Hierarchical Temporal MemorySubutai Ahmadsahmad@numenta.comVice President, EngineeringNumenta<br />Copyright © 2009 Nument...
Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas int...
Numenta Snapshot<br />Creating a new computing technology, Hierarchical Temporal Memory, based on the structure and functi...
Numenta Timeline<br />2002		Redwood Neuroscience Institute, Jeff Hawkins<br />2004 		On Intelligence, Hawkins and Blakesle...
Demo: An Easy Visual Task<br />Goal: output the name of the object in the image<br />cow<br />sailboat<br />cell phone<br ...
Why Isn’t This Easy For Computers?<br />Huge variations in images, even within a single category<br />It is impossible to ...
Vision4 - Four Category Object Recognition Demo<br />
Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas int...
No Universal Learning Machine<br />No Free Lunch Theorem<br />“no learning algorithm has an inherent superiority over othe...
<ul><li>Many different regions performing specialized functions
Local structure is similar across regions</li></ul>The Neocortex<br />
Common Cortical Algorithm<br />
Cortical Hierarchy<br /><ul><li>Representations are distributed hierarchically
Connections are bidirectional – significant feedback projections
Each region exposed to constantly changing sensory patterns and is constantly predicting future patterns</li></ul>Sensory ...
Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas int...
Hierarchical Temporal Memory (HTM)<br />Common sequences<br />Network of learning nodes<br />All nodes do same thing<br />...
First Order Markov Graph<br />HTM Nodes Learn Static Patterns<br />HTM Node<br />Stable, sparse vectors<br />Memorizes sta...
First Order Markov Graph<br />HTM Nodes Learn Temporal Sequences<br />HTM Node<br />Variable order Markov Chains, “groups”...
First Order Markov Graph<br />HTM Nodes Output Probability Over Sequences<br />HTM Node<br />[P(g1), P(g2), … ]<br />[…], ...
HTM Nodes Are Connected In Hierarchies<br />
Hierarchies Allow Contextual Prediction<br />
Summary: Hierarchical Temporal Memory<br />Common sequences<br />Network of learning nodes<br />All nodes do same thing<br...
Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas int...
Web Analytics<br />Analyze temporal patterns in a very high traffic news website (Forbes.com)<br />Question: Can HTM’s mod...
Which Topic Is The User Interested In Next?<br />?<br />?<br />Time<br />177 total topics<br />Random prediction gives 0.5...
Training Paradigm<br />HTM trained using 100,000 user sequences<br />Temporal pooler builds up a variable order sequence m...
Prediction Based On Page View Statistics<br />?<br />?<br />?<br />?<br />?<br />Time<br />Could predict using no temporal...
First Order Prediction<br />?<br />?<br />?<br />?<br />Time<br />Can do better if we use transition probabilities from ea...
Variable Order Prediction<br />?<br />?<br />Time<br />“Variable order prediction” – how much temporal context you need is...
Summary: Predicting News Topics<br />
Summary: Predicting News Topics<br />HTMs potentially represent a powerful mechanism for predicting and analyzing web traf...
Potential Applications In Web Analytics<br />Increase length of site visits<br />Predict pages that are directly relevant ...
Video Analysis: People Tracking<br />Person<br />
Example Videos – Persons<br />Occlusions<br />Non-ideal lighting<br />Groups/overlapping people<br />Small, non-upright<br />
Non-Persons – Potential False Positives<br />Cars/Vehicles<br />Balloons<br />Trees/foliage/pool sweeper<br />Animals<br />
People Tracking Demo<br />
Applications In Biomedical Imaging<br />Numerous pattern recognition tasks in biomedical imaging<br />
Pattern Detection In Digital Pathology<br />Task: detect patterns in biopsy slides indicative of cancer<br />Glands<br />N...
Early Results Were Promising<br />We trained a network to discriminate glands from other structures<br />Test set accuracy...
HTM For Biomedical Imaging<br />HTM performing quite well in gland detection as well as some other tasks<br />There could ...
