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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.
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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) 1940s - 1980s- ignores biology- computer programs- emulate human behavior • Neural Networks1970s - 1990s- mostly ignores biology- networks of “neurons”- classify spatial patterns
Intelligence Paradigms • Artificial Intelligence (AI) 1940s - 1980s- ignores biology- computer programs- emulate human behavior • Neural Networks1970s - 1990s- mostly ignores biology- networks of “neurons”- classify spatial patterns • “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 eventsby 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?
spatially invariant slow changing “objects” spatially specific fast changing “features” “details” touchmotor audition vision
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 predictioninto 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 predictioninto 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 C = causes or context S = sequences X = input P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) X Lower regions
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
P(X) P(Y1|X) P(Y2|X) P(Z1|Y1) P(Z2|Y1) P(Z3|Y1) P(Z4|Y1) Belief Propagation can determine most likely causes of inputin a hierarchy of conditional probabilities
System Architecture Level 3 Level 2 Level 1 4 pixels
Recognition : Examples Correctly Recognized “Incorrectly” recognized
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/
Learning sequencesL5/matrix thalamus/L1 auto-associative loop