I am now going to evaluate the particular model of Hierarchical Temporal Memory (HTM) developed by Jeff Hawkins, Dileep George, and members of their team at Numenta, NuPIC. My key question will be: could HTMs serve as a basis for machine understanding (MU)? Since there are many subgoals on the way to true MU, I will be evaluating HTM capabilities on a number of issues that are usually within the realm of AI (artificial intelligence).
This will be a long project. The current plan is to read and critically summarize "Hierarchical Temporary Memory, Concepts, Theory, and Terminology " by Hawkins and George; read and comment on the "Getting Started with NuPIC Guide;" downloading and trying NuPIC, and then assessing what whether to continue on the project.
In preparation I have previously read On Intelligence by Jeff Hawkins twice; read and commented on Towards a Mathematical Theory of Cortical Micro-circuits by George and Hawkins [see early entries of this blog]; took a course in neural networks at SDSU long ago; and read From Neuron to Brain to get detailed information on biological neurons. I know how to write software and I am fairly good at math.
Still, my head hurts just thinking about it, but here we go (into "HTM Concepts, Theory, and Terminology'):
In the Introduction the authors remind us that the human mind/brain has capabilities that computers have so far been unable to duplicate. HTMs are a memory system that can learn to solve certain problems. They are organized as hierarchical systems of nodes. HTMs are currently implemented as software on traditional computer hardware. "The learning curve can be steep."
That is all ground I have already covered in this blog. My learning curve is probably going to be steeper than that of most students who would be interested in this topic, but hopefully watching me struggle will be helpful to at least a few people.