Bounce: The Myth of Talent and the Power of Practice. Matthew Syed

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СКАЧАТЬ 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 Federer on a tennis court. The complexities are almost impossible to define, let alone solve. It is only in sports like running and lifting – simple activities testing a single dimension such as speed or strength – that the design possibilities become manageable.

      Of course, not all expert decision-making is rapid and intuitive. In some situations, chess players are required to conduct deep searches of possible moves, and firefighters are required to think logically about the consequences of actions. So are top sportsmen and military commanders.

      But even in the most abstract decisions, experience and knowledge play a central role. In an experiment carried out by David Rumelhart, a psychologist at Stanford University, five times as many participants were able to figure out the implications of a logical expression when it was stated in a real setting (‘Every purchase over thirty dollars must be approved by the manager’) than when stated in a less meaningful way (‘Every card with a vowel on the front must have an integer on the back’).

      Earlier in this chapter we saw that the talent myth is disempowering because it causes individuals to give up if they fail to make rapid early progress. But we can now see that it is also damaging to institutions that insist on placing inexperienced individuals – albeit with strong reasoning skills – in positions of power.

      Think, for example, of the damage done to the governance of Britain by the tradition of moving ministers – the most powerful men and women in the country – from department to department without giving them the opportunity to develop an adequate knowledge base in any of them. It is estimated that the average tenure of a ministerial post in recent years in Britain has been 1.7 years. John Reid, the long-serving member of Tony Blair’s government, was moved from department to department no less than seven times in seven years. This is no less absurd than rotating Tiger Woods from golf to football to ice hockey to baseball and expecting him to perform expertly in every arena.

      What we decide about the relative importance of practice and knowledge on the one hand and talent on the other has major implications not just for ourselves and our families, but for corporations, sports, governments, and, indeed, the future of artificial intelligence.^*

      On 3 May 1997, Kasparov and Deep Blue went head-to-head for a second time. The hype was no less intense and the stakes no less high. IBM put up over a million dollars in prize money, and the world’s media descended upon the venue – this time the thirty-fifth floor of the Equitable Center on Seventh Avenue in New York – in even greater numbers (IBM would later estimate that the company gained more than $500 million in free publicity).

      But this time, Deep Blue was triumphant, defeating the world champion by two games to one, with three draws. It was a crushing blow for Kasparov, who stormed out of the venue. He would later allege that IBM had created playing conditions advantageous to Deep Blue and that they had refused to provide computer printouts which would have helped his preparation. He also made entirely unsubstantiated claims that IBM had cheated. He was not a good loser.

      What had happened over the course of the preceding fifteen months? How had Deep Blue managed to convert defeat into a famous victory? Firstly, the machine had been provided with double the processing СКАЧАТЬ