Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [38]
But others proved devilishly hard. This clue initially left Blue J befuddled: “In nine-ball, whenever you sink this, it’s a scratch.” Blue J, Fan said, immediately identified “this” as the object to look for. But what was “this?” The computer had to analyze the rest of the sentence. “This” was something that sank. But it was not related, at least in any clear way, to vessels, the most common sinking objects. To identify the LAT, Blue J would have to investigate the two other distinguishing words in the clue, “nine-ball” and “scratch.” They led the computer to the game of pool and, eventually, to the answer (“What is a cue ball?”).
Sometimes the LAT remained a complete mystery. The computer, Fan said, had all kinds of trouble figuring out what to look for in this World Leaders clue: “In 1984, his grandson succeeded his daughter to become his country’s prime minister.” Should the computer look for the grandson? The daughter? The country? Any human player would quickly understand that it was none of the above. The trick was looking for a single person whose two roles went unmentioned: a father and a grandfather. To unearth this, Blue J would have had to analyze family relationships. In the end, it failed, choosing the grandson (“Who is Rajiv Gandhi?”). In its list of answers, Blue J did include the correct name (“Who is Nehru?”), but it had less confidence in it.
Troubles with specific clues didn’t matter. Even Ken Jennings only won the buzz 62 percent of the time. Blue J could afford to pass on some. The important thing was to fix chronic mistakes and to orient the machine to succeed on as many clues as possible. In previous weeks, Fan and his colleagues had identified twenty-five hundred different LATs in Jeopardy clues and ranked them by their frequency. The easiest for Blue J were the most specific. The machine could zero in on songs, kings, criminals, or plants in a flash, but most of them were more vague. “He,” for example, was the most common, accounting for 2.2 percent of the clues. Over the coming months, Fan would have to teach Blue J how to explore the rest of each clue to figure out exactly what kind of “he” or “this” it should look for.
It was possible, Ferrucci thought, that someday a machine would replicate the complexity and nuance of the human mind. In fact, in IBM’s Almaden research labs, on a California hilltop high above Silicon Valley, a scientist named Dharmendra Modha was building a simulated brain boasting seven hundred million electronic neurons. Within years, he hoped to map the brain of a cat, then a monkey, and eventually a human. But mapping the human brain, with its hundred billion neurons and trillions or quadrillions of connections among them, was a long-term project. With time, it might result in a bold new architecture for computing that would lead to a new level of computer intelligence. Perhaps then machines would come up with their own ideas, wrestle with concepts, appreciate irony, and think more like humans.
But such a machine, if it was ever built, would not be ready for Ferrucci. As he saw it, his team had to produce a functional Jeopardy machine within two years. If Harry Friedman didn’t see a viable machine by 2009, he would never green-light the man-machine match for late 2010 or early 2011. This deadline compelled Ferrucci and his team to assemble their machine with existing technology—the familiar silicon-based semiconductors, servers whirring through billions of calculations and following instructions from lots of software programs that already existed. In its guts, Blue J would not be so different from the ThinkPad Ferrucci lugged from one meeting to the next. Its magic would have to come from its massive scale, inspired design, and carefully tuned