Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [96]
Tesauro gained global renown in the ’90s when he developed the computer that mastered the five-thousand-year-old game of backgammon. (Sumerians, as Roger Craig may already know, played a variation of it in the ancient city of Ur.) What distinguished Tesauro’s approach was that he didn’t teach the machine a thing. Using neural networks, his system, known as TD-Gammon, learned on its own. Following Tesauro’s instructions, it played games against itself, millions of them. Each time it won or lost, it drew conclusions. Certain moves in certain situations led more often to victory, others to defeat. Although this was primitive feedback—no more than thumbs up, thumbs down—each game delivered a minuscule improvement, Tesauro said. Over the course of millions of games, the machine developed a repertoire of winning moves for countless scenarios. Tesauro’s machine beat champions.
Tesauro’s first goal was to run millions of simulated Jeopardy games, just as he had with backgammon. For this he needed mathematical models of three players, Watson and two humans. Modeling Watson wasn’t so hard. “We knew all of its algorithms,” he said, and the team had precise statistics on every aspect of its behavior. The human players were more complicated. Tesauro had to pull together statistics on the thousands of humans who had played Jeopardy: how often they buzzed in, their precision in different levels of clues, their betting patterns for Daily Doubles and Final Jeopardy. From these, the IBM team pieced together statistical models of two humans.
Then they put them into action against the model of Watson. The games had none of the life or drama of Jeopardy—no suspense, no jokes, no jingle while the digital players came up with their Final Jeopardy responses. They were only simulations of the scoring dynamics of Jeopardy. Yet they were valuable. After millions of games, Tesauro was able to calculate the value of each clue at each state of the game. If Watson was in second place, trailing by $1,500 with $14,400 left on the board, what size bet on a Daily Double maximized its chance of winning? The answer changed with every move, and Tesauro was mapping it all out. Humans, when it came to betting, only had about five seconds to make a decision. They went with their gut. Watson, like its number-crunching brethren in advertising and medicine, was turning its pile of data into science.
The science, it turned out, was a bit scary. Watson’s model was based on the record it had established following the simple heuristics. And studies showed that the machine, much like risk-averse humans, had been dramatically underbetting. In many stages of the game, according to Tesauro’s results, the computer could maximize its chances by wagering nearly everything it had. (This wasn’t always the case. If Watson enjoyed a big lead late in the game, it made sense to minimize a bet.) When Tesauro adjusted Watson’s strategy toward a riskier blend of bets, it started winning more of the simulated games. He and Gondek concluded that in many scenarios, Watson should bet the farm. “When we first went to Dave Ferrucci about this,” Tesauro recalled, “he turned pale as a sheet and said, ‘You want to do what?’”
“We showed him all the extra wins we were getting with this,” Gondek said. “But he looked at the colossal bets we were making and said, ‘What if you get them wrong?’”
The conflict between rational analysis and intuition were playing out right in the IBM War Room. And Ferrucci, much like the humans who placed small, safe bets every evening on Jeopardy, was turning away from statistics and