03 March 2007

Jeff Hawkins Wired

Jeff Hawkins made his fortune with Palm Computing, and Handspring. But he wanted more. He wanted to learn how the human mind works. But to prove that he knows how the human mind works, he has to demonstrate a working model. That is his intent with his Redwood Neuroscience Institute, and Numenta.
Hawkins believes that his program, combined with the ever-faster computational power of digital processors, will also be able to solve massively complex problems by treating them just as an infant’s brain treats the world: as a stream of new sensory data to interpret. Feed information from an electrical power network into Numenta’s system and it builds its own virtual model of how that network operates. And just as a child learns that a glass dropped on concrete will break, the system learns to predict how that network will fail. In a few years, Hawkins boasts, such systems could capture the subtleties of everything from the stock market to the weather in a way that computers now can’t.

...As a teenager, Hawkins became intrigued by the mysteries of human intelligence. But as he recounts in On Intelligence, cowritten with New York Times reporter Sandra Blakeslee, for 25 years he pursued his dream to develop a theory of how the brain works and to create a machine that can mimic it as an amateur. Rejected from graduate school at MIT, where he had hoped to enter the AI lab, he enrolled in the biophysics PhD program at UC Berkeley in the mid-1980s, only to drop out after the school refused to let him forgo lab work to pursue his own theories.

Instead, Hawkins found success in business at Intel, at Grid Computing, and eventually at Palm and then Handspring. But all along, Hawkins says, his ultimate goal was to generate the resources to pursue his neuro-science research. Even while raising the first investments for Palm, he says, “I had to tell people, ‘I really want to work on brains.’” In 2002, he finally was able to focus on brain work. He founded the Redwood Neuroscience Institute, a small think tank that’s now part of UC Berkeley, and settled in to write his book.

It was while he was in his PhD program that Hawkins stumbled upon the central premise of On Intelligence: that prediction is the fundamental component of intelligence. In a flash of insight he had while wondering how he would react if a blue coffee cup suddenly appeared on his desk, he realized that the brain is not only constantly absorbing and storing information about its surroundings — the objects in a room, its sounds, brightness, temperature — but also making predictions about what it will encounter next.

Already, Numenta is blazing the trail for more rapid image recognition and electrical system modeling. It is likely that Hawkins' approach to the silicon brain, and other similar approaches, will yield many more breakthroughs in modeling petroleum deposits, biological systems--even the climate.

Computer scientists have tried to achieve a type of machine mind through randomly wired neural networks:

In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6.
"What are you doing?" asked Minsky.
"I am training a randomly wired neural net to play Tic-tac-toe," Sussman replied.
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play," Sussman said.
Minsky then shut his eyes.
"Why do you close your eyes?" Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened.

Like Minsky, Hawkins was bright enough to understand that the only working mind we know of--the human mind--is generated by a brain that is far from randomly wired. So he set about creating a machine that would mimic the way the mind works.

This is not artificial intelligence. But it is one small step in that direction. The human mind is far more modular than machine designers currently understand. Neuroscientists will need to work with machine designers to iteratively improve their machine mind-models.

Computer modeling has a long way to go before it is trustworthy across large swathes of the real world. Unlike climate modeling, which is an outgrowth of academic and political imperatives, realistic modeling of complex systems will have to show credible results. When the breakthroughs begin occurring, they will likely follow one another in rapid succession.

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