Emerging Tech: Jeff Hawkins reinvents artificial intelligence

Jeff Hawkins has a lot on his mind—not least, a new theory about how the brain works. And he's confident his theory will change the entire computing industry.

In Silicon Valley, Hawkins is best known as the founder of Palm Computing Inc. and Handspring Inc., and as the mastermind behind the Palm Pilot and Treo line of smartphones. But a second passion predates Hawkins' fondness for the wireless world: He's nuts about the brain. Hawkins began his career at Intel, in 1979, and while there he made an unsuccessful bid to convince then-Chairman Gordon Moore to launch a research group on neurology and artificial intelligence. Still, Hawkins didn't let that initial setback diminish his enthusiasm, and, in fact, he's spent much of the past quarter century studying the physiology, philosophy and psychology of the brain, even entering a Ph.D. program in biophysics at the University of California at Berkeley, in 1986.

Read about what Palm's Jeff Hawkins has to say about the future of mobile computing

In 2002, Hawkins founded the Redwood Neuroscience Institute (now known as the Redwood Center for Theoretic Neuroscience at the University of California at Berkeley) as a means to develop a rigorous theory of how the human neocortex works. There, he developed a new theory: that the brain makes predictions about the world through pattern recognition and memory, recalling event sequences and their "nested" relationships. Hawkins calls his theory the "memory-prediction framework," and he believes it is the missing link in creating truly intelligent machines. Hawkins published his ideas in his 2004 book, On Intelligence, which he coauthored with New York Times science writer Sandra Blakeslee. And in March 2005, Hawkins founded Numenta Inc., a privately held company in Menlo Park, Calif., that seeks to build intelligent machines based on the theories set forth in his book.

Senior Reporter Debra D'Agostino and Editor Edward Baker spoke with Hawkins about the plausibility of his memory-prediction framework, how it might be translated into software and applied to complex problems—and what that could mean for business. An edited version of their conversation follows.

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