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Numenta ACM Data Min - PowerPoint Presentation

  1. 1. Hierarchical Temporal MemorySubutai Ahmadsahmad@numenta.comVice President, EngineeringNumenta<br />Copyright © 2009 Numenta<br />
  2. 2. Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas into Algorithms?<br />How can we incorporate these ideas into Applications?<br />
  3. 3. Numenta Snapshot<br />Creating a new computing technology, Hierarchical Temporal Memory, based on the structure and function of the neocortex<br />16 employees<br />Founded in 2005 by Jeff Hawkins, Donna Dubinsky and Dileep George<br />For-profit company with very long term roadmap and “patient capital”<br />Focus on core technology<br />Currently developing our third generation of algorithms<br />Very selective corporate partnerships and application development<br />
  4. 4. Numenta Timeline<br />2002 Redwood Neuroscience Institute, Jeff Hawkins<br />2004 On Intelligence, Hawkins and Blakeslee<br /> Described theory of Hierarchical Temporal Memory (HTM)<br />2005 Mathematical formalism (Dileep George)<br />2005 Numenta founded to build new computing<br /> platform based on HTM<br />2007 Released NuPIC software platform<br />2008 First HTM Workshop (>200 attendees)<br />2009 Vision toolkit Beta release<br />2010 Prediction toolkit release<br />
  5. 5. Demo: An Easy Visual Task<br />Goal: output the name of the object in the image<br />cow<br />sailboat<br />cell phone<br />rubber duck<br />
  6. 6. Why Isn’t This Easy For Computers?<br />Huge variations in images, even within a single category<br />It is impossible to write down a set of rules or transformations that cover all possibilities<br />
  7. 7. Vision4 - Four Category Object Recognition Demo<br />
  8. 8. Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas into Algorithms?<br />How can we incorporate these ideas into Applications?<br />
  9. 9. No Universal Learning Machine<br />No Free Lunch Theorem<br />“no learning algorithm has an inherent superiority over other learning algorithms for all problems.”<br />(Wolpert, 1995)<br />x<br />Universal Learning Machine<br />Specific Learning Machine<br />Machine with assumptions that match the structure of the world<br />
  10. 10. <ul><li>Many different regions performing specialized functions
  11. 11. Local structure is similar across regions</li></ul>The Neocortex<br />
  12. 12. Common Cortical Algorithm<br />
  13. 13. Cortical Hierarchy<br /><ul><li>Representations are distributed hierarchically
  14. 14. Connections are bidirectional – significant feedback projections
  15. 15. Each region exposed to constantly changing sensory patterns and is constantly predicting future patterns</li></ul>Sensory data<br />(retina)<br />Sensory data<br />(skin)<br />From: Felleman and Van Essen<br />
  16. 16. Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas into Algorithms?<br />How can we incorporate these ideas into Applications?<br />
  17. 17. Hierarchical Temporal Memory (HTM)<br />Common sequences<br />Network of learning nodes<br />All nodes do same thing<br /> Learns common spatial patterns<br /> Learns common sequences(groups patterns with common cause)<br />Create a hierarchical, spatio-temporal model of data<br />Probability of sequences passed up<br />Predicted spatial patterns passed down<br />Bayesian methods resolve ambiguity<br />High level causes<br />Low level causes<br />Common spatial patterns<br />
  18. 18. First Order Markov Graph<br />HTM Nodes Learn Static Patterns<br />HTM Node<br />Stable, sparse vectors<br />Memorizes static patterns, “coincidences”<br />[Input vector]<br />
  19. 19. First Order Markov Graph<br />HTM Nodes Learn Temporal Sequences<br />HTM Node<br />Variable order Markov Chains, “groups”<br />Models frequency of transitions between patterns<br />Memorizes static patterns, “coincidences”<br />[Input vectors]<br />
  20. 20. First Order Markov Graph<br />HTM Nodes Output Probability Over Sequences<br />HTM Node<br />[P(g1), P(g2), … ]<br />[…], […], […], …<br />
  21. 21. HTM Nodes Are Connected In Hierarchies<br />
  22. 22. Hierarchies Allow Contextual Prediction<br />
