As Paul Feltovich, a researcher at the Institute for Human and Machine Cognition at the University of West Florida, has explained: ‘Although it is tempting to believe that upon knowing how the expert does something, one might be able to teach this to novices directly, this has not been the case. Expertise is a long-term developmental process, resulting from rich instrumental experiences in the world and extensive practice. These cannot simply be handed to someone.’
All of which hints at the decisive advantage held by Kasparov over his machine opponent. Deep Blue had all the ‘talent’: the ability to search moves at a rate measured in tens of millions per second. But Kasparov, although limited to a derisory three moves per second, had the knowledge. A deep, fertile, and endlessly elaborate knowledge of chess: the configurations of real games, how they can be translated into successful outcomes, the structure of defensive and offensive positions, and the overall construction of competitive chess. Kasparov could look at the board and see what to do in the same way an experienced firefighter can confront a blazing building and see what to do. Deep Blue can’t.
It is worth noting something else here. You’ll remember that SF, the person who performed so well on the digit span task, was able to remember more than eighty numbers by relating them to his experiences as a competitive runner. The numbers 9 4 6 2, for example, became 9 minutes, 46.2 seconds – a very good time for running two miles. SF’s retrieval structure was, in effect, an ad hoc device derived from his life beyond the test.
Kasparov’s memory of chess positions, on the other hand, is embedded in the living, breathing reality of playing chess. When he sees a chessboard, he does not chunk the pattern by relating it to an altogether different experience but by perceiving it immediately as the Sicilian Defence or the Latvian Gambit. His retrieval structure is rooted within the fabric of the game. This is the most powerful type of knowledge, and is precisely the kind possessed by firefighters, top sportsmen, and other experts.
By now it should be obvious why Deep Blue’s gigantic advantage in processing speed was not sufficient to win – combinatorial explosion. Even in a game as simple as chess, the variables rapidly escalate beyond the capacity of any machine to compute. There are around thirty ways to move towards the beginning of a game, and thirty ways in which to respond. That amounts to around 800,000 possible positions after two moves each. A few moves after that, and the number of positions are measured in trillions. Eventually, there are more possible positions than there are atoms in the known universe.
To be successful, a player must cut down on the computational load by ignoring moves unlikely to result in a favourable outcome and concentrating on those with greater promise. Kasparov is able to do this by understanding the meaning of game situations. Deep Blue is not.
As Kasparov put it after winning game two of the six-game match: ‘Had I been playing the same game against a very strong human I would have had to settle for a draw. But I simply understood the essence of the end game in a way the computer did not. Its computational power was not enough to overcome my experience and intuitive appreciation of where the pieces should go.’
Gary Klein, the psychologist who studied the firefighters, wanted to double-check whether chess players really do make rapid decisions based on the perceptual chunking of patterns (as opposed to conducting brute-force searches, like computers).
He reasoned that if the chunking theory is correct, top chess players would make similar decisions even if the available time was dramatically reduced. So he tested chess masters under ‘blitz’ conditions, where each player has only five minutes on the clock, with around six seconds per move (in standard conditions there are forty moves in a ninety-minute period, allowing around two minutes, fifteen seconds per move).
Klein found that, for chess experts, the move quality hardly changed at all in blitz conditions, even though there was barely enough time to take the piece, move it, release it, and hit the timer.
Klein then tested the pattern-recognition theory of decision-making directly. He asked chess experts to think aloud as they studied mid-game positions. He asked them to tell him everything they were thinking, every move considered, including the poor ones, and especially the very first move considered. He found that the first move considered was not only playable but also in many cases the best possible move from all the alternatives.
This obliterates the presumption that chess is exclusively about computational force and processing speed. Like firefighters and tennis players, chess masters generate usable options as the first ones they think of. This looks magical when you first see it (particularly when chess masters are playing lots of games simultaneously), but that is because we have not seen the ten thousand hours of practice that have made it possible.
It is a bit like learning a language. At the beginning, the task of remembering thousands of words and fitting them together using abstract rules of grammar seems impossible. But after many years of experience, we can look at a random sentence and instantly comprehend its meaning. It is estimated that most English language users have a vocabulary of around 20,000 words. American psychologist Herbert Simon has estimated that chess masters command a comparable vocabulary of patterns, or chunks.
Now consider the scope of combinatorial explosion in games like rugby, football, tennis, ice hockey, American football, and the like. Even when scientists have invented simplified representations of these sports, they have quickly been overwhelmed by complexity. In robot football, for example, positions on the pitch are represented by 1,680 by 1,088 pixels. When you consider that a chessboard has eight by eight squares and that the pieces move in well-defined ways – unlike a football, which can fly anywhere at any time – you get some idea of the fiendish difficulty of designing a machine to compete without falling victim to information overload.
Now, here’s a description of Wayne Gretzky, arguably the greatest player in the history of ice hockey, taken from an article in the New York Times magazine in 1997:
Gretzky doesn’t look like a hockey player .. . Gretzky’s gift, his genius even, is for seeing ... To most fans, and sometimes even to the players on the ice, ice hockey frequently looks like chaos: sticks flailing, bodies falling, the puck ricocheting just out of reach.
But amid the mayhem, Gretzky can discern the game’s underlying pattern and flow, and anticipate what’s going to happen faster and in more detail than anyone else in the building. Several times during a game you’ll see him making what seem to be aimless circles on the other side of the rink from the traffic, and then, as if answering a signal, he’ll dart ahead to a spot where, an instant later, the puck turns up.
This is a perfect example of expert decision-making in practice: circumventing combinatorial explosion via advanced pattern recognition. It is precisely the same skill wielded by Kasparov, but on an ice hockey pitch rather than a chessboard. How was Gretzky able to do this? Let’s hear from the man himself: ‘I wasn’t naturally gifted in terms of size and speed; everything I did in hockey I worked for.’ And later: ‘The highest compliment that you can pay me is to say that I worked hard every day…That’s how I came to know where the puck was going before it even got there.’
All of which helps to explain a qualification that was made earlier in the chapter: you will remember that the ten-thousand-hour rule was said to apply to any complex task. What is meant by complexity? In effect, it describes those tasks characterized by combinatorial explosion; tasks where success is determined, first and foremost, by superiority in software (pattern recognition and sophisticated motor programmes) rather than hardware (simple speed or strength).
Most sports are characterized by combinatorial explosion: tennis, table tennis, football, ice hockey, and so on. Just try to imagine, for a moment, designing a robot capable of solving the real-time spatial, motor, and perceptual challenges necessary to defeat Roger