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Real-Time Streaming Data Analysis with HTM

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Presentation given by Yuwei Cui, Research Engineer at Numenta. San Francisco Artificial Intelligence Meetup. April 7, 2016.

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Real-Time Streaming Data Analysis with HTM

  1. 1. San Francisco Artificial Intelligence Meetup April, 2016 Yuwei Cui ycui@numenta.com Real-time streaming data analysis with HTM
  2. 2. History of Numenta 2005 – 2009  First generation algorithms  Hierarchy and vision problems 2002 2004 2009 – 2012  Cortical Learning Algorithms  SDRs, sequence memory, continuous learning 2013 – 2015  NuPIC open source project  Grok for anomaly detection 2005 2014 – ??  Sensorimotor  Goal directed behavior
  3. 3. Outline • Numenta’s approach to machine intelligence • A theory of sequence memory in the neocortex • Learning high-order complex sequences online • Application to real-world sequence learning with streaming data • Numenta anomaly benchmark (NAB) • A wide variety of applications with HTM
  4. 4. Numenta Research HTM theory HTM algorithms NuPIC Open source community Technology Validation and Development Streaming Analytics Natural Language Sensorimotor Inference Numenta’s Approach *HTM = Hierarchical Temporal Memory Neuroscience Experimental Research
  5. 5. 1) Discover the computational principles of the neocortex - information and biological theory - making good progress 2) Create Technology for Machine Intelligence based on neocortical principles - not whole-brain simulation, not human-like - new senses, new embodiments, faster , larger Numenta’s Goals Mission: Be the leader in the coming era of machine intelligence
  6. 6. What Does the Neocortex Do? Sensory stream retina cochlea somatic The neocortex learns a model of the world from fast changing sensory data Sensory arrays Motor stream The model is time-based and predictive. light sound touch The neocortex learns a sensory-motor model of the world
  7. 7. Cortical Architecture Hierarchy Cellular layers Mini-columns Neurons: 5-10K synapses 10% proximal 90% distal Active dendrites Learning = new synapses Remarkably uniform - anatomically - functionally 2.5 mm 2/3 4 6 5 Sheet of ~20 billion cells
  8. 8. Cortical Theory Hierarchy Cellular layers Mini-columns Neurons: 5-10K synapses 10% proximal 90% distal Active dendrites Learning = new synapses Remarkably uniform - anatomically - functionally 2.5 mm Sheet of ~20 billion cells 2/3 4 6 5 HTM Hierarchical Temporal Memory Hierarchy of identical regions Each regions learns sequences
  9. 9. Outline • Numenta’s approach to machine intelligence • A theory of sequence memory in the neocortex • Learning high-order complex sequences online • Application to real-world sequence learning with streaming data • Numenta anomaly benchmark (NAB) • A wide variety of applications with HTM
  10. 10. The Neuron Σ ANN neuron Few synapses Sum input x weights Learn by modifying weights of synapses HTM neuron Thousands of synapses Active dendrites: Cell recognizes 100’s of unique patterns Learn by modeling growth of new synapses Biological neuron Thousands of synapses Active dendrites: Cell recognizes 100’s of unique patterns Learn by growing new synapses Feedback Local Feedforward Linear Generate spikes Non-linear 8-20 coactive synapses lead to dendritic NMDA spikes Weakly depolarize soma Hawkins & Ahmad, Front. Neural Circuits, 2016
  11. 11. Feedforward Input Sparse activation of columns (intercolumn inhibition) No prediction All cells in column become active With prediction Only predicted cells in column become active (due to intracolumn inhibition) Arranging Neurons In Minicolumns Leads To Powerful Sequence Memory & Prediction Algorithm t-1 t Two separate sparse representations No prediction A subset of cells are depolarized via predictive contextual input With prediction Feedforward Input Hawkins & Ahmad, Front. Neural Circuits, 2016
  12. 12. High Order Sequences Two sequences: A-B-C-D X-B-C-Y X A B B C C D Y Before learning X B’’ C’’ D’ Y’’ After learning A B’ C’ Same columns, but only one cell active per column after learning. Active cells Depolarized (predictive) cells Inactive cells Time X A B B C C D Y Before learning X B’’ C’’ D’ Y’’ After learning A B’ C’ Same columns, but only one cell active per column after learning. Active cells Depolarized (predictive) cells Inactive cells Time Hawkins & Ahmad, Front. Neural Circuits, 2016 Columns with depolarized cells represent predictions
  13. 13. X A B B C C D Y Before learning X B’’ C’’ D’ Y’’ After learning A B’ C’ Same columns, but only one cell active per column after learning. Active cells Depolarized (predictive) cells Inactive cells Time B input C input D’ AND Y” predicted Start in the middle of learned sequences without context C’ AND C” predicted Multiple simultaneous predictions Two sequences: A-B-C-D X-B-C-Y Hawkins & Ahmad, Front. Neural Circuits, 2016 Multiple predictions are carried forward until sufficient evidence disambiguates them
  14. 14. 1) On-line learning 2) High-order representations For example: sequences “ABCD” vs. “XBCY” 3) Multiple simultaneous predictions For example: “BC” predicts both “D” and “Y” 4) Fully local and unsupervised learning rules 5) Extremely robust Tolerant to >40% noise and faults 6) High capacity HTM Sequence Memory : Computational Properties Extensively tested, deployed in commercial applications Full source code and documentation available: numenta.org & github.com/numenta Papers available: (Hawkins & Ahmad, Front. Neural Circuits, 2016; Cui et al., 2015, 2016)
