Magazine article Newsweek

A Teachable Moment for Your Computer

Magazine article Newsweek

A Teachable Moment for Your Computer

Article excerpt

Byline: Ben Wolford

Since July, a computer in Pittsburgh has been doing nothing but looking at millions of pictures, 24/7. Each minute, it flips through another thousand images of mundane, everyday things like cars and airplanes, turtles and geese. And little by little, this machine is learning about what it sees.

The Never Ending Image Learner (NEIL) at Carnegie Mellon University is part of a rapidly expanding new model of computing in which relying on human input is so 1990s. Computers of this new era, scientists say, can think and recognize the world for themselves. In about four months of running through images, NEIL has already identified 1,500 types of objects and gleaned 2,500 concepts related to the things it sees. With no input from programmers, NEIL can tell you that trading floors are crowded, the Airbus A330 is an airplane, and babies have eyes.

"The more it goes on, the more it will learn and come close to a child [in intelligence]," Abhinav Gupta, an assistant research professor in Carnegie Mellon's Robotics Institute, tells Newsweek. Gupta and his two doctoral student partners call the machine "never ending" for a reason; they're curious to see how long it can keep building on the information it learns, which they compare to the "common sense" knowledge humans acquire all the time.

Gupta's work is part of a field known to robo-researchers as computer vision. It began in the 1970s, when military scientists became interested in whether computers could recognize airplanes or tanks on the battlefield. The first attempts, says Pietro Perona of the California Institute of Technology, focused on edges. Because a digital image is just numbers, it would be a big deal if a machine could find "the distinguishing patterns of lines," Perona tells Newsweek.

By 1995, machines were laboring to process a single image, says Lisa Brown, a computer vision researcher at IBM, who earned her Ph.D. at that time. "Is the road at an angle? We were just trying to answer that," Brown recalls.

As the technology became more advanced, computers could be programmed to understand categories of objects - lamps or coffee mugs, for example - based on shapes and textures. Humans typically recognize 30,000 categories of objects out of a growing number of perhaps more than 1 million, Perona says. …

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