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Applications of HTM
Chetan Surpur, Software Engineer
Numenta Workshop – October 17, 2014
Current Implementation of HTM
Implemented
Research in
progress
The Future of Data Analytics
Requirements
• Automated model creation
(billions of models)
• Unsupervised training,
continu...
Data and Problem Characteristics
 Is your data time-series?
 Is your data high-velocity?
 Do you need real-time predict...
Application Examples
Grok for server
monitoring
Rogue human
behavior
Geospatial
tracking
Natural language
search/predictio...
Application: Server monitoring
HTM
Scalar
Encoder
SDR
Metric
Anomalies
Notifications
Numerical data
Sampled every 5 mins
....
Application: Server monitoring
SlowSudden In predictable data In noisy data
Easy to start with on AWS. Either:
• Use with ...
Application: Geospatial tracking
HTM
Geospatial
Encoder
SDR
Metric
Latitude, Longitude, Speed
Sampled every 1 min
.
.
.
HT...
Application: Geospatial tracking
Position anomaly Speed anomaly Direction anomaly
Learning a route
Application: Natural language
HTM
Word
Encoder
SDR
Metric
Next word from text stream
.
.
.
HTM
Word
Encoder
SDR
Metric Pre...
Application: Natural language
+
- =
Apple Fruit Computer
Macintosh
Microsoft
Mac
Linux
Operating system
….
Word 3Word 2Wor...
Application: Natural language
+
Training set
eatsfox
?
frog eats flies
cow eats grain
elephant eats leaves
goat eats grass...
Application: Natural language
+
Training set
eatsfox
rodent
frog eats flies
cow eats grain
elephant eats leaves
goat eats ...
Your data
HTM
Encoder
SDRMetric(s)
Predictions
Anomalies
• Time-series
• High-velocity
• Real-time
• Many automated models...
Questions?
Follow us on Twitter @numenta
Sign up for our newsletter at www.numenta.com
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Applications of Hierarchical Temporal Memory (HTM) Slide 1 Applications of Hierarchical Temporal Memory (HTM) Slide 2 Applications of Hierarchical Temporal Memory (HTM) Slide 3 Applications of Hierarchical Temporal Memory (HTM) Slide 4 Applications of Hierarchical Temporal Memory (HTM) Slide 5 Applications of Hierarchical Temporal Memory (HTM) Slide 6 Applications of Hierarchical Temporal Memory (HTM) Slide 7 Applications of Hierarchical Temporal Memory (HTM) Slide 8 Applications of Hierarchical Temporal Memory (HTM) Slide 9 Applications of Hierarchical Temporal Memory (HTM) Slide 10 Applications of Hierarchical Temporal Memory (HTM) Slide 11 Applications of Hierarchical Temporal Memory (HTM) Slide 12 Applications of Hierarchical Temporal Memory (HTM) Slide 13 Applications of Hierarchical Temporal Memory (HTM) Slide 14 Applications of Hierarchical Temporal Memory (HTM) Slide 15
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Applications of Hierarchical Temporal Memory (HTM)

Presentation by Chetan Surpur at Numenta Workshop on October 17, 2014.

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Applications of Hierarchical Temporal Memory (HTM)

  1. 1. Applications of HTM Chetan Surpur, Software Engineer Numenta Workshop – October 17, 2014
  2. 2. Current Implementation of HTM Implemented Research in progress
  3. 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. 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. 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. 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. 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. 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. 9. Application: Geospatial tracking Position anomaly Speed anomaly Direction anomaly Learning a route
  10. 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. 11. Application: Natural language + - = Apple Fruit Computer Macintosh Microsoft Mac Linux Operating system …. Word 3Word 2Word 1 HTM
  12. 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. 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. 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. 15. Questions? Follow us on Twitter @numenta Sign up for our newsletter at www.numenta.com
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Presentation by Chetan Surpur at Numenta Workshop on October 17, 2014.

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