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Computers are very, very fast at making calculations. Moreover, they can be counted on to calculate faithfully—without getting tired or emotional or changing their mode of analysis in midstream. But this does not mean that computers produce perfect forecasts, or even necessarily good ones. The acronym GIGO (“garbage in, garbage out”) sums up this problem. If you give a computer bad data, or devise a foolish set of instructions for it to analyze, it won’t spin straw into gold. Meanwhile, computers are not very good at tasks that require creativity and imagination, like devising strategies or developing theories about the way the world works. Computers are most useful to forecasters, therefore, in fields like weather forecasting and chess where the system abides by relatively simple and well-understood laws, but where the equations that govern the system must be solved many times over in order to produce a good forecast. They seem to have helped very little in fields like economic or earthquake forecasting where our understanding of root causes is blurrier and the data is noisier. In each of those fields, there were high hopes for computer-driven forecasting in the 1970s and 1980s when computers became more accessible to everyday academics and scientists, but little progress has been made since then.

Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules. More often than not, a system like this is liable to translate “minister of agriculture” as “priest of farming.” Still, for math and chess it worked great, and the proponents of symbolic A.I. took it for granted that no activities signaled “general intelligence” better than math and chess.

There are precedents for such reclaiming—pejorative words like “queer” and even “slut,” for instance, which their targets have taken over and brandished with pride. But maybe a more apt comparison would be the word “nerd.” “Nerd” used to be a put-down—and it used to cover boys more often than girls. Like “bossy,” it wasn’t really that harsh, but it wasn’t nice, either. It actually had a gender dimension, too, because it called out brainy boys who were not athletic or aggressive. It was a dis of boys who lived in their heads and wore pocket protectors and ate their lunch indoors, playing chess. Just as “bossy” might be said to undermine female leadership, “nerd” might be said to have undermined male intellectualism. But now “nerd,” and its close cousin “geek,” are words that lots of people are happy to identify with, humble-bragging about their obsessive expertise. Brainiac techies can get rich these days, and that has helped spiff up the image of nerdery. John Green, an author of young-adult novels, and his brother Hank have developed a thriving online and off-line community of “nerdfighters,” girls as well as boys, who like to say that they fight for good with their brains and hearts, calculators and trombones. They have heartthrobs like the actor Benedict Cumberbatch. They find each other on Tumblr.

Bos­trom writes, “Artificial intelligence already outperforms human intelligence in many domains.” The examples range from chess to Scrabble. One program from 1981, called Eurisko, was designed to teach itself a naval role-playing game. After playing ten thousand matches, it arrived at a morally grotesque strategy: to field thousands of small, immobile ships, the vast majority of which were intended as cannon fodder. In a national tournament, Eurisko demolished its human opponents, who insisted that the game’s rules be changed. The following year, Eurisko won again—by forcing its damaged ships to sink themselves.