Principles of Hierarchical Temporal Memory - Foundations of Machine Intelligence
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Presentation by Jeff Hawkins at Numenta Workshop on October 17, 2014
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Principles of Hierarchical Temporal Memory - Foundations of Machine Intelligence
- 1. Numenta Workshop October 17, 2014 Jeff Hawkins jhawkins@Numenta.com Principles of Hierarchical Temporal Memory Foundations of Machine Intelligence
- 2. 1) Discover operating principles of neocortex. 2) Create technology for machine intelligence based on neocortical principles. Numenta’s Mission
- 3. Why Will Machine Intelligence be Based on Cortical Principles? 1) Cortex uses a common learning algorithm vision hearing touch behavior 2) Cortical algorithm is incredibly adaptable languages engineering science arts … 3) Network effects hardware and software efforts will focus on most universal solution
- 4. Talk Topics - Cortical facts - Cortical theory (HTM) - Research roadmap - Applications roadmap - Thoughts on Machine Intelligence easy deep easy
- 5. What the Cortex Does patterns The neocortex learns a model from fast changing sensory data The model generates - predictions - anomalies - actions Most of sensory changes are due to your own movement The neocortex learns a sensory-motor model of the world patterns patterns light sound touch retina cochlear somatic
- 6. Cortical Facts Hierarchy Cellular layers Mini-columns Neurons w/1000’s of synapses - 10% proximal - 90% distal Active distal dendrites Synaptogenesis Remarkably uniform - anatomically - functionally 2.5 mm Sheet of cells 2/3 4 6 5
- 7. Cortical Theory Hierarchy Cellular layers Mini-columns Neurons w/1000’s of synapses - 10% proximal - 90% distal Active distal dendrites Synaptogenesis Remarkably uniform - anatomically - functionally Sheet of cellsHTM Hierarchical Temporal Memory 1) Hierarchy of identical regions 2) Each region learns sequences 3) Stability increases going up hierarchy if input is predictable 4) Sequences unfold going down Questions - What does a region do? - What do the cellular layers do? - How do neurons implement this? - How does this work in hierarchy? 2/3 4 6 5
- 8. 2/3 4 5 6 Cellular Layers Sequence memory Sequence memory Sequence memory Sequence memory Inference Inference Motor Attention FeedforwardFeedback Each layer implements a variation of a common sequence memory algorithm.
- 9. 2/3 4 5 6 Copy of motor commands Sensor/afferent data Next higher region Two Types of Inference (L4, L2/3) Learns sensory-motor sequences Learns high-order sequences Stable Predicted Pass through changes Un-predicted A-B-C-D X-B-C-Y A-B-C- ? D X-B-C- ? Y These are universal inference steps. They apply to all sensory modalities. Produces receptive field properties seen in cortex.
- 10. Feedforward Linear summation Binary activation Local Feedback Feedback Local Feedforward Linear Generate spikes The Neuron Biological neuron HTM neuron Non-linear Dendritic APs depolarize soma Threshold coincidence detectors “Predicted” cell state HTM SynapsesBiological Synapses Learning is mostly formation of new synapses. Synapses are low fidelity. Binary weight 0.0 1.00.4 Scalar “permanence” 0 1
- 11. Sparse Distributed Representations (SDRs) • Many bits (thousands) • Few 1’s mostly 0’s • Example: 2,000 bits, 2% active • Each bit has semantic meaning • Learned 01000000000000000001000000000000000000000000000000000010000…………01000 Dense Representations • Few bits (8 to 128) • All combinations of 1’s and 0’s • Example: 8 bit ASCII • Bits have no inherent meaning • Arbitrarily assigned by programmer 01101101 = m Sparse Distributed Representations (SDRs) The Language of Intelligence
- 12. SDR Properties subsampling is OK 3) Union membership: Indices 1 2 | 10 Is this SDR a member? 2) Store and Compare: store indices of active bits Indices 1 2 3 4 5 | 40 1) 2) 3) …. 10) 2% 20%Union E.g. a cell can recognize many unique patterns on a single dendritic branch. Ten synapses from Pattern 1 Ten synapses from Pattern N 1) Similarity: shared bits = semantic similarity
- 13. Cell activates from dozens of feedforward patterns Neurons Recognize Hundreds of Patterns Cell predicts its activity in hundreds of contexts
- 16. Learning Transitions Sparse cell activation
- 17. Time = 1 Learning Transitions
- 18. Time = 2 Learning Transitions
- 19. Learning Transitions Form connections to previously active cells. Predict future activity.
