Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [63]
7. AI
ON A MIDSUMMER afternoon in 2010, a cognitive scientist at MIT named Joshua Tenenbaum took a few minutes to explain why the human brain was superior to a question-answering machine like Watson. He used the most convenient specimen of human cognition at hand, his own mind, to make his case. Tenenbaum, a youthful professor with sandy hair falling across his forehead and an easy smile, has an office in MIT’s imposing headquarters for research on brains, memory, and cognitive science. His window looks across the street at the cascading metallic curves of MIT’s Stata Center, designed by the architect Frank Gehry.
Tenenbaum is focusing his research on the computational basis of human learning and trying to replicate it with machines. His goal is to come up with computers whose intelligence reaches far beyond answering questions or finding correlations in masses of data. One day, he hopes, the systems he’s working on will come up with concepts and theories, the way humans do, sometimes basing them on just a handful of observations. They would make what he called inductive leaps, behaving more like Charles Darwin than, say, Google’s search engine or Watson. Darwin’s data—his studies of worms, pigeons, and a host of other plants and animals—was tiny by today’s standards; they would occupy no more than a few megabytes on a hard drive. Yet he came up with a theory that explained the evolution of life on earth. Could a computer do that?
Tenenbaum was working toward that distant vision, but for the moment his objective was more modest. He thought Watson acted smarter than it was, and he wanted to demonstrate why. He had recently read in a magazine about Watson’s mastery of Jeopardy’s Before and After clues, the ones that linked two concepts or people with a shared word in the middle. When asked about a candy bar that was a Supreme Court justice, Watson had quickly come up with “Who is Baby Ruth Ginsberg.”
Now Tenenbaum was creating a Before and After clue of his own. “How about this one?” he said. “A president who wrote a founding document and later led a rebellion against it.” The answer, a combination of the third president of the United States and the only president of the Confederacy: Thomas Jefferson Davis.
Tenenbaum’s point was that it took a team of gifted engineers to teach Watson how to handle these questions by devising clever algorithms. But humans, after seeing a single example of a Before and After clue, could build on it, not only figuring out how to respond to such questions but inventing new ones. “I know who Ruth Ginsberg is and I know what Baby Ruth is and I see how they overlap, and from that one example I can extract that template,” he said. “I don’t have to be programmed with that question.” We humans, he explained, create our own algorithms on the fly.
As in many fields of science, researchers in Artificial Intelligence have long fallen into two groups, pragmatists and visionaries. And most of the visionaries, including Tenenbaum, argue that machines like Watson merely simulate intelligence by racing through billions of correlations. Watson and its kin don’t really “know” or “understand” anything. Watson can ace Jeopardy clues on Shakespeare, but only because the ones and zeros that spell out “Shakespeare” pop up on lists and documents near other strings of ones and zeros representing playwrights, England, Hamlet, Elizabethan, and so on. It lacks anything resembling awareness. Most reject the suggestion that the clusters of data nestled among its transistors mirror the memories encoded chemically in the human brain or that Watson’s search for Jeopardy answers, and its statistical methods of balancing one candidate answer with another, mimic what goes