A New Theory of Neocortex and Its Implications for Machine Intelligence

1. 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

2. 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

3. 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

4. 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

5. 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?

6. 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?

8. 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?

9. 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.

11. Reptilian brain Reptilian brain Behavior Sophisticated senses

12. Mammalian brain Neocortex Reptilian brain Behavior Sophisticated senses

13. Human brain Neocortex Reptilian brain Complex behavior Sophisticated senses

14. 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?

16. Hierarchical connectivity

17. spatially invariant slow changing “objects” spatially specific fast changing “features” “details” touchmotor audition vision

18. Prediction touch motor audition vision

19. Prediction across senses touch motor audition vision

20. Sensory/motor integration touch motor audition vision

21. touch motor audition vision

22. touch motor audition vision

23. What does each region do? ? touch motor audition vision

24. 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

25. Unanticipated events rise up the hierarchy until some region can interpret it.

26. 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.

27. Hierarchical Temporal Memories Can Explain Many Psychological Phenomena • Creativity, Intuition, Prejudice • Thought • Consciousness • Learning

28. 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”

29. 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?

30. All inputs and outputs from a memory region are probability distributions Higher regions Lower regions

31. 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

32. Recognition without context Higher regions P(C) P(S|C) SA(xt,xt+1,...) SB(xt,xt+1,...) X Lower regions

33. 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

34. 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

35. 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

36. 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

38. System Architecture Level 3 Level 2 Level 1 4 pixels

39. Recognition : Examples Correctly Recognized “Incorrectly” recognized

40. Correctly Recognized Test Cases

41. Prediction/Filling-in : Example1

42. Prediction/Filling-in : Example2

43. 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

44. 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?

45. 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

46. 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

47. www.OnIntelligence.org www.stanford.edu/~dil/invariance/

48. Thank ---

50. Learning sequencesL5/matrix thalamus/L1 auto-associative loop