Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

October 14, 2011

Hierarchical Temporal Memory

Filed under: CS Lectures,Hierarchical Temporal Memory (HTM),Machine Learning — Patrick Durusau @ 6:23 pm

Hierarchical Temporal Memory: How a Theory of the Neocortex May Lead to Truly Intelligent Machines by Jeff Hawkins.

Don’t skip because of the title!

Hawkins covers his theory of the neocortex but however you feel about that, 2/3 of the presentation is on algorithms, completely new material.

Very cool presentation on “Fixed Sparsity Distributed Representation” and lots of neural science stuff. Need to listen to it again and then read the books/papers.

What I liked about it was the notion that even in very noisy or missing data contexts, that highly reliable identifications can be made.

True enough, Hawkins was talking about vision, etc., but he didn’t bring up any reasons why that could not work in other data environments.

In other words, when can a program treat extra data about a subject as noise and recognize it anyway?

Or if some information is missing about a subject, have a program reliably recognize it.

Or if we only want to store some information and yet have reliable recognition?

Don’t know if any, some or all of those are possible but it is certainly worth finding out.

Description:

Jeff Hawkins (Numenta founder) presents as part of the UBC Department of Computer Science’s Distinguished Lecture Series, March 18, 2010.

Coaxing computers to perform basic acts of perception and robotics, let alone high-level thought, has been difficult. No existing computer can recognize pictures, understand language, or navigate through a cluttered room with anywhere near the facility of a child. Hawkins and his colleagues have developed a model of how the neocortex performs these and other tasks. The theory, called Hierarchical Temporal Memory, explains how the hierarchical structure of the neocortex builds a model of its world and uses this model for inference and prediction. To turn this theory into a useful technology, Hawkins has created a company called Numenta. In this talk Hawkins will describe the theory, its biological basis, and progress in applying Hierarchical Temporal Memory to machine learning problems.

Part of this theory was described in Hawkins’ 2004 book, On Intelligence. Further information can be found at www.Numenta.com

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