Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [14]
Several years later, the U.S. Defense Department’s Advanced Research Projects Agency (DARPA) would fund Rao’s research for a program called Mind’s Eye. The idea was to teach machines not only to recognize objects but to be able to reason about what they were doing, where they might have come from. This work, they hoped, would lead to smart surveillance cameras, which would mean that computers could replace humans in the tedious and exhausting task of monitoring a spot—what the Pentagon calls “persistent stare.” Instead of simply recording movements, these systems would interpret them. If a man in Afghanistan went into a building carrying a package and emerged without it, the system would conclude that he had left it there. If he walked toward another person with a suitcase in his hand, it would predict that he was going to give it to him. A seeing and thinking machine that could generate hypotheses based on observations might zero in on potential roadside bombs or rooftop snipers. This type of intelligence, according to DARPA, would extend computer surveillance from objects to actions—from nouns to verbs.
This skill required the computer to understand relationships—precisely the stumbling block of IBM’s Piquant as it struggled with questions in the TRec competition. But potential breakthroughs such as Mind’s Eye were still in the infant stage of research and wouldn’t be ready for years—certainly not in time to give a Jeopardy machine a dose of human smarts. What’s more, Ferrucci was busy managing another big software project. So after consulting his team and assembling the discouraging evidence, he broke the news to a disappointed Paul Horn. His team would not pursue the Jeopardy challenge. It was just too hard to guarantee results on a schedule.
Free of that distraction, the Q-A team returned to its work, preparing Piquant for the next TRec competition. As it turned out, though, Ferrucci had won them only a respite, and a short one at that. Months later, in the summer of 2006, Horn returned with exactly the same question: How about Jeopardy?
Reluctantly, Ferrucci and his small Q-A team gathered in a small room at the Hawthorne research center, a ten-minute drive south from Yorktown. (It was a far less elegant structure, a cuboid of black glass in an office park. But unlike Yorktown, where the public spaces were bathed in natural light and the offices windowless, Hawthorne’s offices did have views, mostly of parking lots.) The discussion followed the familiar, depressing lines: the team’s travails in the TRec competitions, the insanely broad domain of Jeopardy, and the difficulty of coming up with answers and a betting strategy in three to five seconds. TRec had no time limit at all, and the computer often churned away for minutes trying to answer a single question.
While the team talked, Ferrucci sat at the back of the room, uncharacteristically quiet. He had a laptop open and was typing away. He was looking up Jeopardy clues online and then searching for answers on Google. The answers certainly didn’t pop up. But in many cases, the search engine led to the right neighborhood. He started thinking about the technologies needed to refine Google’s vague pointer to a precise answer. It would require much of the tech muscle of IBM. He’d have to bring in top natural-language researchers and experts in machine learning. To speed up the answering process, he’d need to spread out the computing to hundreds or even thousands of machines. This would require a crack hardware unit. His team would also need to educate the machine in strategy. Ferrucci had a few colleagues who focused on game theory. Several