The Age of Robots
We're close to making humanlike machines. It's time to reckon with the promises and perils
The trick to making an athletic robot is simulating the finely tuned orchestra of muscle, bone, and nerve that has evolved over countless millennia. All robots make use of the same basic components to do this. A jointed metal or plastic frame serves for a skeleton, and a variety of actuators (motors, pulleys, gears, hydraulics, and so forth) provide muscle power. But the new humanoids are not just bodies; they're also sophisticated sensing machines, packed with cameras, microphones, even "haptic" sensors that mimic the sense of touch. Significant engineering challenges still remain--one of the most fundamental is finding a way to power the energy-hungry machines--but most researchers are confident that they'll get the physical side of things worked out in the near future.
And then there are the brains. At MIT's humanoid robotics lab, the cartoonish, head-only robot Kismet is just slightly larger than a normal human noggin. And yet the contraption relies on a bank of 15 external computers to control its social abilities and impressive array of facial expressions. Asimo and P3 are downright doltish by comparison, depending on remote-control operators and pre-scripted programs to tell them what to do. Other advanced humanoid bodies leave all the thinking to humans. NASA's prototype space worker Robonaut, for example, mimics the movements of a human operator in a sensor-laden "tele-presence" suit. The operator bends his elbow, and the robot bends its elbow in response, like a mechanical mirror image.
Despite decades of intensive research in artificial intelligence, the brains are lagging behind the knees and wrists. During the field's early days, says Rodney Brooks, director of MIT's AI lab and its humanoid robotics group, researchers tried to make machines smart by writing elaborate, computerized problem-solving programs. They assumed the sequences of facts, physical laws, and logical relationships would add up to thinking. The results could be impressive--just ask chess grandmaster Garry Kasparov, who was humbled by IBM's Deep Blue in 1997--but making a thinking machine turned out to be much harder than the scientists imagined. Chess, for all its challenges to human brain power, turns out to be a simpler pursuit than, say, making soup. A chess-bot needs only information and logic, but a chef-bot without a dash of creativity, intuition, and flair would be little more than an expensive, programmable Cuisinart. Take that pot of soup. You cut some vegetables, boil some bones, throw in a bay leaf or two, maybe some other spices. But what vegetables and how many? How to tell a turnip from a turkey leg? And, ahem, whose bones? And how on Earth could a robot add salt to taste with no sense of what "saltiness" means--and, for that matter, no sense of taste?
The high marks of Enlightenment thinking--logic and problem solving--turn out to be much easier to simulate than the perceptual and intuitive things that any kid can do, Brooks says. Stuffing a computer full of facts (chicken bones good, dog bones bad) and equations (salt tolerances between 1 and 3 teaspoons per quart, say) works well for number-crunching tasks. But for real-world smarts--remembering to grab an umbrella if it looks like rain--logic alone just doesn't cut it.
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