ILLUSTRATION:Bryan Christie Design

By the age of five, a child can understand spoken language, distinguish a cat from a dog, and play a game of catch. These are three of the many things humans find easy that computers and robots currently cannot do. Despite decades of research, we computer scientists have not figured out how to do basic tasks of perception and robotics with a computer.

Our few successes at building "intelligent" machines are notable equally for what they can and cannot do. Computers, at long last, can play winning chess. But the program that can beat the world champion can't talk about chess, let alone learn backgammon. Today's programs-at best-solve specific problems. Where humans have broad and flexible capabilities, computers do not.

Perhaps we've been going about it in the wrong way. For 50 years, computer scientists have been trying to make computers intelligent while mostly ignoring the one thing that is intelligent: the human brain. Even so-called neural network programming techniques take as their starting point a highly simplistic view of how the brain operates.

In some ways, the task has been wrongly posed right from the start. In 1950, Alan Turing, the computer pioneer behind the British code-breaking effort in World War II, proposed to reframe the problem of defining artificial intelligence as a challenge that has since been dubbed the Turing Test. Put simply, it asked whether a computer, hidden from view, could conduct a conversation in such a way that it would be indistinguishable from a human.

So far, the answer has been a resounding no. 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. We call our theory, for reasons that I will explain shortly, Hierarchical Temporal Memory, or HTM. We have created a software platform that allows anyone to build HTMs for experimentation and deployment. You don't program an HTM as you would a computer; rather you configure it with software tools, then train it by exposing it to sensory data. HTMs thus learn in much the same way that children do. HTM is a rich theoretical framework that would be impossible to describe fully in a short article such as this, so I will give only a high level overview of the theory and technology. Details of HTM are available at http://www.numenta.com.

First, I will describe the basics of HTM theory, then I will give an introduction to the tools for building products based on it. It is my hope that some readers will be enticed to learn more and to join us in this work.

We have concentrated our research on the neocortex, because it is responsible for almost all high-level thought and perception, a role that explains its exceptionally large size in humans-about 60 percent of brain volume [see illustration "Goldenrod"]. The neocortex is a thin sheet of cells, folded to form the convolutions that have become a visual synonym for the brain itself. Although individual parts of the sheet handle problems as different as vision, hearing, language, music, and motor control, the neocortical sheet itself is remarkably uniform. Most parts look nearly identical at the macroscopic and microscopic level.