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This series covers the history of Artificial Intelligence and games (until Deep Blue) and focuses on machines that played chess, checkers, and backgammon. The following topics are covered: how to build chess machines, Shannon’s work on chess, Turing’s work on chess, The Turk, El Ajedrecista, MANIAC, Bernstein chess program, Samuel’s checkers, Mac Hack VI, Cray Blitz, BKG, HiTech, Chinook, Deep Thought, TD-Gammon, and Deep Blue.
Part 1: Machines That Play (Overview) — this one
Part 4: Machines That Play (Deep Blue)
If you want a summary of the first 5 parts, focusing on the human elements, go here.
Part 6: Machines That Play (Checkers)
Part 7: Machines That Play (Backgammon)
This is part 5 of a 7 part series. Here we talk about reactions to Deep Blue’s win against Garry Kasparov and computer chess after that.
Kasparov was asked to explain his earlier statement regarding Deep Blue: that he was in presence of some kind of intelligence.
He answered, “Yes. I think we can hardly call it intelligence because we always believe that intelligence is something similar to our mind. But playing with Deep Blue, and other computers but mainly with Deep Blue, I can smell that the decisions that it’s making are intelligent because I would come to the same conclusion by using my intuition…
…But if I use 90% of my intuition and positional judgement and 10% of calculation, and Deep Blue uses 95% of computation and 5% of built in chess knowledge, and the result matches four times out of five, maybe we should talk about some sort of artificial intelligence.”
Recall the infamous game 2, one that disturbed Kasparov, one that experts said looked like a human-type game, not a computer-type game, one that Kasparov resigned after 45 moves — but one he could have drawn (something he had never done before).
Imagine this: It’s game 3 and Kasparov knows he could have drawn game 2, but didn’t. Even worse, he has never resigned a drawn position. How does this effect how he plays game 3? Well, in game 3 he has White, but doesn’t play his usual game. He plays a very different game.
“I don’t want to say he’s afraid, but when the world champion with the white pieces doesn’t want to attack, what do you do?”
“…Garry was in a mental framework which said to himself “Man, I hate this game. I’m disgusted with myself. I played like a jerk. I’m going to lose in front of millions of fans. What am I doing here? Why did I wake up today? Again, there is that intimidating factor. When you sit there and you’re told that your opponent analyses chess at 2OO million moves a second, and all you’re looking at is a three or four move perpetual check, you’ve got to figure that your opponent’s seen everything.”
But IBM had retired Deep Blue. A rematch would have required months of resources and preparation, which IBM did not want to spend on this. In the Scientific American article titled “20 Years after Deep Blue”, Campbell said, “We felt we had achieved our goal, to demonstrate that a computer could defeat the world chess champion in a match and that it was time to move on to other important research areas.”
Usually in any significant chess match, players study their opponent’s previous games. According to Kasparov and his team, when they asked IBM for Deep Blue’s previous matches, IBM said there is nothing to share because there were no public games. It turned out that Deep Blue had not played any matches after the one against Kasparov in 1996. Deep Blue’s training and preparation had been done entirely in private. Hence Garry went into the 1997 match blind — something he had never done. And Deep Blue had every game Kasparov had ever played in its memory.
“IBM bent the rules. They didn’t actually cheat, but they exploited every resource of the rule book to disadvantage Garry. He would have won if they’d played fair with him.”
The author of the Wired Article then says, “Most grandmasters, even those who regularly get kicked all over the chessboard by Kasparov, agree.”
“Legs have been broken for less in pool halls and card rooms.”
So, was Deep Blue really the best chess player? Or was Kasparov still the better player?
The problem is that the rematch was only six games and Kasparov was only one point behind. Championship matches usually have a lot more games and most end in draws. So, it was hard to say if the rematch of six games said anything about who the better player was. Most would argue that Kasparov was still the better player. But may be that wasn’t the real point. We saw some very special humans put tremendous efforts to create a machine that forced even the best of us to doubt. It beat us at one of our most treasured games and it left us in awe (or some in fear).
