Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [109]
Looking back, it was fortunate for IBM that Jeopardy had insisted on building a finger for Watson so that it could press the physical buzzer. This demand ten months earlier had initially irked Ferrucci, who worried that Jeopardy’s executives would continue to call for changes in their push for a balanced match. But if Watson had beaten Jennings and Rutter to the buzz with its original (and faster) electronic signal, the match certainly would have been widely viewed as unfair— just as Harry Friedman and his team had warned all along.
Still, despite Watson’s virtuosity with the buzzer and its remarkable performance on Jeopardy clues, the machine’s education is far from complete. As this question-answering technology expands from its quiz show roots into the rest of our lives, engineers at IBM and elsewhere must sharpen its understanding of contextual language. And they will. Smarter machines will not call Toronto a U.S. city, and they will recognize the word “missing” as the salient fact in any discussion of George Eyser’s leg. Watson represents merely a step in the development of smart machines. Its answering prowess, so formidable on a winter afternoon in 2011, will no doubt seem quaint in a surprisingly short time.
Two months before the match, Ken Jennings sat in the empty Wheel of Fortune studio on the Sony lot, thinking about a world teeming with ever-smarter computers. “It does make me a little sad that a know-it-all like me is not the public utility that he used to be,” he said. “There used to be a guy in every office, and everyone would know which cubicle you would go to find out things. ‘What’s the name of the bassist in that band again?’ Or ‘What’s the movie where . . . ?’ Or ‘Who’s that guy on the TV show . . . he’s got the mustache?’ You always know who the guy to ask is, right?”
I knew how he felt. And it hit me harder after the match, as I made my way from the giddy reception through a long, narrow corridor toward the non-VIP parking lot. Halfway down, in an office strewn with wires and cameras, stood a discouraged Jennings and Rutter. They were waiting to be filmed for their postgame reflections. It had been a long and draining experience for them. What’s more, the entire proceeding had been a tribute to the machine. Even the crowd was pulling for it. “We were the away team,” Jennings said. And in the end, the machine delivered a drubbing.
Yet I couldn’t regret the outcome. I’d come to know and appreciate the other people in this drama, the ones who had devoted four years to building this computer. For them, a loss would have been even more devastating than it was for Jennings and Rutter. And unlike the two Jeopardy stars, the researchers had to worry about what would come next. Following a loss, there would be extraordinary pressure to fine-tune the machine for a rematch. Watson, like Deep Blue, wasn’t likely to retire from the game without winning. The machine could always get smarter. This meant that instead of a deliverance from Jeopardy, the team might be plunged back into it. This time, though, instead of a fun and unprecedented event, it would have the grim feel of a do-or-die revenge match. For everyone concerned, it was time to move on. Ferrucci, his team, and their machine all had other horizons to explore. I did too.
But the time I spent with Watson’s programmers led me to think more than ever about the programming of our own minds. Of course, we’ve had to adapt our knowledge and skills for millennia. Many of us have decided, somewhere along the way, that we don’t need to know how to trap a bear, till a field, carry out long division, or read a map. But now, as Jennings points out, the value of knowledge itself is in flux. In a sense, each of us faces the question that IBM’s Jeopardy team grappled with as they outfitted Watson with gigabytes of data and operating instructions. What makes sense to store up there? And what cognitive work should be farmed out to