Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [35]
It was up to Chu-Carroll and a few of her colleagues to map out the machine’s reading curriculum. Chu-Carroll had black bangs down to her eyes and often wore sweatshirts and jeans. Like practically everyone else on the team, she had a doctorate in computer science, hers from the University of Delaware. She had worked for five years at Lucent Technology’s Bell Labs, in New Jersey. There she taught machines how to participate in a dialogue and how to modulate their voices to communicate different signals. (Lucent was developing automated call centers.) When Chu-Carroll came to IBM in 2001, joining her husband, Mark, she plunged into building Q-A technologies. (Mark later left for Google.)
In mid-2007, the nascent Jeopardy system wasn’t really a machine at all. Fragments of a Jeopardy player existed as a collection of software programs, some of them hand-me-downs from the recent bake-off, all of them easy to load onto a laptop. As engineers pieced together an architecture for the new system, Chu-Carroll pondered a fundamental question: How knowledgeable did this computer really need to be? One of its forebears, Basement Baseline, had hunted down its answers on the Web. Blue J wouldn’t have that luxury. So as Chu-Carroll sat down for Blue J’s first day of school, her pupil was a tabula rasa.
She quickly turned to a promising resource. James Fan had already demonstrated the value of Wikipedia for answering a small subsection of Jeopardy clues. “It related to popular culture and what people care about,” Chu-Carroll said. So she set to work extracting much of the vast corpus of Wikipedia articles from the online site and putting them into a format that Blue J could read.
But how about books? Gutenberg.org offered hundreds of classics for free, along with a ranking of the most popular downloads. Chu-Carroll could feed any or all of them to Blue J. After all, words didn’t take up much space. Moby Dick, for example, was only 1.5 megabytes. Photographs on new camera phones packed more bits than that. So one day she downloaded the Gutenberg library and gave Blue J a crash course on the Great Books.
“It wasn’t a smart move,” she later admitted. “One of the most popular books on Gutenberg was a manual for surgeons from a hundred years ago.” This meant that when faced with a clue about modern medicine, Blue J could be consulting a source unschooled in antibiotics, CAT scans, and HIV, one fixated instead on scurvy, rickets, and infections (not to mention amputations) associated with trench warfare. “I’m not sure why that book is so popular,” said Chu-Carroll. “Are people doing at-home surgery?”
Whatever their motives, most human readers knew exactly what they were getting when downloading the medical relic. If not, they quickly found out. In addition to surgical descriptions, the book contained extraordinary pictures of exotic and horrifying conditions, such as elephantiasis of the penis. Aside from these images, the book’s interest was largely historical. Humans had little trouble placing it in this context. Chu-Carroll’s pupil, by contrast, had a maddening habit endemic among its ilk: It tended to take every source at its word.
Blue J’s literal-mindedness posed the greatest challenge at every step of its education. Finding suitable data for this gullible machine was only the first task. Once Blue J had its source material, from James Joyce to archives of the Boing-Boing blog, the IBM team would have to teach it to make sense of all those words: to place names and facts into context and to come to grips with how they were related to one another. Hamlet, to pick one example, was related