In the 1990s Garry Kasparov’s chess matches against IBM’s supercomputer Deep Blue set off anxieties about the encroachment of algorithmic intelligence into the human domain—anxieties surely familiar to anyone who has recently read an essay about artificial intelligence. In the Review’s February 11, 2010, issue, he reflected on his experiences beating and losing to Deep Blue: “It was my luck (perhaps my bad luck) to be the world chess champion during the critical years in which computers challenged, then surpassed, human chess players.”
Ever a deliberate thinker, Kasparov was, on the one hand, mindful of the effects of the proliferation of chess software—“humans today are starting to play more like computers”—and, on the other, open to the possibilities offered by powerful computers: “What if instead of human versus machine we played as partners?… The idea was to create the highest level of chess ever played, a synthesis of the best of man and machine.”
Below, we present a free preview of Kasparov’s essay. Read the full article for free on the Review’s website here.
In 1985, in Hamburg, I played against thirty-two different chess computers at the same time in what is known as a simultaneous exhibition. I walked from one machine to the next, making my moves over a period of more than five hours. The four leading chess computer manufacturers had sent their top models, including eight named after me from the electronics firm Saitek.
It illustrates the state of computer chess at the time that it didn’t come as much of a surprise when I achieved a perfect 32–0 score, winning every game, although there was an uncomfortable moment. At one point I realized that I was drifting into trouble in a game against one of the “Kasparov” brand models. If this machine scored a win or even a draw, people would be quick to say that I had thrown the game to get PR for the company, so I had to intensify my efforts. Eventually I found a way to trick the machine with a sacrifice it should have refused. From the human perspective, or at least from my perspective, those were the good old days of man vs. machine chess.
Eleven years later I narrowly defeated the supercomputer Deep Blue in a match. Then, in 1997, IBM redoubled its efforts—and doubled Deep Blue’s processing power—and I lost the rematch in an event that made headlines around the world. The result was met with astonishment and grief by those who took it as a symbol of mankind’s submission before the almighty computer. (”The Brain’s Last Stand” read the Newsweek headline.) Others shrugged their shoulders, surprised that humans could still compete at all against the enormous calculating power that, by 1997, sat on just about every desk in the first world.
It was the specialists—the chess players and the programmers and the artificial intelligence enthusiasts—who had a more nuanced appreciation of the result. Grandmasters had already begun to see the implications of the existence of machines that could play—if only, at this point, in a select few types of board configurations—with godlike perfection. The computer chess people were delighted with the conquest of one of the earliest and holiest grails of computer science, in many cases matching the mainstream media’s hyperbole. The 2003 book Deep Blue by Monty Newborn was blurbed as follows: “a rare, pivotal watershed beyond all other triumphs: Orville Wright’s first flight, NASA’s landing on the moon....”
The AI crowd, too, was pleased with the result and the attention, but dismayed by the fact that Deep Blue was hardly what their predecessors had imagined decades earlier when they dreamed of creating a machine to defeat the world chess champion. Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating 200 million possible moves on the chess board per second and winning with brute number-crunching force. As Igor Aleksander, a British AI and neural networks pioneer, explained in his 2000 book, How to Build a Mind:
By the mid-1990s the number of people with some experience of using computers was many orders of magnitude greater than in the 1960s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.
It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Not that losing to a $10 million alarm clock made me feel any better.
My hopes for a return match with Deep Blue were dashed, unfortunately. IBM had the publicity it wanted and quickly shut down the project. Other chess computing projects around the world also lost their sponsorship. Though I would have liked my chances in a rematch in 1998 if I were better prepared, it was clear then that computer superiority over humans in chess had always been just a matter of time. Today, for $50 you can buy a home PC program that will crush most grandmasters. In 2003, I played serious matches against two of these programs running on commercially available multiprocessor servers—and, of course, I was playing just one game at a time—and in both cases the score ended in a tie with a win apiece and several draws.
Inevitable or not, no one understood all the ramifications of having a super-grandmaster on your laptop, especially what this would mean for professional chess. There were many doomsday scenarios about people losing interest in chess with the rise of the machines, especially after my loss to Deep Blue. Some replied to this with variations on the theme of how we still hold footraces despite cars and bicycles going much faster, a spurious analogy since cars do not help humans run faster while chess computers undoubtedly have an effect on the quality of human chess.
