Chess can offer a lot to the world of artificial intelligence
In his new book, Garry Kasparov, arguably the greatest chess player of all time, turns to the subject of artificial intelligence, and remembers his mightiest opponent, IMB’s Deep Blue.
By Garry Kasparov Publisher: John Murray Pages: 288 Price: Rs 599
It was a pleasant day in Hamburg on June 6, 1985, but chess players rarely get to enjoy the weather. I was inside a cramped auditorium, pacing around inside a circle of tables upon which rested 32 chessboards. Across from me at every board was an opponent, who moved promptly when I arrived at the board in what is known as a simultaneous exhibition. “Simuls,” as they are known, have been a staple of chess for centuries, a way for amateurs to challenge a champion, but this one was unique. Each of my opponents, all 32 of them, was a computer.
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 bearing the “Kasparov” brand name from the electronics firm Saitek. One of the organisers warned me that playing against machines was different because they would never get tired or resign in dejection the way a human opponent would; they would play to the bitter end. But I relished this interesting new challenge— a and the media attention it attracted. I was 22 years old, and by the end of the year, I would become the youngest world chess champion in history. I was fearless, and, in this case, my confidence was fully justified.
It illustrates the state of computer chess at the time that it didn’t come as much of a surprise, at least not in the chess world, when I achieved a perfect 32–0 score, winning every game, although there was one uncomfortable moment. At one point I realised that I was drifting into trouble in a game against one of the Kasparov models. If this machine scored a win or even a draw against me, people might suggest that I had thrown the game to get publicity 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 and secure my clean sweep. From the human perspective, or at least from my perspective as the human in this equation, these were the good old days of human versus machine chess. But this golden age would be brutally short.
12 years later I was in New York City fighting for my chess life against just one machine, a $10 million IBM supercomputer nicknamed “Deep Blue”. This battle, actually a rematch, became the most famous human- machine competition in history. Newsweek’s cover called it “The Brain’s Last Stand” and a flurry of books compared it to Orville Wright’s first flight and the moon landing. Hyperbole, of course, but not out of place at all in the history of our love-hate relationship with so-called intelligent machines.
Jump forward another 20 The rare bond between a painter and a collector develops to the benefit of both, each making the other richer over time. Exploring artist’s aesthetics, thematic engagements and art historical relevance, this book focuses on Niloufar and Mitchell S. Crites’ collection of Jangarh Singh Shyam’s paintings and drawings in New Delhi. years to today, to 2017, and you can download any number of free chess apps for your phone that rival any human Grandmaster. You can easily imagine a robot in my place in Hamburg, circling inside the tables and defeating 32 of the world’s best human players at the same time. The tables have turned, as they always do in our eternal race with our own technology.
Ironically, if a machine did perform a chess simul against a room full of human professional players, it would have more trouble moving from board to board and physically moving the pieces than it would have calculating the moves. Despite centuries of science fiction about automatons that look and move like people, and for all the physical labor today done by robots, it’s fair to say that we have advanced further in duplicating human thought than human movement.
In what artificial intelligence and robotics experts call Moravec’s paradox, in chess, as in so many things, what machines are good at is where humans are weak, and vice versa. In 1988, the roboticist Hans Moravec wrote, “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.” I wasn’t aware of these theories at the time, and in 1988 it was safe to include checkers but not yet chess, but ten years later it was obviously the case in chess as well. Grand-masters excelled at recognising patterns and strategic planning, both weaknesses in chess machines that, however, could calculate in seconds tactical complications that would take even the strongest humans days of study to work out. This disparity gave me an idea for an experiment after my matches with Deep Blue attracted so much attention. You could also call it “if you can’t beat ’em, join ’em,” but I was eager to continue the computer chess experiment even if IBM was not. I wondered, what if instead of human versus machine we played as partners? My brainchild saw the light of day in a match in 1998 in León, Spain, and we called it Advanced Chess. Each player had a PC at hand running the chess software of his choice during the game. The idea was to create the highest level of chess ever played, a synthesis of the best of man and machine. It didn’t quite go according to plan, as we’ll see later, but the fascinating results of these “centaur” competitions convinced me that chess still had a lot to offer the worlds of human cognition and artificial intelligence.
“It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-yearold when it comes to perception and mobility.”