•HTM(Hierarchical Temporal Memory) : Not programmed & not different algorithms for different problem.
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1) Discover cause
– Find relationships at inputs.
– Possible cause is called “belief”.
2) Infer causes of novel input
– Inference : Similar to pattern recognition
– Ambiguous -> Flat.
– HTMs handle novel input both during inference & training
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3) Make predictions
– Each node store sequences of patterns
+ current input -> Predict what would happen next.
4) Direct behavior : Interact with world.
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How do HTMs discover and infer causes?
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Why is a hierarchy important?
1) Shared representations lead to generalization and storage efficiency.
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2) The hierarchy of HTM matches the spatial and temporal hierarchy of the real world.
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3) Belief propagation ensures all nodes quickly reach the best mutually compatible beliefs.
– Belief propagation calculates the marginal distribution for
each unobserved node, conditional on any observed nodes.
4) Hierarchical representation affords mechanism for attention
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How does each node discover and infer causes?
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Assigning causes.
Most common sequence of pattern are assigned.
Assigned causes are used for prediction, behavior etc.
Why is time necessary to learn?
•Pooling(many-one) method
– Overlap
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Several images of watermelons are overlapped in one picture
– Learning of sequence : HTM uses this way.
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- 4 pictures are stored sequentially
– Reference
Hierarchical Temporal Memory – Concepts, Theory, and Terminology by Jeff Hawkins and Dileep George, Numenta Inc.