Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [16]
This flexibility isn’t a weakness of language but a strength. Humans need words to be inexact; if they were too precise, each person would have a unique vocabulary of several billion words, all of them unintelligible to everyone else. You might have a unique word for the sip of coffee you just took at 7:59 A.M., which was flavored with the anxiety about the traffic in the Lincoln Tunnel or along Paris’s Périphérique. (That single word would be as useless to you as to everyone else. A word has to be used at least twice to have any purpose.)
Each word is a lingua franca, a fragment of a clumsy common language. Imagine a man saying a simple sentence to a friend: “I’m weary.” He’s thinking about something, but what is it? Has he carried a load a long way in the sun? Does he have a sick child or financial troubles? His friend certainly has different ideas, based on his own experience, about what “weary” means. In addition to the various contexts, it might send other signals. Maybe where he comes from, the word has a slightly rarefied feel, and he’s wondering whether his friend is trumpeting his sophistication. Neither one knows exactly what the other is thinking. But that single word, “weary,” extends an itsy bridge between them.
Now, with that bridge in place, the word shared, they dig deeper to see if they can agree on its meaning. They study each other’s expression and tone of voice. As Carbonell noted, context is crucial. Someone who has won the Boston Marathon might be contentedly weary. Another, in a divorce hearing, is anything but. One person may slack his jaw in an exaggerated way, as if to say “Know what I mean?” In this tiny negotiation, far beyond the range and capabilities of machines, two people can bridge the gap between the formal definition of a word and what they really want to say.
It’s hard to nail down the exact end of AI winter. A certain thaw set in when IBM’s computer Deep Blue bested Garry Kasparov in their epic 1997 showdown. Until that match, human intelligence, with its blend of historical knowledge, pattern recognition, and the ability to understand and anticipate the behavior of the person across the board, ruled the game. Human grandmasters pondered a rich set of knowledge, jewels that had been handed down through the decades—from Bobby Fischer’s use of the Sozin Variation in his 1972 match with Boris Spassky to the history of the Queen’s Gambit Denied. Flipping through scenarios at about three per second—a glacial pace for a computing machine—these grandmasters looked for a flash of inspiration, an insight, the hallmark of human intelligence.
Equally important, chess players tried to read the minds of their foes. This is a human specialty, a mark of our intelligence. Cognitive scientists refer to it as “theory of mind”; children develop it at about age four. It’s what enables us to imagine what someone else is experiencing and to build large and convoluted structures based on such analysis. “I wonder what he was thinking I knew when I told him . . .” Most fiction, from Henry James to Elmore Leonard, revolves around this very human analysis, something other species—and computers—cannot even approach. (It’s also why humans make such expert liars.)
Unlike previous AI visions, in which a computer would “think” more or less the way we do, Deep Blue set off on a different course. It played on the strengths of a supercomputer: a fabulous memory and extraordinary calculating speed. Statistical approaches to machine intelligence had been around since the dawn of AI, but the numbers mavens had never witnessed anything