ACM CIKM October 31, 2013 Jeff Hawkins jhawkins@GrokSolutions

1. On-line Learning From Streaming Data ACM CIKMOctober 31, 2013 Jeff Hawkins jhawkins@GrokSolutions.com

2. Industrial Research Track 1) Discover operating principles of neocortex Anatomy,Physiology Theoreticalprinciples Software • Build systems based on these principles Cortical algorithms Anomaly detection in high velocity data

3. The neocortex is a memory system. The neocortex learns a model from sensory data - predictions - anomalies - actions retina data stream cochlea somatic The neocortex learns a sensory-motor model of the world

4. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea somatic data stream

5. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea 2) Hierarchy of memory regions somatic data stream

6. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea 2) Hierarchy of memory regions somatic 3) Sequence memory - inference - motor data stream

7. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea 2) Hierarchy of memory regions somatic 3) Sequence memory data stream 4) Sparse Distributed Representations

8. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea 2) Hierarchy of memory regions somatic 3) Sequence memory data stream 4) Sparse Distributed Representations 5) All regions are sensory and motor Motor

9. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea 2) Hierarchy of memory regions somatic x x x 3) Sequence memory data stream x x x x 4) Sparse Distributed Representations x x x x x x 5) All regions are sensory and motor 6) Attention

10. Principles of Neocortical Function retina 1) On-line learning from streaming data cochlea 2) Hierarchy of memory regions somatic 3) Sequence memory data stream 4) Sparse Distributed Representations 5) All regions are sensory and motor 6) Attention These six principles are necessary and sufficientfor biological and machine intelligence. - All mammals from mouse to human have them

11. Dense Representations • Few bits (8 to 128) • All combinations of 1’s and 0’s • Example: 8 bit ASCII • Individual bits have no inherent meaning • Representation is assigned by programmer Sparse Distributed Representations (SDRs) 01101101 = m • Many bits (thousands) • Few 1’s mostly 0’s • Example: 2,000 bits, 2% active • Each bit has semantic meaning • Meaning of each bit is learned, not assigned 01000000000000000001000000000000000000000000000000000010000…………01000

12. 1) Similarity: shared bits = semantic similarity A Few SDR Properties 2) Store and Compare: store indices of active bits Indices12345|40 subsampling is OK Indices12|10

13. Sequence Memory (for inference and motor) Coincidence detectors How does a layer of neurons learn sequences?

14. Each cell is one bit in our Sparse Distributed Representation SDRs are formed via a local competition between cells.

15. SDR (time =1)

16. SDR (time =2)

17. Cell forms connections to subsample of previously active cells.Predicts its own future activity.

18. Multiple Predictions Can Occur at Once With one cell per column, 1st order memoryWe need a high order memory

19. High Order Sequence Memory Enabled by Columns of Cells Cortical Learning Algorithm (CLA)Distributed sequence memoryHigh order High capacity Multiple simultaneous predictions Semantic generalization

20. Three Current Directions • NuPIC Open Source Project

21. NuPIC Open Source Project www.Numenta.org Single source tree (used by GROK) GPLv3 Steady community growth • 67 contributors (+26 since July) • 245 mailing list subscribers • 1621 total messages eBook from community member OS community joining KaggleCompetitions Fall Hackathon: 70 attendees

22. Three Current Directions • NuPIC Open Source Project • Custom CLA Hardware • Needed for scaling research and commercial applications • DARPA “Cortical Processor” • IBM, Seagate, Sandia Labs • Commercialization

23. Data: Past and Future Past 1. Store data 2. Look at data 3. Build models Problem: - Doesn’t scale with velocity and #models Solution: - Automated model creation - Continuous learning - Temporal inference PredictionsAnomaliesActions Stream data Automated model creation Continuous learning Temporal inference Future

24. Anomaly Detection Using Predictive Cortical Models Cortical Memory Prediction Point anomaly score Time average Distribution of averages Metric anomaly score Encoder Metric 1 SDR Cortical Memory Prediction Point anomaly score Time average Distribution of averages Metric anomaly score Encoder SystemAnomalyScore ... Metric N SDR SDR

25. Largely predictable Metric value Anomalyscore Largely unpredictable Metric value Anomalyscore

26. Grok for IT Monitoring Breakthrough Science for Anomaly Detection Reinventing UX for IT Monitoring • Detects problems thresholds miss • Continuous learning • Automated model building • State-of-the art neocortical model • Smartphone-centric • Ranks anomalous instances • Rapid drill down • Continuously updated • User-controlled notifications In private beta for Amazon AWS cloud users grokbeta@GrokSolutions.com

27. Extensible Architecture • Custom metrics for any application/server • Web interface and mobile client source code available under no-cost license • Engine API to be published • NuPIC open source community