“…Deep Blue won. Brilliantly. Creatively. Humanly. It played with — forgive me — nuance and subtlety.”
So is Deep Blue intelligent?
May be, a little. Deep Blue was certainly not stupid, but it also wasn’t intelligent, in the same way we say another human being is intelligent. What Deep Blue showcased was a narrow kind of intelligence; the kind that shows brilliance in one domain and it does so because humans create better hardware, better software, better algorithms, and better representations. But if you ask these specialized machines to do anything else, they will fail. Deep Blue would have failed at all those other non-chess related tasks we do; it did not exhibit general intelligence. No machine till date has exhibited general intelligence and it appears that they still have a long way to go before they can.
“…you can never know for sure exactly why it did what it did.”
Deep Blue could only play chess, it could do nothing else. This is called narrow intelligence. This narrow intelligence, however, was already so complex that its makers could not trace its individual decisions. Deep Blue did not make the same move in a given position and it was simply too complicated, too complex, or too hard to understand its decisions. Explainability was already too hard then, and it has become more and more challenging to solve since then.
Until Deep Blue, humans were winning at chess. Machines really couldn’t beat the best humans — not even close. But then Deep Blue won. And soon so did the other machines and they have been beating humans ever since. This massive growth in performance is their identity.
No matter what our rate of improvement, once machines begin to improve, their progress ends up being measured exponentially. And ours doesn’t.
But it’s not really us vs. them, even though it was Garry Kasparov vs. Deep Blue. That was a game, a way to test how machines could learn, improve, and play. But the biggest win was for the humans because their intelligence had created Deep Blue.
Most people believed Kasparov was still the better player, but his emotions got in the way. Either way, one of the biggest takeaways from this match was that we had collectively underestimated both the physiological and psychological aspects of the match. Our emotions, fears, desires, and doubts had a way of getting the best of us and sometimes we cannot do much more than just stand by and let it pass. And this is a uniquely human problem, one our machine opponents do not worry about.
Our emotions, fears, desires, and doubts had a way of getting the best of us…And this is a uniquely human problem, one our machine opponents do not worry about.
The end of human-computer matches
In 2005, Hydra, a dedicated chess supercomputer with custom hardware and 64 processors crushed seventh-ranked Michael Adams (5.5–0.5) in a six-game match. Some people criticized Michael Adams for not preparing as well as Kasparov had, but that was irrelevant — this event was the beginning of the end of human-computer matches.
Hydra was developed by a team with Dr. Christian Donninger, Dr. Ulf Lorenz, Grandmaster Christopher Lutz and Muhammad Nasir Ali. The team estimated its rating to be over 3000!
What next? The rise of the Centaurs
Garry Kasparov introduced Advanced Chess (also known as cyborg chess, centaur chess or Ivanov chess), where a human player and a computer chess program would play as a team against other such pairs. This is a perfect example of the way Kasparov saw (and continues to see) the ideal interplay between humans and machines. The idea is that advanced chess would amplify human performance.
The first Advanced Chess event was held in June 1998 in León, Spain. The 6-game match was played between Garry Kasparov, who was using Fritz 5, and Veselin Topalov, who was using ChessBase 7.0. It was decided that the players would consult the built-in million games databases only for the 3rd and 4th game, and would use the chess engines without consulting the databases for the remaining games. The time available to each player during the games was 60 minutes. The match ended in a 3–3 tie.
In 2017, chess engine Zor won the freestyle Ultimate Challenge tournament (freestyle is a variation or Advanced Chess, where consultation teams are also allowed). The best human plus computer came in 3rd place. Chess machines are now superior to human plus computers.
Computer Chess Status
Chess machines perform at a super-human level, i.e. they perform better than all humans. Here’s when different chess machines beat humans:
How do we play chess now?
