Applications of Hierarchical Temporal Memory (HTM)
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Presentation by Chetan Surpur at Numenta Workshop on October 17, 2014.
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Applications of Hierarchical Temporal Memory (HTM)
- 1. Applications of HTM Chetan Surpur, Software Engineer Numenta Workshop – October 17, 2014
- 2. Current Implementation of HTM Implemented Research in progress
- 3. The Future of Data Analytics Requirements • Automated model creation (billions of models) • Unsupervised training, continuous learning • Real-time Actions data streams Tomorrow online models Challenges • People, not automated • Model obsolescence • Slow reaction visualization models storage Today data
- 4. Data and Problem Characteristics Is your data time-series? Is your data high-velocity? Do you need real-time predictions and anomalies? Do you have too many individual data sources to hand-craft models? Do you need your models to learn continuously? Is your data unlabeled?
- 5. Application Examples Grok for server monitoring Rogue human behavior Geospatial tracking Natural language search/prediction Stock volume anomalies HTM Encoder SDRMetric(s) Predictions Anomalies
- 6. Application: Server monitoring HTM Scalar Encoder SDR Metric Anomalies Notifications Numerical data Sampled every 5 mins . . . HTM Scalar Encoder SDR Metric Anomalies Notifications
- 7. Application: Server monitoring SlowSudden In predictable data In noisy data Easy to start with on AWS. Either: • Use with IT data and Cloudwatch, or • Feed in custom metrics
- 8. Application: Geospatial tracking HTM Geospatial Encoder SDR Metric Latitude, Longitude, Speed Sampled every 1 min . . . HTM Geospatial Encoder SDR Metric Anomalies Notifications Anomalies Notifications
- 9. Application: Geospatial tracking Position anomaly Speed anomaly Direction anomaly Learning a route
- 10. Application: Natural language HTM Word Encoder SDR Metric Next word from text stream . . . HTM Word Encoder SDR Metric Prediction …and she jumped …will emerge in May Prediction
- 11. Application: Natural language + - = Apple Fruit Computer Macintosh Microsoft Mac Linux Operating system …. Word 3Word 2Word 1 HTM
- 12. Application: Natural language + Training set eatsfox ? 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 ---- ---- ----- HTM
- 13. Application: Natural language + Training set eatsfox rodent 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 ---- ---- ----- HTM • Unsupervised learning • Semantic generalization – SDR: lexical – HTM: grammatic
- 14. Your data HTM Encoder SDRMetric(s) Predictions Anomalies • Time-series • High-velocity • Real-time • Many automated models • Continuous learning • Unlabeled data • Scalar • Date / time • Category • Positional / Geospatial • Word (Cortical.io) • … Early, informed actions
- 15. Questions? Follow us on Twitter @numenta Sign up for our newsletter at www.numenta.com