  15. 15. Outline • Numenta’s approach to machine intelligence • A theory of sequence memory in the neocortex • Learning high-order complex sequences online • Application to real-world sequence learning with streaming data • Numenta anomaly benchmark (NAB) • A wide variety of applications with HTM
  16. 16. Learning high-order sequences online Test prediction accuracy at the end of the sequence Cui et al, arXiv 2015 Shared subsequence Start End High-order sequences Sequence Noise Sequence Noise Continuous learning/testing from streaming data Sequence Noise …Sequence Noise Switch to a new set of sequences
  17. 17. Learning high-order sequences online 00 Online extreme learning machine LSTM with short buffer LSTM with long buffer HTM
  18. 18. Learning high-order sequences online Switch to a new set of sequences
  19. 19. Ability to Make Multiple Predictions Cui et al, arXiv 2015 0 2000 4000 6000 8000 10000 12000 Num ber of elem ents seen 0.0 0.2 0.4 0.6 0.8 1.0 PredictionAccuracy HTM: 2 predictions LSTM: 2 predictions HTM: 4 predictions LSTM: 4 predictions Multiple predictions are made in the form of sparse distributed representations (SDRs), which also have very large coding capacity
  20. 20. Fault Tolerance Kill a fraction of cells
  21. 21. Outline • Numenta’s approach to machine intelligence • A theory of sequence memory in the neocortex • Learning high-order complex sequences online • Application to real-world sequence learning with streaming data • Numenta anomaly benchmark (NAB) • A wide variety of applications with HTM
  22. 22. Application to real-time streaming data analytics Cui et al, arXiv 2015 HTM High Order Sequence Memory Encoder SDR Data Predictions Classification Classifier SDR 2015-04-20 Monday 2015-04-21 Tuesday 2015-04-22 Wednesday 2015-04-23 Thursday 2015-04-24 Friday 2015-04-25 Saturday 2015-04-26 Sunday 0 k 5 k 10 k 15 k 20 k 25 k 30 k PassengerCountin30minwindow A B C 0.8 1.0 0.30 0.35 2.0 2.5 od D NYC Taxi demand Source: http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
  23. 23. Performance On Real-World Streaming Data Sources ARIMA (statistical method) Recurrent Neural network (ESN, LSTM) HTM Extreme Learning Machine (feedforward NN)
  24. 24. Fast adaptation to changes in the data streams Cui et al, arXiv 2015 New pattern introduced 20% increase of night taxi demand 20% decrease of morning taxi demand
  25. 25. Outline • Numenta’s approach to machine intelligence • A theory of sequence memory in the neocortex • Learning high-order complex sequences online • Application to real-world sequence learning with streaming data • Numenta anomaly benchmark (NAB) • A wide variety of applications with HTM
  26. 26. Benchmarking Real-time Streaming Anomaly Detection Traditional benchmarks don’t apply: – Don’t incorporate time, e.g. favor early detection over later detections – Usually batch format – Very few with real world data Numenta Anomaly Benchmark (NAB) – Scoring methodology favors early detection – Incorporates continuous learning (learning a new normal baseline) – Labeled real world data streams – Different “application profiles” – Fully open source Lavin & Ahmad, IEEE ICMLA 2015 The NAB competition (Part of the IEEE WCCI): Win up to $5,000 if you can contribute more datasets and/or anomaly detection algorithms http://numenta.org/nab/
  27. 27. Real-time anomaly detection Lavin & Ahmad, IEEE ICMLA 2015 HTM detects anomaly earlier Other algorithms https://github.com/numenta/NAB
  28. 28. Outline • Numenta’s approach to machine intelligence • A theory of sequence memory in the neocortex • Learning high-order complex sequences online • Application to real-world sequence learning with streaming data • Numenta anomaly benchmark (NAB) • A wide variety of applications with HTM
  29. 29. Datacenter server anomalies Rogue human behavior Geospatial tracking Stock anomalies Applications Using HTM High-Order Inference Social media streams (Twitter) HTM High Order Sequence Memory Encoder SDRData Predictions Classification Anomalies All using the core HTM algorithm, with same parameters
  30. 30. Anomaly Detection in Geospatial Tracking Data HTM Encoder SDRs Prediction Anomaly Detection Classification GPS+ Velocity Trick: convert GPS coordinates into an SDR After input is encoded as an SDR, learning algorithm is agnostic
  31. 31. HTM Studio: an easy way to run HTM with your data Now looking for beta testers for HTM studio!
  32. 32. Summary - Experimental findings from Neuroscience can lead to improved learning algorithms - Used properties of active dendrites, Hebbian-style plasticity and minicolumns - Creating biologically inspired algorithms that really work leads to deeper understanding of cortical principles and numerous testable predictions Research Roadmap - Understand functional properties of laminar microcircuit and thalamocortical inputs - Model multiple regions and hierarchy - More biophysically accurate neuron models (e.g. spiking models)
  33. 33. Collaborators - Jeff Hawkins (PI) - Subutai Ahmad - Scott Purdy - Alex Lavin Contact info: ycui@numenta.com

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