Our digital future isn’t all Facebook and iPhone apps. Meet the engineer behind Google X.

Hewlett-Packard is laying off 27,000 people. Yahoo is treading water. Facebook IPO shares got flipped and then flopped. Has Silicon Valley reached the end of the line? Will everyone just develop me-too iPhone apps?

I knew just the guy to prove otherwise.

The entrance to his building is littered with the gaudy red, blue, yellow and green bicycles that Googlers tool around on. I’m at the secret headquarters of the not-so-secret Google X, where the way-out-there projects of the search giant turn into reality. The gregarious play master, Sebastian Thrun, leads us into a well-worn conference room. The chairs are a shade of green not found in nature and the disrupting clang and cheers from a rousing foosball game waft in through the door. Mr. Thrun, 45 and slight in stature, is sporting a gray T-shirt of a local start-up and speaks softly with German-English diction.

“I feel I jump from an ocean liner and then learn how to swim,” he starts. Oh, this is going to be interesting.

Mr. Thrun earned a Ph.D. in computer science from the University of Bonn, “the 53rd of 53 German computer-science schools,” he adds. His focus was on artificial intelligence, a field that failed in the 1980s with a rules-based approach—because humans could never come up with all the rules a machine needed—but then flourished in the mid-90s when machines had to learn the rules by themselves, by trial and error, almost like an infant.


Ken Fallin

Mr. Thrun left Germany in the mid-90s for Carnegie Mellon—looking “for the lack of authority, unlike Germany”—to build intelligent machines. His mentor at CMU, Tom Mitchell, told him, “Pick a problem that matters to society.” So he helped create robots, including a “nursebot” to assist the elderly in nursing homes and robotic tour guides, where one named Minerva led thousands of visitors during a stint at the Smithsonian National Museum of Natural History. This required a cross-discipline education including nursing, psychology, material science and whatever else was required to help machines learn about the real world. These were hard projects, he says. “Just let go, trust your ability to learn, more