Office Seminar Slide.

1. Md. Asfak Mahamud
m.asfak@yahoo.com
Software Engineer
KAZ Software Ltd.
Bangladesh
Date: Feb 22, 2011
Introduction to HTM

2. Disclaimer
This theory/technology is a result from research of several decades. This
theory/technology is doing a great job in various practical fields today. Many
companies (for more info http://numenta.com/about-numenta/customers.php ) are using it actively. Many
(e.g. http://www.atl.lmco.com/papers/1597.pdf) are doing research on its various applications.
The speaker has been reading on this technology for a very short time. Any misleading
or wrong explanation may occur due to lack of knowledge of the speaker.
Every slide on this presentation has been tried to serve with source of reference (if
required). For any confusion audiences are requested to visit the reference pages.
The speaker hereby is declaring earnest eagerness to change any content of this
presentation if it violets any code of conduct of any organization or company or
institutions or hurts any individual.

3. Points
• Ground.
• Rule.
• Ball.
• Bat.
• Umpire.
• Player.
• Powerplay.
This is the first time Bangladesh is one of the
organizing countries of ICC World Cup Cricket.
To keep a remembrance of on going cricket craze, points
are intentionally coined from sports more specifically
from Cricket.
Image url: http://en.wikipedia.org/wiki/File:2011_Cricket_World_Cup_Logo.svg

4. Points
• Ground. (A rational motive for a belief or action - wordweb)
• Rule.
• Ball.
• Bat.
• Umpire.
• Player.
• Powerplay. Image source: http://www.cricket-for-parents.com/cricket-fielding-positions.html

5. At 19 Weeks of Fetus
Baby's brain designates specialized
areas for
– smell, taste, hearing, vision, and touch.
Some research suggests that baby
may be able to hear sound now.
Reference: http://www.babycenter.com/fetal-development-images-19-weeks
Image collected and edited from: http://www.babycenter.com/fetal-development-images-19-weeks

6. Aniya’s Learning
At her two years of old my niece, Aniya somehow knew about dog (in bengali কুকুর, ‘Kukur’). In her
voice it was “Kukun”, কুকুন। She also knew the bengali word বড় ( বড়, ‘Boro’ means Big). In her voice it was
‘Bolo’, বোলো।
কুকুন
Image: http://jaagruti.org/2010/04/18/the-indian-street-dog/
http://www.impactlab.net/2007/04/13/building-brainlike-computers/
Image: http://www.travelblog.org/Photos/1654785
বোলো কুকুন
Supervised Unsupervised

7. Ambiguity
Vernon B Mountcastle
Formerly University Professor of Neuroscience,
Johns Hopkins University, Baltimore, Maryland, USA
Image: http://www.ibro.org/Pub/Pub_Main_Display.asp?LC_Docs_ID=3561
An organizing principle for
cerebral function: the unit
module and the distributed
system. In The Mindful Brain.
MIT Press, 1978.
Info:
Video: http://tinyurl.com/4n9ux59
Dileep George, Cognitive Computing, 2007
Paper: http://tinyurl.com/464x4yt
Dileep George, Phd Thesis, Stanford University, June, 2008
Same Algorithm for
- Audition,
- Vision,
- Speech
and so on.
Common Cortical Algorithm
No learning algorithm has an inherent
superiority over another algorithm for all
learning problems.
(Wolpert, 1995)
No Free Lunch Theorem
“An algorithm’s superiority comes from the
assumptions that it makes about the problem
at hand.”

