At the dawn of the computer age, in 1950, the influential Bell Labs engineer Claude Shannon published a paper in Philosophical Magazine called “Programming a Computer for Playing Chess.” The creation of a “tolerably good” computerized chess player, he argued, was not only possible but would also have metaphysical consequences. It would force the human race “either to admit the possibility of a mechanized thinking or to further restrict [its] concept of ‘thinking.’” He went on to offer an insight that would prove essential both to the development of chess software and to the pursuit of artificial intelligence in general. A chess program, he wrote, would need to incorporate a search function able to identify possible moves and rank them according to how they influenced the course of the game. He laid out two very different approaches to programming the function. “Type A” would rely on brute force, calculating the relative value of all possible moves as far ahead in the game as the speed of the computer allowed. “Type B” would use intelligence rather than raw power, imbuing the computer with an understanding of the game that would allow it to focus on a small number of attractive moves while ignoring the rest. In essence, a Type B computer would demonstrate the intuition of an experienced human player.
When Shannon wrote his paper, he and everyone else assumed that the Type A method was a dead end. It seemed obvious that, under the time restrictions of a competitive chess game, a computer would never be fast enough to extend its analysis more than a few turns ahead. As Kasparov points out, there are “over 300 billion possible ways to play just the first four moves in a game of chess, and even if 95 percent of these variations are terrible, a Type A program would still have to check them all.” In 1950, and for many years afterward, no one could imagine a computer able to execute a successful brute-force strategy against a good player. “Unfortunately,” Shannon concluded, “a machine operating according to the Type A strategy would be both slow and a weak player.”
Type B, the intelligence strategy, seemed far more feasible, not least because it fit the scientific zeitgeist. Fascination with digital computers intensified during the 1950s, and the so-called “thinking machines” began to influence theories about the human mind. Many scientists and philosophers came to assume that the brain must work something like a digital computer, using its billions of networked neurons to calculate thoughts and perceptions. Through a curious kind of circular logic, this analogy in turn guided the early pursuit of artificial intelligence: if you could figure out the codes that the brain uses in carrying out cognitive tasks, you’d be able to program similar codes into a computer. Not only would the machine play chess like a master, but it would also be able to do pretty much anything else that a human brain can do. In a 1958 paper, the prominent AI researchers Herbert Simon and Allen Newell declared that computers are “machines that think” and, in the near future, “the range of problems they can handle will be coextensive with the range to which the human mind has been applied.” With the right programming, a computer would turn sapient.
It took only a few decades after Shannon wrote his paper for engineers to build a computer that could play chess brilliantly. Its most famous victim: Garry Kasparov.
One of the greatest and most intimidating players in the history of the game, Kasparov was defeated in a six-game bout by the IBM supercomputer Deep Blue in 1997. Even though it was the first time a machine had beaten a world champion in a formal match, to computer scientists and chess masters alike the outcome wasn’t much of a surprise. Chess-playing computers had been making strong and steady gains for years, advancing inexorably up the ranks of the best human players. Kasparov just happened to be in the right place at the wrong time.
But the story of the computer’s victory comes with a twist. Shannon and his contemporaries, it turns out, had been wrong. It was the Type B approach — the intelligence strategy — that ended up being the dead end. Despite their early optimism, AI researchers utterly failed in getting computers to think as people do. Deep Blue beat Kasparov not by matching his insight and intuition but by overwhelming him with blind calculation. Thanks to years of exponential gains in processing speed, combined with steady improvements in the efficiency of search algorithms, the computer was able to comb through enough possible moves in a short enough time to outduel the champion. Brute force triumphed. “It turned out that making a great chess-playing computer was not the same as making a thinking machine on par with the human mind,” Kasparov reflects. “Deep Blue was intelligent the way your programmable alarm clock is intelligent.”
The history of computer chess is the history of artificial intelligence. After their disappointments in trying to reverse-engineer the brain, computer scientists narrowed their sights. Abandoning their pursuit of human-like intelligence, they began to concentrate on accomplishing sophisticated, but limited, analytical tasks by capitalizing on the inhuman speed of the modern computer’s calculations. This less ambitious but more pragmatic approach has paid off in areas ranging from medical diagnosis to self-driving cars. Computers are replicating the results of human thought without replicating thought itself. If in the 1950s and 1960s the emphasis in the phrase “artificial intelligence” fell heavily on the word “intelligence,” today it falls with even greater weight on the word “artificial.”
Particularly fruitful has been the deployment of search algorithms similar to those that powered Deep Blue. If a machine can search billions of options in a matter of milliseconds, ranking each according to how well it fulfills some specified goal, then it can outperform experts in a lot of problem-solving tasks without having to match their experience or insight. More recently, AI programmers have added another brute-force technique to their repertoire: machine learning. In simple terms, machine learning is a statistical method for discovering correlations in past events that can then be used to make predictions about future events. Rather than giving a computer a set of instructions to follow, a programmer feeds the computer many examples of a phenomenon and from those examples the machine deciphers relationships among variables. Whereas most software programs apply rules to data, machine-learning algorithms do the reverse: they distill rules from data, and then apply those rules to make judgments about new situations.
In modern translation software, for example, a computer scans many millions of translated texts to learn associations between phrases in different languages. Using these correspondences, it can then piece together translations of new strings of text. The computer doesn’t require any understanding of grammar or meaning; it just regurgitates words in whatever combination it calculates has the highest odds of being accurate. The result lacks the style and nuance of a skilled translator’s work but has considerable utility nonetheless. Although machine-learning algorithms have been around a long time, they require a vast number of examples to work reliably, which only became possible with the explosion of online data. Kasparov quotes an engineer from Google’s popular translation program: “When you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.”
