Last November Hawkins gave a well-received keynote speech at a worldwide meeting of 30,000 neuroscientists. But he is more interested in making things. In 2005, he turned RNI over to the Helen Wills Institute (where it was redubbed the Redwood Center for Theoretical Neuroscience) so he could focus on Numenta, a company that is building software based on this theory of memory-based learning and abstraction among a hierarchy of nodes. One California firm is implementing the software on behalf of a major oil company, to look for unusual patterns of power use and environmental change in an offshore platform.

The Numenta software seeks abstractions and develops generalized rules no matter what it is examining. This is a radical departure from traditional computing, which is most successful when it is designed for specific tasks, with memory stored and fetched as needed. If Numenta works, it will be revolutionary. But Hawkins has skeptics, both in academia and the private sector. Knight says, “the physiology is correct, but it would be a good idea to find things to back up the theory.” Peter Norvig, an author of texts on artificial intelligence who now heads up research at Google, has for now given up on Hawkins-style aspirations of making software structured like the brain, to focus on improved statistical models and massive number crunching.

Hawkins is unfazed. “I wouldn’t want to compare myself,” he says, “but Einstein was confident about relativity before there was proof. Watson knew the double helix was right for DNA.” Besides, the engineer is not really concerned over proving his theories of the brain. “If it turns into a successful technology, that is how we’ll make the most progress finding out what the brain really is doing.”

Such error-based success would join a rich tradition of scientific and technological advancement through incorrect theory, a tradition with a notable pedigree in the study of our own brains. René Descartes drove apart the brain and the mind and posited that mental activity was sorted out by the soul from its position on the pineal gland (which he incorrectly thought was the only unitary element in the divided brain). The early 19th-century physician Franz-Joseph Gall studied crania and correctly deduced that the brain is differentiated into regions. But he was wrong about what they did, and thought they pushed out the skull, giving us another quack science—phrenology.

Indeed, without mistakes we might not have computers. The binary math underlying their operations was reputedly developed in light of a misreading of the I Ching when it was first imported to Europe from China. Based on his own understanding of mental processes, Alan Turing laid the foundations of computer science with an orderly series of steps—the algorithm—that most researchers now doubt bears much relation to the way our brains actually work.

Scientists can’t always find a person undergoing brain surgery who is also willing and able to participate in an experiment. So research continues with the classic transcranial method—electrodes attached to a cap.

It is disputable whether Turing actually intended his “universal machine” to stand for all mental activity, but that hasn’t stopped generations of artificial intelligence fans. Of late this group, computer science professors and sharp Internet millionaires among them, have congratulated themselves by drawing a line that traces the increase in the number of transistors fitting on a computer chip, from a few to today’s millions. They continue this line to a time some four decades hence when a chip will hold transistors equaling the number of neurons in a brain. By implication, we will have a brain.

The idea makes neuroscientists cringe. “These guys don’t know anything about how a brain works,” says Bruno Olshausen, director of the Redwood Center and a fan of Hawkins’s theories. “They don’t account for the levels in the brain, any of the complexity beyond neural signaling.” The oscillations Knight has found, for example, might be a completely different kind of communication: a network event without simple on/off signaling.

Still, today’s technology wunderkinder, in love with their own tools, yearn for a transhuman future. At a recent lunch with a young Internet mogul who made a fortune on PayPal, I dipped sardines in blue-tinted Jurassic salt while he enthused about our highly networked future. We will disappear into the machine, and vice versa, he told me, as networked chips implanted in our brains update our memories daily, providing us with the latest news. “Who knows what will be in our brains?” he asked and then added, “They will be hacked!” Still apparently eager for this future, he left lunch early to work at hastening its arrival.

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