Another group postulated that the game would be solved, i.e., a mathematically conclusive way for a computer to win from the start would be found. (Or perhaps it would prove that a game of chess played in the best possible way always ends in a draw.) Perhaps a real version of HAL 9000 would simply announce move 1.e4, with checkmate in, say, 38,484 moves. These gloomy predictions have not come true, nor will they ever come to pass. Chess is far too complex to be definitively solved with any technology we can conceive of today. However, our looked-down-upon cousin, checkers, or draughts, suffered this fate quite recently thanks to the work of Jonathan Schaeffer at the University of Alberta and his unbeatable program Chinook.
The number of legal chess positions is 1040, the number of different possible games, 10120. Authors have attempted various ways to convey this immensity, usually based on one of the few fields to regularly employ such exponents, astronomy. In his book Chess Metaphors, Diego Rasskin-Gutman points out that a player looking eight moves ahead is already presented with as many possible games as there are stars in the galaxy. Another staple, a variation of which is also used by Rasskin-Gutman, is to say there are more possible chess games than the number of atoms in the universe. All of these comparisons impress upon the casual observer why brute-force computer calculation can’t solve this ancient board game. They are also handy, and I am not above doing this myself, for impressing people with how complicated chess is, if only in a largely irrelevant mathematical way.
This astronomical scale is not at all irrelevant to chess programmers. They’ve known from the beginning that solving the game—creating a provably unbeatable program—was not possible with the computer power available, and that effective shortcuts would have to be found. In fact, the first chess program put into practice was designed by legendary British mathematician Alan Turing in 1952, and he didn’t even have a computer! He processed the algorithm on pieces of paper and this “paper machine” played a competent game.
Rasskin-Gutman covers this well-traveled territory in a book that achieves its goal of being an overview of overviews, if little else. The history of the study of brain function is covered in the first chapter, tempting the reader to skip ahead. You might recall axons and dendrites from high school biology class. We also learn about cholinergic and aminergic systems and many other things that are not found by my computer’s artificially intelligent English spell-checking system—or referenced again by the author. Then it’s on to similarly concise, if inconclusive, surveys of artificial intelligence, chess computers, and how humans play chess.
There have been many unintended consequences, both positive and negative, of the rapid proliferation of powerful chess software. Kids love computers and take to them naturally, so it’s no surprise that the same is true of the combination of chess and computers. With the introduction of super-powerful software it became possible for a youngster to have a top-level opponent at home instead of needing a professional trainer from an early age. Countries with little by way of chess tradition and few available coaches can now produce prodigies. I am in fact coaching one of them this year, nineteen-year-old Magnus Carlsen, from Norway, where relatively little chess is played.
The heavy use of computer analysis has pushed the game itself in new directions. The machine doesn’t care about style or patterns or hundreds of years of established theory. It counts up the values of the chess pieces, analyzes a few billion moves, and counts them up again. (A computer translates each piece and each positional factor into a value in order to reduce the game to numbers it can crunch.) It is entirely free of prejudice and doctrine and this has contributed to the development of players who are almost as free of dogma as the machines with which they train. Increasingly, a move isn’t good or bad because it looks that way or because it hasn’t been done that way before. It’s simply good if it works and bad if it doesn’t. Although we still require a strong measure of intuition and logic to play well, humans today are starting to play more like computers.
The availability of millions of games at one’s fingertips in a database is also making the game’s best players younger and younger. Absorbing the thousands of essential patterns and opening moves used to take many years, a process indicative of Malcolm Gladwell’s “10,000 hours to become an expert” theory as expounded in his recent book Outliers. (Gladwell’s earlier book, Blink, rehashed, if more creatively, much of the cognitive psychology material that is re-rehashed in Chess Metaphors.) Today’s teens, and increasingly pre-teens, can accelerate this process by plugging into a digitized archive of chess information and making full use of the superiority of the young mind to retain it all. In the pre-computer era, teenage grandmasters were rarities and almost always destined to play for the world championship. Bobby Fischer’s 1958 record of attaining the grandmaster title at fifteen was broken only in 1991. It has been broken twenty times since then, with the current record holder, Ukrainian Sergey Karjakin, having claimed the highest title at the nearly absurd age of twelve in 2002. Now twenty, Karjakin is among the world’s best, but like most of his modern wunderkind peers he’s no Fischer, who stood out head and shoulders above his peers—and soon enough above the rest of the chess world as well.
Read the full article for free on the Review’s website here.
Attended one of the games. I think it was Garry’s request that a human be seated across the board from him. Guy sat there, made the moves relayed to him, and otherwise imitated a mannequin.
I would have loved to be that guy.