Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [64]
The parallels, Tenenbaum said, are deceiving. Watson, for example, appears to learn. But its learning comes from adjusting its judgments to feedback, moving toward the combinations that produce correct answers and away from errors. These “error-driven learning algorithms,” he said, are derived from experiments in behavioral psychology. “The animals do something, and they’re rewarded or they’re punished,” he said. That kind of learning may be crucial to survival, leading humans and many animals alike to recoil from flames, coiled snakes, and bitter, potentially poisonous, berries. But this describes a primitive level of brain function. What’s more, Watson’s learning laboratory was limited, extending only to its 75 gigabytes of data and the instructions of its algorithms. Outside that universe, Tenenbaum stressed, Watson knew nothing. And it formed no theories.
Ferrucci didn’t disagree. Watson had its limitations. One time, when Ferrucci learned that another scientist had disparaged Watson as an “idiot savant,” he said, “Idiot savant? I’ll take it!” While he objected to that term, which he viewed as demeaning, Ferrucci said he only wished that Watson could approach the question-answering mastery of humans like Kim Peek, the so-called megasavant played by Dustin Hoffman in the movie Rainman. Peek, who died in 2009, was a walking encyclopedia. He had read voluminously and seemed to recall every detail with precision. Yet he had grave physical and developmental shortcomings. His brain was missing the corpus callosum, the bundle of nerves connecting the two hemispheres. He had little meaningful interaction with people—with the exception of his father—and he did not appear to draw sophisticated conclusions from his facts, much less come up with theories. He was a stunted genius. But unlike Watson, he was entirely fluent in language. As far as Ferrucci was concerned, a Q-A machine with the language proficiency of a human was a dream. It would have boundless market potential. He would leave it to other kinds of machines to come up with theories.
The question was whether computers like Watson, products of this pragmatic, problem-solving (and profit-seeking) side of the AI world, were on a path toward higher intelligence. Within a decade, computers would likely run five hundred times as fast and would race through databases a thousand times as large. Within fifteen years, studies predicted that a single supercomputer would be able to carry out 1020 calculations per second. This was enough computing power to count every grain of sand on earth in a single second (assuming it didn’t have more interesting work to do). At the same time, the algorithms running such machines, each one resulting from decades of rigorous Darwinian sifting, would be smarter and more precise. Would these supercharged descendants of Watson still be in the business of simulating intelligence? Or could they make the leap to a human level, then advance beyond?
The AI community was full of doubters. And their concerns about the limitations of statistical crunchers like Watson stirred plenty of debate within the scientific community. Going back decades, the sparkling vision of AI was to develop machines that could think, know, and learn. Watson, many argued, landed its star spot on national television without accomplishing any of those goals. A human answering a Jeopardy question draws on “layers and layers of knowledge,” said MIT’s Sajit Rao. “There’s so much knowledge around every single word.” Watson couldn’t compare. “If you ask Watson what time it is,” wrote one computer scientist in an e-mail, “it won’t have an answer.”
If Watson hadn’t been so big, few would have cared. But the size and scope of the project, and the razzmatazz surrounding it, fueled resentment. Big Blue was a leading force in AI, and its focus on Jeopardy funneled more research dollars toward its statistical approach. What’s more, Watson was sure to hog the press. The publicity leading up to the man-machine Jeopardy showdown would likely shine a brighter spotlight