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A New Theory of Neocortex and Its Implications for Machine Intelligence TTI/Vanguard, All that Data February 9, 2005 Jeff Hawkins Director The Redwood Neuroscience Institute Intelligence Paradigms Artificial Intelligence (AI) - ignores biology - computer programs - emulate human behavior 1940s - 1980s Neural Networks - mostly ignores biology - networks of “neurons” - classify spatial patterns 1970s - 1990s Intelligence Paradigms Artificial Intelligence (AI) - ignores biology - computer programs - emulate human behavior 1940s - 1980s Neural Networks - mostly ignores biology - networks of “neurons” - classify spatial patterns 1970s - 1990s “Real Intelligence” 2005 – - biologically derived - hierarchical temporal memory - pattern prediction Hierarchical Temporal Memories (HTMs) A Fundamental technology Automatically discover causes in complex systems Predict future behavior of complex systems Can build super-human intelligence (not C3PO) - faster - more memory - novel senses Agenda Introduction to neocortex What does the neocortex do? How does it do it? Can we express this mathematically? How do we build it? What problems can be solved? Agenda Introduction to neocortex What does the neocortex do? How does it do it? Can we express this mathematically? How do we build it? What problems can be solved? Agenda Introduction to neocortex What does the neocortex do? How does it do it? Can we express this mathematically? How do we build it? What problems can be solved? 1) The neocortex is a memory system. 2) Through exposure, it builds a model the world. 3) The neocortical memory model predicts future events by analogy to past events. Reptilian brain Reptilian brain Behavior Sophisticated senses Mammalian brain Neocortex Reptilian brain Behavior Sophisticated senses Human brain Neocortex Reptilian brain Complex behavior Sophisticated senses Agenda Introduction to neocortex What does the neocortex do? How does it do it? Can we express this mathematically? How do we build it? What problems can be solved? Hierarchical connectivity touch motor audition vision spatially invariant slow changing “objects” spatially specific fast changing “features” “details” Prediction touch motor audition vision Prediction across senses touch motor audition vision Sensory/motor integration touch motor audition vision touch motor audition vision touch motor audition vision What does each region do? ? touch motor audition vision What does each region do? Every region: 1) Stores sequences 2) Passes sequence “name” up 3) Predicts next element 4) Converts invariant prediction into specific prediction 5) Passes specific prediction “down” touch motor audition vision Hierarchical cortex captures hierarchical structure of world - sequences of sequences - structure within structure Unanticipated events rise up the hierarchy until some region can interpret it. Hippocampus is at the top. Novel inputs that cannot be explained as part of known structure automatically rise to the top. HC Unanticipated events rise up the hierarchy until some region can interpret it. Hierarchical Temporal Memories Can Explain Many Psychological Phenomena - Creativity, Intuition, Prejudice - Thought - Consciousness - Learning How does a region work - biology Every region: 1) Stores sequences 2) Passes sequence “name” up 3) Predicts next element 4) Converts invariant prediction into specific prediction 5) Passes specific prediction “down” Agenda Introduction to neocortex What does the neocortex do? How does it do it? Can we express this mathematically? How do we build it? What problems can be solved? All inputs and outputs from a memory region are probability distributions Higher regions Lower regions Learning Higher regions C P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) X Lower regions C = causes or context S = sequences X = input Recognition without context Higher regions P(C) P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) X Lower regions Recognition with context can lead to new interpretation Higher regions C1 C1 P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) X Lower regions Passing a belief down the hierarchy Higher regions C C P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) Xt f ( Xt, P(S|C) ) Lower regions Predicting the future Higher regions C C P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) Xt f ( Xt+1, P(S|C) ) Lower regions Belief Propagation can determine most likely causes of input in a hierarchy of conditional probabilities P(X) P(Y1|X) P(Z1|Y1) P(Z2|Y1) P(Y2|X) P(Z3|Y1) P(Z4|Y1) System Architecture Level 3 Level 2 Level 1 4 pixels Recognition : Examples Correctly Recognized “Incorrectly” recognized Correctly Recognized Test Cases Prediction/Filling-in : Example1 Prediction/Filling-in : Example2 What’s new? Hierarchical Neocognitron HMax Seemore, Visnet Sequence memory auto-associative memories synfire chains Prediction/feedback HMMs ART Sensory/motor integration Biologically derived/constrained/testable Agenda Introduction to neocortex What does the neocortex do? How does it do it? Can we express this mathematically? How do we build it? What problems can be solved? Hierarchical Temporal Memories (HTMs) A Fundamental technology Automatically discover causes in complex systems Predict future behavior of complex systems Can build super-human intelligence (not C3PO) - faster - more memory - novel senses What problems can be solved with HTMs? Traditional AI applications - Vision - Language - Robotics Novel modeling applications - markets - weather - demographics - protein folding - gene interaction - mathematics - physics www.OnIntelligence.org www.stanford.edu/~dil/invariance/ Thank --- Learning sequences L5/matrix thalamus/L1 auto-associative loop Creating a sequence “name” Turning an invariant prediction into a specific prediction