- 20. - This is a first order sequence memory. - It cannot learn A-B-C-D vs. X-B-C-Y. - Mini-columns turn this into a high-order sequence memory. Learning Transitions Multiple predictions can occur at once. A-B A-C A-D
- 21. Forming High Order Representations Feedforward Sparse activation of columns No prediction All cells in column become active With prediction Only predicted cells in column become active
- 22. Representing High-order Sequences A-B-C-D vs. X-B-C-Y A X B B C C Y D Before training A X B’’ B’ C’’ C’ Y’’ D’ After training Same columns, but only one cell active per column. IF 40 active columns, 10 cells per column THEN 1040 ways to represent the same input in different contexts
- 23. HTM Temporal Memory (aka Cellular Layer) Converts input to sparse activation of columns Recognizes, and recalls high-order sequences - Continuous learning - High capacity - Local learning rules - Fault tolerant - No sensitive parameters - Semantic generalization HTM Temporal Memory is a building block of neocortex/machine intelligence motor inference inference attention
- 24. 2/3 4 5 6 Research Roadmap Sensory-motor Inference High-order Inference Motor Sequences Attention/Feedback Theory 98% Extensively tested Commercial Theory 80% In development Theory 50% Theory 10% Data: Streaming Capabilities: Prediction Anomaly detection Classification Applications: Predictive maintenance Security Natural Language Processing
- 25. Applications Using HTM High-order Inference Server anomalies GROK available on AWS Unusual human behavior Geospatial anomalies Natural language search/prediction Cortical.IO Stock volume anomalies HTM High Order Sequence Memory Encoder SDRData Predictions Anomalies All use the same HTM code base
- 26. 2/3 4 5 6 Research Roadmap Sensory-motor Inference High-order Inference Motor Sequences Attention/Feedback Theory 98% Extensively tested Commercial Theory 80% In development Theory 50% Theory 10% Data: Streaming Capabilities: Prediction Anomaly detection Classification Applications: IT Security Natural Language Processing Data: Static (with simple behaviors) Capabilities: Classification Prediction Applications: Vision image classification (with saccades) Network classification
- 27. 2/3 4 5 6 Research Roadmap Sensory-motor Inference High-order Inference Motor Sequences Attention/Feedback Theory 98% Extensively tested Commercial Theory 80% In development Theory 50% Theory 10% Data: Streaming Capabilities: Prediction Anomaly detection Classification Applications: IT Security Natural Language Processing Data: Static With simple behaviors Capabilities: Classification Prediction Applications: Vision image classification (with saccades) Network classification Data: Static and/or streaming Capabilities: Goal-oriented behavior Applications: Robotics Smart bots Proactive defense
- 28. 2/3 4 5 6 Research Roadmap Sensory-motor Inference High-order Inference Motor Sequences Attention/Feedback Theory 98% Extensively tested Commercial Theory 80% In development Theory 50% Theory 10% Data: Streaming Capabilities: Prediction Anomaly detection Classification Applications: IT Security Natural Language Processing Data: Static (with simple behavior) Capabilities: Classification Prediction Applications: Vision image classification (with saccades and hierarchy) Network classification Data: Static and/or streaming Capabilities: Goal-oriented behavior Applications: Robotics Smart bots Proactive defense Enables : Multi-sensory modalities Multi-behavioral modalities
- 29. Algorithms are documented Multiple independent implementations Numenta’s software is open source (GPLv3) NuPIC www.Numenta.org Active discussion groups for theory and implementation Numenta’s research code is posted daily Collaborations IBM Almaden Research, San Jose, CA DARPA, Washington D.C Cortical.IO, Austria Jhawkins@numenta.com @Numenta Research Roadmap: open and transparent
- 30. Machine Intelligence Landscape Cortical (e.g. HTM) ANNs (e.g. Deep learning) A.I. (e.g. Watson)
- 31. Machine Intelligence Landscape Premise Biological Mathematical Engineered Cortical (e.g. HTM) ANNs (e.g. Deep learning) A.I. (e.g. Watson)
- 32. Machine Intelligence Landscape Premise Biological Mathematical Engineered Data Spatial-temporal Behavior Spatial-temporal Language Documents Cortical (e.g. HTM) ANNs (e.g. Deep learning) A.I. (e.g. Watson)
- 33. Machine Intelligence Landscape Premise Biological Mathematical Engineered Data Spatial-temporal Behavior Spatial-temporal Language Documents Capabilities Prediction Classification Goal-oriented Behavior Classification NL Query Cortical (e.g. HTM) ANNs (e.g. Deep learning) A.I. (e.g. Watson)
