Learn Like A Human

By Jeff Hawkins
Why Can't A Computer Be More Like A Brain?

Turing's behavioral framing of the problem has led researchers away from the most promising avenue of study: the human brain. It is clear to many people that the brain must work in ways that are very different from digital computers. To build intelligent machines, then, why not understand how the brain works, and then ask how we can replicate it?

My colleagues and I have been pursuing that approach for several years. We've focused on the brain's neocortex, and we have made significant progress in understanding how it works.

neocortex brain grid illustration

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Because of the neocortex's uniform structure, neuro-scientists have long suspected that all its parts work on a common algortihm-that is, that the brain hears, sees, understands language, and even plays chess with a single, flexible tool. Much experimental evidence supports the idea that the neocortex is such a general-purpose learning machine. What it learns and what it can do are determined by the size of the neocortical sheet, what senses the sheet is connected to, and what experiences it is trained on. HTM is a theory of the neocortical algorithm. If we are right, it represents a new way of solving computational problems that so far have eluded us.

Although the entire neocortex is fairly uniform, it is divided into dozens of areas that do different things. Some areas, for instance, are responsible for language, others for music, and still others for vision. They are connected by bundles of nerve fibers. If you make a map of the connections, you find that they trace a hierarchical design. The senses feed input directly to some regions, which feed information to other regions, which in turn send information to other regions. Information also flows down the hierarchy, but because the up and down pathways are distinct, the hierarchical arrangement remains clear and is well documented.

As a general rule, neurons at low levels of the hierarchy represent simple structure in the input, and neurons at higher levels represent more complex structure in the input. For example, input from the ears travels through a succession of regions, each representing progressively more complex aspects of sound. By the time the information reaches a language center, we find cells that respond to words and phrases independent of speaker or pitch.

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Good stuff. If you are interested, here is the whole article