Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [79]
While her colleagues steered Watson away from gaffes, Chu-Carroll was concentrating on Final Jeopardy, an area of mounting concern for Ferrucci’s team. Final Jeopardy was often decisive. Throughout Watson’s training, the team had studied and modeled all of the clues as a single group. They knew from the beginning that the Final Jeopardy clues were trickier—“less direct, more implicit,” in Chu-Carroll’s words—but their data set of these clues was much smaller, only one sixty-first of the total. Because of this, the computer was still treating the Final Jeopardy clue like every other clue on the board, coming up with its answer in three to five seconds—and then just waiting as the thirty-second jingle went through its sixty-four notes. This was enough time for trillions of additional calculations. Wasn’t there a way to take advantage of the extra seconds?
The team was not about to devise new ways to find answers. That would require major research. But Watson could take more time to analyze the answers it collected. The method, like most of Watson’s cognitive work, would require exhaustive and repetitive computing. The idea was to generate from each answer a series of declarative statements, then check to see if they looked right. In the category English Poets, for example, one recent Final Jeopardy clue had read: “Translator Edward Fitzgerald wrote that her 1861 ‘death is rather a relief to me . . . no more Aurora Leighs, thank God.’” Let’s say Watson came up with measurable confidence in three potential names, Alfred Lord Tennyson, Emily Dickinson, and Elizabeth Barrett Browning. It could then proceed to craft statements, putting each name in the following sentences: “_____ died in 1861,” “_______ wrote Aurora Leigh,” “_______ was an English poet.” Naturally, some of the sentences would turn out to be foolish, perhaps: “_________ found relief in death” or “________ died, thank God.” In any case, for each of dozens of sentences, Watson would race through its database looking for matches. This represented an immense amount of work. But the results could boost its confidence in the correct response—“Who is Elizabeth Barrett Browning?”—and guide it toward acing Final Jeopardy.
James Fan, meanwhile, was going over clues in which Watson failed to understand the subject. At one meeting at the Hawthorne labs, he brought up an especially puzzling one. In the category Postal Matters, it asked: “The first known air mail service took place in Paris in 1870 by this conveyance.” From its analysis, Watson could conclude that it was supposed to find a “conveyance.” That was the lexical answer type, or LAT. But what was a conveyance? In all of the ontologies it had on hand, there was no such grouping. There were groups of trees, colors, presidents, even flavors of ice cream—but no “conveyances.” And if Watson looked up the word, it would find vague references to everything from communication to the transfer of legal documents. One of its meanings involved transport, but the computer would hardly know to focus its search there.
What to do? Fan was experimenting with a new grouping of LATs. At a meeting of one algorithm team on a June afternoon, he started to explain how he could prepare Watson for what he called weird LATs.
Ferrucci didn’t like the sound of it. “We don’t have any way to mathematically classify ‘weird,’” he objected. “That’s a word you just introduced.” Run-of-the-mill LATs, such as flowers, presidents, or diseases, provided Watson with vital intelligence, dramatically narrowing its search. But an amorphous grouping of “weird” words, he feared, would send the computer off in bizarre directions,