Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [12]
The second-place player, Bray, was the only one to get it right: “Who was Edward VIII?” Yet he had bet only $500. It was a strange number. It placed him $100 behind the leader, not ahead of her. But the bet kept him beyond the reach of the third-place player. Most players bet at least something on a clue. If Schwartz had wagered and missed, he would win. Indeed, Schwartz missed the clue. She didn’t even bother guessing. But she had bet nothing, leaving herself $100 ahead and winning the game.
The betting in Final Jeopardy, Ferrucci saw, might actually play to the strength of a computer. A machine could analyze betting patterns over thousands of games. It could crunch the probabilities and devise optimized strategies in a fraction of a second. “Computers are good at that kind of math,” he said.
It was the rest of Jeopardy that appeared daunting. The game featured complex questions and a wide use of puns posing trouble for literal-minded computers. Then there was Jeopardy’s nearly boundless domain. Smaller and more specific subject areas were easier for computers, because they offered a more manageable set of facts and relationships to master. They provided context. A word like “leak,” for example, had a specific meaning in deep-sea drilling, another in heart surgery, and a third in corporate press relations. A know-it-all computer would have to recognize different contexts to keep the meanings clear. And Jeopardy’s clues took the concept of a broad domain to a near-ludicrous extreme. The game had an entire category on Famous Understudies. Another was on the oft-forgotten president Rutherford B. Hayes. Worse, from a computer architect’s point of view, the game demanded answers within seconds—and penalized players for getting them wrong. A Jeopardy machine, just like the humans on the show, would have to store all of its knowledge in its internal memory. (The challenge, IBM figured, wouldn’t be nearly as impressive if a bionic player had access to unlimited information on the Web. What’s more, Jeopardy would be unlikely to accept a Web-surfing contestant, since others didn’t have the same privilege.) Beating humans in Jeopardy, it seemed, was more than a stretch goal. It appeared impossible and spelled potential disaster for researchers. To embarrass the company on national television—or, more likely, to flame out before even getting there—was no way to manage a career.
Ferrucci’s pessimism was also grounded in experience. In annual government competitions, known as TRec (Text Retrieval Conference), his question-answering (Q-A) team developed a system called Piquant. It struggled far below Jeopardy levels with a much easier test. In TRec, the competing teams were each given a relatively small “corpus” of about one million documents. They then had to train the machines to answer questions based on the material. (In one version from 2004, several of the questions had to do with Tom Cruise and his ex-wife.)
In answering these questions, the computer, for all its processing power and memory, resembled nothing so much as a student with serious brain damage. An apparently simple question could turn it into knots. In 2005, it was asked: “What is Francis Scott Key best known for?” The first job was to determine which of those words represented