In his FiveThirtyEight interview, Murray Campbell was asked if computers are draining the beauty out of chess, to which he replied, “Grandmasters that have grown up with most of their training in the computer era play a much more objective style of chess. They’re less willing to dismiss a move because it’s ugly, or doesn’t appeal to their aesthetics…Chess is an art, but it’s more of a sport. If you’re interested in winning, then you play the right move, even if it’s an ‘ugly’ move or a ‘computer’ move…Super-deep preparation can create a draw-ish tendency. The white player will try to create a position where the opponent has chances to go wrong. And the black player, if they’ve prepared well enough, will have found the way to navigate through that mess and find the way to the draw. I can certainly think of some 20- or 30-move games that have probably been entirely calculated at home.”
In 2017, DeepMind’s AlphaZero beat Stockfish 28–0, with 72 draws, in a 100-game match. It used the a similar approach to master not just chess, but also Go and shogi.
Here’s the mind-boggling part: Imagine showing a computer how the chess pieces move, i.e. showing it legal moves and nothing more. Then you tell the computer to learn to play the game — by itself. And in just 9 hours — yes ONLY 9 hours — it figures out not just how to play chess, but how to play at such a high level that it beats the strongest programs in the world — by far!
After just four hours of training, AlphaZero was playing at a higher Elo rating than Stockfish 8 and after 9 hours of training, it had decisively defeated Stockfish 8 in 100-game tournament.
Jonathan Schaeffer, an AI researcher at the University of Alberta, said, “It surprised the hell out of me. The games were beautiful and creative. AlphaZero made apparently crazy sacrifices that humans would not even consider in order to get more freedom of movement. But it also played differently to all other chess programmes which rely on human input.” Other chess grandmasters were equally impressed. Russian champion Peter Svidler said that AlphaZero’s play was “absolutely fantastic, phenomenal” and he felt in “awe” of its play.
Magnus Carlsen’s coach Peter Nielsen said,
“…the aliens came and showed us how to play chess”
Chess.com asked experts for their first reactions:
It seems right to end with Garry Kasparov’s TED talk and his view on the experience.
“What I learned from my own experience is that we must face our fears if we want to get the most out of our technology, and we must conquer those fears if we want to get the best out of our humanity. While licking my wounds, I got a lot of inspiration from my battles against Deep Blue. As the old Russian saying goes, if you can’t beat them, join them. Then I thought, what if I could play with a computer — together with a computer at my side, combining our strengths, human intuition plus machine’s calculation, human strategy, machine tactics, human experience, machine’s memory. Could it be the perfect game ever played? But unlike in the past, when machines replaced farm animals, manual labor, now they are coming after people with college degrees and political influence. And as someone who fought machines and lost, I am here to tell you this is excellent, excellent news. Eventually, every profession will have to feel these pressures or else it will mean humanity has ceased to make progress. We don’t get to choose when and where technological progress stops.
We cannot slow down. In fact, we have to speed up. Our technology excels at removing difficulties and uncertainties from our lives, and so we must seek out ever more difficult, ever more uncertain challenges. Machines have calculations. We have understanding. Machines have instructions. We have purpose. Machines have objectivity. We have passion. We should not worry about what our machines can do today. Instead, we should worry about what they still cannot do today, because we will need the help of the new, intelligent machines to turn our grandest dreams into reality. And if we fail, if we fail, it’s not because our machines are too intelligent, or not intelligent enough. If we fail, it’s because we grew complacent and limited our ambitions. Our humanity is not defined by any skill, like swinging a hammer or even playing chess.There’s one thing only a human can do. That’s dream. So let us dream big.”
In this 2018 WSJ article titled “Intelligent Machines Will Teach Us — Not Replace Us”, Garry Kasparov reflected on the progress of AI and said, “My chess loss in 1997 to IBM supercomputer Deep Blue was a victory for its human creators and mankind, not triumph of machine over man. In the same way, machine-generated insight adds to ours, extending our intelligence the way a telescope extends our vision. We aren’t close to creating machines that think for themselves, with the awareness and self-determination that implies. Our machines are still entirely dependent on us to define every aspect of their capabilities and purpose, even as they master increasingly sophisticated tasks.”
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