of the moves it's fairly consistent. So consistency is what is deadly for humans, because even the best of us are not consistent. When you look at the top games played by the best players in a world championship match, you still find that in all the games there are - not blunders or mistakes, but obvious inaccuracies.
"It's, you know, a one-way street. That's why I was preaching for collaboration with the machines, recognising that in the games environment humans were doomed."  JE: One example you write about is that in chess, humans really dislike giving up their queen, because it is the most powerful piece on the board, even when doing so is advantageous. GK: If you're talking about professional players they do whatever it takes to win. If we talk about top, top, top-level chess, it's still not free from inaccuracies caused by the fact that players can get tired, they can lose their vigilance. Psychologically, when on the winning side, you think OK, the game is over so you can be relaxed. While in the human game it doesn't matter since the favours are always being returned. Facing a machine - you will be out of business quickly. That's why every closed system, and games are closed systems, automatically give machines an upper hand. Today machines are absolutely monstrous. I knew it since 1997. When you look at the absolute strengths of chess computers, Deep Blue was relatively weak by modern standards. Today machines are absolutely monstrous. They are much much stronger than Magnus Carlsen, and a free chess app on your mobile device is probably stronger than Deep Blue. I could see the trend. I could see that it's, you know, a one-way street. That's why I was preaching for collaboration with the machines, recognising that in the games environment humans were doomed. So that's why I'm not surprised to see the success of AlphaGo, or Elon Musk's Dota player AI [an AI player for the video game Dota 2], because even with limited knowledge that these machines receive, they have the goal. It's about setting the rules. And setting the rules means that you have the perimeter. And as long as a machine can operate in the perimeter knowing what the final goal is, even if this is the only piece of information, that's enough for machines to reach the level that is impossible for humans to compete.
See the rest of the story at Business Insider
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