- 34. Machine Intelligence Landscape Premise Biological Mathematical Engineered Data Spatial-temporal Behavior Spatial-temporal Language Documents Capabilities Prediction Classification Goal-oriented Behavior Classification NL Query Valuable? Yes Yes Yes Cortical (e.g. HTM) ANNs (e.g. Deep learning) A.I. (e.g. Watson)
- 35. Machine Intelligence Landscape Premise Biological Mathematical Engineered Data Spatial-temporal Behavior Spatial-temporal Language Documents Capabilities Prediction Classification Goal-oriented Behavior Classification NL Query Valuable? Yes Yes Yes Path to M.I.? Yes Probably not No Cortical (e.g. HTM) ANNs (e.g. Deep learning) A.I. (e.g. Watson)
- 36. 1940’s 1950’s - Analog vs. digital - Decimal vs. binary - Wired vs. memory-based programming - Serial vs. random access memory Many approaches - Digital - Binary - Memory-based programming - Two tier memory One dominant paradigm The Birth of Programmable Computing Why Did One Paradigm Win? - Network effects Why Did This Paradigm Win? - Most flexible - Most scalable
- 37. 2010’s 2020’s The Birth of Machine Intelligence - Specific vs. universal algorithms - Mathematical vs. memory-based - Spatial vs. time-based patterns - Batch vs. on-line learning Many approaches - Universal algorithms - Memory-based - Time-based patterns - On-line learning One dominant paradigm Why Will One Paradigm Win? - Network effects Why Will This Paradigm Win? - Most flexible - Most scalable How Do We Know This is Going to Happen? - Brain is proof case - We have made great progress
- 38. What Can Be Done With Software 1 layer 30 msec / learning-inference-prediction step 10-6 of human cortex 2048 columns 65,000 neurons 300M synapses
- 39. Challenges Dendritic regions Active dendrites 1,000s of synapses 10,000s of potential synapses Continuous learning Challenges and Opportunities for Neuromorphic HW Opportunities Low precision memory (synapses) Fault tolerant - memory - connectivity - neurons - natural recovery Simple activation states (no spikes) Connectivity - very sparse, topological
- 40. Requirements for Online learning • Train on every new input • If pattern does not repeat, forget it • If pattern repeats, reinforce it Connection strength/weight is binary Connection permanence is a scalar Training changes permanence If permanence > threshold then connected Learning is the formation of connections 10 connectedunconnected Connection permanence 0.2
- 41. 1 2/3 4 5 6 Motor 1 2/3 4 5 6 Sensory Motor Kinesthetic Thalamus Thalamus We believe all layers implement variations of the same learning algorithm: - Learning transitions in afferent data. Stable representations are formed for predicted transitions. Unpredicted transitions are passed to next layer. Layer 4: Learns sensory/motor transitions. Layer 3: Learns high-order sequence transitions. Layers 5 and 6 learn sequences for motor and attention Sequence Memory Cortical Layers
- 42. Document corpus (e.g. Wikipedia) 128 x 128 100K “Word SDRs” - = Apple Fruit Computer Macintosh Microsoft Mac Linux Operating system …. Natural Language +
- 43. Training set frog eats flies cow eats grain elephant eats leaves goat eats grass wolf eats rabbit cat likes ball elephant likes water sheep eats grass cat eats salmon wolf eats mice lion eats cow dog likes sleep elephant likes water cat likes ball coyote eats rodent coyote eats rabbit wolf eats squirrel dog likes sleep cat likes ball ---- ---- ----- Word 3Word 2Word 1 Sequences of Word SDRs HTM
- 44. Training set eats“fox” ? frog eats flies cow eats grain elephant eats leaves goat eats grass wolf eats rabbit cat likes ball elephant likes water sheep eats grass cat eats salmon wolf eats mice lion eats cow dog likes sleep elephant likes water cat likes ball coyote eats rodent coyote eats rabbit wolf eats squirrel dog likes sleep cat likes ball ---- ---- ----- Sequences of Word SDRs HTM
- 45. Training set eats“fox” rodent 1) Unsupervised Learning 2) Semantic Generalization 3) Many Applications frog eats flies cow eats grain elephant eats leaves goat eats grass wolf eats rabbit cat likes ball elephant likes water sheep eats grass cat eats salmon wolf eats mice lion eats cow dog likes sleep elephant likes water cat likes ball coyote eats rodent coyote eats rabbit wolf eats squirrel dog likes sleep cat likes ball ---- ---- ----- Sequences of Word SDRs HTM
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