  23. 23. Summary: Hierarchical Temporal Memory<br />Common sequences<br />Network of learning nodes<br />All nodes do same thing<br /> Learns common spatial patterns<br /> Learns common sequences(groups patterns with common cause)<br />Creates hierarchical model of data<br />Sequence names passed up<br />Predicted spatial patterns passed down<br />Bayesian methods resolve ambiguity<br />High level causes<br />Low level causes<br />Common spatial patterns<br />
  24. 24. Agenda<br />Introduction to Numenta<br />What can we learn from Neuroscience? <br />How can we incorporate these ideas into Algorithms?<br />How can we incorporate these ideas into Applications?<br />
  25. 25. Web Analytics<br />Analyze temporal patterns in a very high traffic news website (Forbes.com)<br />Question: Can HTM’s model temporal statistics and predict topics and pages of interest to users?<br />
  26. 26. Which Topic Is The User Interested In Next?<br />?<br />?<br />Time<br />177 total topics<br />Random prediction gives 0.56% accuracy<br />
  27. 27. Training Paradigm<br />HTM trained using 100,000 user sequences<br />Temporal pooler builds up a variable order sequence model<br />
  28. 28. Prediction Based On Page View Statistics<br />?<br />?<br />?<br />?<br />?<br />Time<br />Could predict using no temporal context, based just on popularity of different topics (“0’th order” prediction)<br />This is what most sites do today<br />Leads to 23% accuracy<br />
  29. 29. First Order Prediction<br />?<br />?<br />?<br />?<br />Time<br />Can do better if we use transition probabilities from each page<br />Improves accuracy from 23% to 28%<br />
  30. 30. Variable Order Prediction<br />?<br />?<br />Time<br />“Variable order prediction” – how much temporal context you need is determined based on individual sequences<br />Accuracy jumps to 45%<br />
  31. 31. Summary: Predicting News Topics<br />
  32. 32. Summary: Predicting News Topics<br />HTMs potentially represent a powerful mechanism for predicting and analyzing web traffic patterns<br />
  33. 33. Potential Applications In Web Analytics<br />Increase length of site visits<br />Predict pages that are directly relevant to each user<br />Increase revenue<br />Predict ad-clicks based on current user’s immediate history<br />Display interesting traffic patterns through a website<br />What are most common sequences?<br />Display changes in traffic patterns<br />How are sequence models changing from day to day?<br />
  34. 34. Video Analysis: People Tracking<br />Person<br />
  35. 35. Example Videos – Persons<br />Occlusions<br />Non-ideal lighting<br />Groups/overlapping people<br />Small, non-upright<br />
  36. 36. Non-Persons – Potential False Positives<br />Cars/Vehicles<br />Balloons<br />Trees/foliage/pool sweeper<br />Animals<br />
  37. 37. People Tracking Demo<br />
  38. 38. Applications In Biomedical Imaging<br />Numerous pattern recognition tasks in biomedical imaging<br />
  39. 39. Pattern Detection In Digital Pathology<br />Task: detect patterns in biopsy slides indicative of cancer<br />Glands<br />Not glands<br />Malformed glands -> could be prostate cancer<br />
  40. 40. Early Results Were Promising<br />We trained a network to discriminate glands from other structures<br />Test set accuracy was around 95%<br />Glands<br />Not glands<br />
  41. 41. HTM For Biomedical Imaging<br />HTM performing quite well in gland detection as well as some other tasks<br />There could be applications in other areas of Biomedical Imaging<br />Radiology<br />Electron microscopy<br />….<br />Key differentiator: <br />General purpose pattern recognition algorithm<br />Most existing work involves coding very specific algorithms to specific patterns<br />
  42. 42. Applications Areas<br />Web analytics<br />Biomedical Imaging<br />Video Analysis<br />Credit card fraud<br />Automotive<br />Gaming<br />Drug discovery<br />Business modeling<br />Healthcare<br />
  43. 43. Working With Numenta On HTMs<br /><ul><li>NuPIC, Numenta Platform For Intelligent Computing, available free for research on numenta.com
  44. 44. Support through an active forum
  45. 45. Contains implementation of our second generation of algorithms
  46. 46. Vision Toolkit Beta, free for research
  47. 47. Easy to use GUI for creating vision applications
  48. 48. Includes hosted inference and a web services API
  49. 49. Internships available for students!
  50. 50. Send email to interns@numenta.com</li></li></ul><li>THANK YOU!!<br />sahmad@numenta.com<br />

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