It seems like a reasonable and not uncommon idea that an AI could be built as a mostly-hierarchical autoassiciative memory. As you point out, it's not so different from Hawkins's ideas. Neighboring "pixels" will correlate in space and time; "features" such as edges should become principle components given enough data, and so on. There is a bunch of such work on self-organizing the early visual system like this. That overall concept doesn't get you very far though; the trick is to make it work past the first few rather obvious feature extraction stages of sensory data, and to account for things like episodic memory, language use, goal-directed behavior, and all other cognitive activity that is not just statistical categorization. I sympathize with your approach and wish you luck. If you think you have something that produce more than Hawkins has with his HTM, please explain it with enough precision that we can understand the details.
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