SCIENCE

Ask a computer to add 100 and 100, and its answer will be 200. But what if it sometimes answered 202, and sometimes 199, or any other number within about 1 percent of the correct answer?

Arithmetic circuits that returned such imprecise answers would be much smaller than those in today’s computers. They would consume less power, and many more of them could fit on a single chip, greatly increasing the number of calculations it could perform at once. The question is how useful those imprecise calculations would be.

If early results of a research project at MIT are any indication, the answer is, Surprisingly useful. About a year ago, Joseph Bates, an adjunct professor of computer science at Carnegie Mellon University, was giving a presentation at MIT and found himself talking to Deb Roy, a researcher at MIT’s Media Lab. Three years earlier, before the birth of his son, Roy had outfitted his home with 11 video cameras and 14 microphones, intending to flesh out what he calls the “surprisingly incomplete and biased observational data” about human speech acquisition. Data about a child’s interactions with both its caregivers and its environment could help confirm or refute a number of competing theories in developmental psychology. But combing through more than 100,000 hours of video for, say, every instance in which either a child or its caregivers says “ball,” together with all the child’s interactions with actual balls, is a daunting task for human researchers and artificial-intelligence systems alike. Bates had designed a chip that could perform tens of thousands of simultaneous calculations using sloppy arithmetic and was looking for applications that leant themselves to it.

Roy and Bates knew that algorithms for processing visual data are often fairly error-prone: A system that identifies objects in static images, for instance, is considered good if it’s right about half the time. Increasing a video-processing algorithm’s margin of error ever so slightly, the researchers reasoned, probably wouldn’t compromise its performance too badly. And if the payoff was the ability to do thousands of computations in parallel, Roy and his colleagues might be able to perform analyses of video data that they hadn’t dreamt of before.