Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [65]
In sum, from a skeptic’s view, the machine was too dumb, too ignorant, too famous, and too rich. (In that sense, IBM’s computer resembled lots of other television stars. And, interestingly enough, the resentment within the field mirrored the combination of envy and contempt that serious actors feel for the celebrities on reality TV.)
These shortcomings aside, Watson had one quality that few could ignore. In the broad realm of Jeopardy, it worked. It made sense of most of the clues, even those in complex English, and it came up with answers within a few seconds. The question was whether other lines of research in AI would surpass it—or perhaps one day endow a machine with the human smarts or expertise that it lacked.
Dividing the pragmatists like Ferrucci and the idealists within AI was the human brain. For many, including Tenenbaum, the path toward true machine intelligence had less to do with the power of the computer than the nature of its instructions and architecture. Only the brain, they believed, held the keys to higher levels of thinking—to concepts, ideas, and theories. But they were tangled up in the most complex circuitry known in the universe.
Tenenbaum compared the effort required to build theorizing and idea-spouting machines with the American push, a half century earlier, to send a manned voyage to the moon. The moon shot, he said, was far easier. When President Kennedy issued his call for a lunar mission in May 1961, most of the basic scientific research had already been accomplished. Indeed, the march toward space travel had begun early in the seventeenth century, when Galileo started to write down the mathematical equations describing how certain objects moved. This advanced through the Scientific and Industrial Revolutions, from the physics of Newton to the harnessing of electricity, the development of chemical bonds and powerful fuels, the creation of metal alloys, and, finally, advances in rocket technology. By the 1960s, the basic science behind sending a spaceship to the moon was largely complete. Much of the technology existed. It was up to the engineers to assemble the pieces, build them to the proper scale, and send the finished spacecraft skyward.
“If you want to compare [AI] to the space program, we’re at Galileo,” Tenenbaum said. “We’re not yet at Newton.” He is convinced that while ongoing research into the brain is shining a light on intelligence, the larger goal—to reverse engineer human thought—will require immense effort and time. An enormous amount of science awaits before the engineering phase can begin. “The problem is exponentially harder [than manned space flight],” he said. “I wouldn’t be surprised if it took a couple hundred years.”
No wonder, you might say, that IBM opted for a more rapid approach. Yet even as Tenenbaum and others engage in quasi-theological debates about the future of intelligence, many are already snatching ideas from the brain to build what they can in the here-and-now. Tenenbaum’s own lab, using statistical formulas inspired by brain functions, is training computers to sort through scarce data and make predictions about everything from the location of oil deposits to suicide bombing attacks. For this, he hopes to infuse the machines with a thread or two of logic inspired by observations of the brain, helping them to connect dots the way people do. At the same time, legions of theorists, focused on the exponential advances in computer