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.

  Good questions.

  I agree with you on Hawkins & HTM, but his main problem is conceptual.
  He seems to be profoundly confused as to what the hierarchy should select 
for: generality or novelty. He nominates both, apparently not realizing that 
they're mutually exclusive. This creates a difficulty in defining a 
quantitative criterion for selection, which is a key for my approach. This 
internal inconsistency leads to haphazard hacking in the HTM. For example, he 
starts by comparing 2D frames in a binary fashion, which pretty perverse for an 
incremental approach. I start from the begining, by comparing pixels: the limit 
of resolution, & I quantify the degree of match right there, as a distinct 
variable. I also record & compare explicit coordinates & derivatives, while he 
simply junks all that information. His approach doesn't scale because it's not 
consistent & incremental enough.

  I disagree that we need to specifically code episodic memory, language, & 
action, - to me these are "emergent properties" (damn, I hate that word:)).

  Boris.   
  http://scalable-intelligence.blogspot.com/

-------------------------------------------
agi
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