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Machine Understanding

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This section is about how the human (and mammal and possibly other) brain works and how its intelligence might be brought to computing machines. At first this page will only have links to my blog on the same subject. At blogspot you can leave feedback, sign up for an RSS feed, etc. At OpenIcon you will only be able to read the blog entries.

Machine Understanding at Google Blog Spot

Mathematics Supplement

Machine Understanding Blog at Openicon:

Aping as the Basis of Intelligence (June 5, 2012)

Predictive Memory and Particle Filters (January 22, 2012)
Stanford AI Class wrap up (December 26, 2011)
Stanford AI Class thoughts, and a brief tour of AI history (October 24, 2011)
Neural Modeling Computational Trajectory (September 22, 2011)
Counting Nodes: A Framework for Updating Expectations (June 16, 2010)
Understanding Probability and Probabilistic Reasoning (June 10, 2010)
Invariance Light (April 9, 2010)
Songbirds, Genes, and Neurons (April 5, 2010)
Bitworm Part 3: Spatial and Temporal Pool Overview (April 4, 2010)
Understanding the Bitworm Example Program, Part 2: Network Creation Overview (March 31, 2010)
Understanding the Bitworm NuPIC HTM Example Program , Part 1 (March 29, 2010)
Downloading and Installing NuPIC on a Windows computer (March 22, 2010)
Evaluating HTMs, Part 8: CPT details; specific memories (March 18, 2010)
Evaluating HTMs, Part 7: Belief Propagation (March 15, 2010)
Why Time Is Necessary for Learning (March 9, 2010)
Internal Operations of Nodes (March 8, 2010)
Why is a hierarchy important? (March 6, 2010)
How do HTMs Learn? (March 4, 2010)
What do HTMs Do? (March 3, 2010)
Evaluating HTMs, Part 1 (March 2, 2010)
Top Level Review of Towards a Mathematical Theory of Cortical Micro-circuits (November 14, 2009)
On Bayesian Belief Propagation (October 23, 2009)
Bottoms Up for Machine Understanding (October 17, 2009)
Numenta (July 8, 2009)
Theories of the Brain (June 23, 2009)
On Passions and Machine Understanding (June 18, 2009)