The pragmatic turn in AI research is producing many such breakthroughs, but this shift also highlights the limitations of artificial intelligence. Through brute-force data processing, computers can churn out answers to well-defined questions and forecast how complex events may play out, but they lack the understanding, imagination, and common sense to do what human minds do naturally: turn information into knowledge, think conceptually and metaphorically, and negotiate the world’s flux and uncertainty without a script. Machines remain machines.
That fact hasn’t blunted the public’s enthusiasm for AI fantasies. Along with TV shows and movies featuring scheming computers and bloody-minded robots, we’ve seen a slew of earnest nonfiction books with titles like Superintelligence, Smarter Than Us, and Our Final Invention, all suggesting that machines will soon be brainier than we are. The predictions echo those made in the 1950s and 1960s, and, as before, they’re founded on speculation, not fact. Despite monumental advances in hardware and software, computers give no sign of being any nearer to self-awareness, volition, or emotion. Their strength — what Kasparov describes as an “amnesiac’s objectivity” — is also their weakness.
In addition to questioning the common wisdom about artificial intelligence, Kasparov challenges our preconceptions about chess. The game, particularly when played at its highest levels, is far more than a cerebral exercise in logic and calculation, and the expert player is anything but a stereotypical egghead. The connection between chess skill and the kind of intelligence measured by IQ scores, Kasparov observes, is weak at best. “There is no more truth to the thought that all chess players are geniuses than in saying that all geniuses play chess,” he writes. “[O]ne of the things that makes chess so interesting is that it’s still unclear exactly what separates good chess players from great ones.”
Chess is a grueling sport. It demands stamina, resilience, and an aptitude for psychological warfare. It also requires acute sensory perception. “Move generation seems to involve more visuospatial brain activity than the sort of calculation that goes into solving math problems,” writes Kasparov, referring to recent neurological experiments. To the chess master, the board’s 64 squares define not just an abstract geometry but an actual terrain. Like figures on a landscape, the pieces form patterns that the master, drawing on years of experience, reads intuitively, often at a glance. Methodical analysis is important, too, but it is carried out as part of a multifaceted and still mysterious thought process involving the body and its senses as well as the brain’s neurons and synapses.
The contingency of human intelligence, the way it shifts with health, mood, and circumstance, is at the center of Kasparov’s account of his historic duel with Deep Blue. Having beaten the machine in a celebrated match a year earlier, the champion enters the 1997 competition confident that he will again come out the victor. His confidence swells when he wins the first game decisively. But in the fateful second game, Deep Blue makes a series of strong moves, putting Kasparov on the defensive. Rattled, he makes a calamitous mental error. He resigns the game in frustration after the computer launches an aggressive and seemingly lethal attack on his queen. Only later does he realize that his position had not been hopeless; he could have forced the machine into a draw. The loss leaves Kasparov “confused and in agony,” unable to regain his emotional bearings. Though the next three games end in draws, Deep Blue crushes him in the sixth and final game to win the match.
One of Kasparov’s strengths as a champion had always been his ability to read the minds of his adversaries and hence anticipate their strategies. But with Deep Blue, there was no mind to read. The machine’s lack of personality, its implacable blankness, turned out to be one of its greatest advantages. It disoriented Kasparov, breeding doubts in his mind and eating away at his self-confidence. “I didn’t know my opponent at all,” he recalls. “This intense confusion left my mind to wander to darker places.” The irony is that the machine’s victory was as much a matter of psychology as of skill. 
If Kasparov hadn’t become flustered, he might have won the 1997 match. But that would have just postponed the inevitable. By the turn of the century, the era of computer dominance in chess was well established. Today, not even the grandest of grandmasters would bother challenging a computer to a match. They know they wouldn’t stand a chance.
But if computers have become unbeatable at the board, they remain incapable of exhibiting what Kasparov calls “the ineffable nature of human chess.” To Kasparov, this is cause for optimism about the future of humanity. Unlike the eight-by-eight chessboard, the world is an unbounded place, and making sense of it will always require more than mathematical or statistical calculations. The inherent rigidity of computer intelligence leaves plenty of room for humans to exercise their flexible and intuitive intelligence. If we remain vigilant in turning the power of our computers to our own purposes, concludes Kasparov, our machines will not replace us but instead propel us to ever-greater achievements.
One hopes he’s right. Still, as computers become more powerful and more adept at fulfilling our needs, there is a danger. The benefits of computer processing are easy to measure — in speed, in output, in dollars — while the benefits of human thought are often impossible to express in hard numbers. Given contemporary society’s worship of the measurable and suspicion of the ineffable, our own intelligence would seem to be at a disadvantage as we rush to computerize more and more aspects of our jobs and lives. The question isn’t whether the subtleties of human thought will continue to lie beyond the reach of computers. They almost certainly will. The question is whether we’ll continue to appreciate the value of those subtleties as we become more dependent on the mindless but brutally efficient calculations of our machines. In the face of the implacable, the contingent can seem inferior, its strengths appearing as weaknesses.
Near the end of his book, Kasparov notes, with some regret, that “humans today are starting to play chess more like computers.” Once again, the ancient game may be offering us an omen.
Nicholas Carr is the author of several books about computers and culture, including The Shallows, The Glass Cage, and, most recently, Utopia Is Creepy.
 A bit of all-too-human deviousness was also involved in Deep Blue’s win. IBM’s coders, it was later revealed, programmed the computer to display erratic behavior — delaying certain moves, for instance, and rushing others — in an attempt to unsettle Kasparov. Computers may be innocents, but that doesn’t mean their programmers are.