Friday, November 27, 2009

Just posted a link to a September 2009 speech regarding HTM

This is the most recent speech I can find by a Numenta employee regarding the HTM technology. In it, Subutai Ahmad goes through the basics of Hawkins' theory, and outlines how it is currently being implemented in software. A couple of interesting tidbits from it: Ahmad mentioned that Numenta is currently creating its third generation of algorithms. He also mentioned that Numenta plans in 2010 to release a Prediction toolkit (on the heels of its recent release of the Vision toolkit). The vision toolkit allows individuals without any programming skill to create a working computer vision system that can recognize objects in grayscale images. The second generation of algorithms are what allowed the technology to advance to allow such an application. I anticipate that the release of the Prediction toolkit will accelerate the race toward many commercial applications of the HTM technology.

As anyone who read Hawkins' book knows, he argues in it that prediction is the essence of higher intelligence. The ability to predict the future by way of the human neocortex is what separates us from animals such as dogs and cats. The heart of the theory is that columns of neurons in our brains are continuously looking for patterns in the sensory data presented to them. Patterns that follow each other in time (sequences of patterns) are stored for later use. Predictive abilities rise in the brain due to its ability to recognize that it is in a sequence that it has seen before (i.e. when you are walking down the hall of your home, your brain predicts that you will shortly enter your kitchen). The second generation of HTM algorithms only implemented predictive capabilities in a very, very limited way. Presumably, the third generation of algorithms will allow for broad predictive capabilities in Numenta's software. Ahmad demonstrated one interesting prediction application using HTM. Working with a well known financial publication, Numenta stored thousands of sequences in its HTM hierarchy showing the click patterns for users of the publication's website. The idea was to see if the HTM software could predict what topic a user would click on next, given the history of which articles the user had previously clicked on. While random chance would provide less than 1% accuracy (there were nearly 200 topics to choose from), the software predicted with 45% accuracy which topics users would click on next. This is but one example of how HTM will allow for tremendously useful web traffic analysis.

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