ACM CIKM October 31, 2013 Jeff Hawkins On-line Learning From Streaming Data

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

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

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

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

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

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

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

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

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

Sparse Distributed Representations (SDRs) 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 …………01000 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 = m

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

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

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

SDR (time =1)

SDR (time =2)

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

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

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

1)NuPIC Open Source Project Three Current Directions

Single source tree (used by GROK) GPLv 3 Steady community growth – 67 contributors (+26 since July) – 245 mailing list subscribers – 1621 total messages eBook from community member OS community joining Kaggle Competitions Fall Hackathon: 70 attendees NuPIC Open Source Project

1)NuPIC Open Source Project 2)Custom CLA Hardware -Needed for scaling research and commercial applications -DARPA “Cortical Processor” -IBM, Seagate, Sandia Labs 3)Commercialization Three Current Directions

2. Look at data3. Build models Problem: - Doesn’t scale with velocity and #models Solution: - Automated model creation - Continuous learning - Temporal inference 1. Store data Stream data Automated model creation Continuous learning Temporal inference Predictions Anomalies Actions Past Future Data: Past and Future

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

Largely predictable Largely unpredictable Metric value Anomaly score Metric value Anomaly score

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

 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 Extensible Architecture