8. Solution
To find Assumptions
– General enough
to large classes of problems.
– Specific enough
to make learning possible.
Info from:
Video: http://tinyurl.com/4n9ux59
Dileep George, Cognitive Computing, 2007
To believe 3 three things
• The principles of brain function can be understood.
• We can build machines that work on these principles.
• Many machine learning, AI and robotics problems can only
be solved this way.
Jeff Hawkins,
November 2010,
“Advances in Modeling Neocortex and its impacts on
machine intelligence”
Link: http://tinyurl.com/4reyyzf
World Cortex
Physics
Statistics
Biology
Anatomy
Structure
Physiological Results
Psychological Results
Info from: video: http://tinyurl.com/4n9ux59 Dileep George, Cognitive Computing, 2007

9. Comparison of neocortex among mouse,
monkey and human. The neocortical
surfaces are colored blue.
Image ref:
http://www.nibb.ac.jp/brish/Gallery/cortexE.html
Why Neocortex is on focus?
Felleman and Van Essens (1991)
Model of the cortical hierarchy
Image from:
http://thebrain2.wikidot.com/tribal-networks
• 75% of volume of human brain
• All high level vision, audition, motor, language, thought.
• Composed of a repetitive element
– Complex
– Hierarchical
Jeff Hawkins,
November 2010,
“Advances in Modeling Neocortex and
its impacts on machine intelligence”
Link: http://tinyurl.com/4reyyzf

10. Cells that are vertically
aligned in columns all
respond
to edges with the same
orientation.
Figure: Response properties
of cells in V1, the first cortical
region to process information
from the retina.
Ref: Appendix A: A Comparison between Biological Neurons and HTM cells
Appendix B: A Comparison of Layers in the Neocortex and an HTM Region
Hierarchical Temporal Memory including HTM Cortical Learning Algorithms
http://tinyurl.com/4pr59bv
Biological Neuron and Layers in Neocortex
ApicalDendrite
DistalDendrite
ProximalDendrite
Axon
Layered and Columnar organization
of the neocortex becomes evident when
neural tissue is stained.
Mini-Column: The smallest columnar
Structure of the neocortex.
Diameter: 30um (Approx )
Contains: 80-100 neurons
across all five cellular layers.

11. Inter Region Major Connections
Ref: Appendix B: A Comparison of Layers in the Neocortex and an HTM Region
Hierarchical Temporal Memory including HTM Cortical Learning Algorithms
http://tinyurl.com/4pr59bv
Indirect
Feed forward
Pathway.
Direct Feed
forward Pathway.
Feed back
Pathway.

12. Biological Neuron, Simple Artificial Neuron, HTM Cell,
HTM Regions
Ref: Appendix A: A Comparison between Biological Neurons and HTM cells
Hierarchical Temporal Memory including HTM Cortical Learning Algorithms
http://tinyurl.com/4pr59bv
DistalDendrite
Proximal Dendrite
Axon
Jeff Hawkins,
November 2010,
“Advances in Modeling Neocortex and its impacts on machine intelligence”
Link: http://tinyurl.com/4reyyzf
“HOW THE BRAIN MIGHT WORK: A HIERARCHICAL AND
TEMPORAL MODEL FOR LEARNING AND RECOGNITION”,
Dileep George, June 2008,
Stanford University

13. Old Wine in New Glass?
NO !!
 Hierarchy in space and time.
 Slowness of time, combined with the hierarchy, enables efficient learning of intermediate
levels of the hierarchy.
 Learning of causes by using time continuity and actions.
 Models of attention and specific memories.
 A probabilistic model specified in terms of relations between a hierarchy of causes.
 Belief Propagation in the hierarchy to use temporal and spatial context for inference.
Ref: http://numenta.com/htm-overview/education/HTM_Comparison.pdf

14. Old Wine in New Glass?
Ref: http://numenta.com/htm-overview/education/HTM_Comparison.pdf
General-
Purpose
Probabilistic
Models
• Baysian Networks
• Energy-based
Models
(HTMs do not treat them as rivals
but tools in its toolbox.)
• HHHMs (Hierarchical Hidden Markov Model, a special
form of Baysian Network)
have hierarchy only in space. HTM has hierarchy in space and time.
• Boltzman Machine and Helhmholtz Machine
do not include temporal aspects of data in the model and do not make
any assumptions about hierarchy.
Non-
Generative
Models
• Support Vector
Machines (SVMs)
• Classic Neural
Networks
• Slow Feature
Analysis (Many properties
similar to HTM)
• They are typically supervised. HTMs are fundamentally
unsupervised.
• They are not able to generate data for predictions. HTM can do
that.
Empirical
Neurobiological
Model
• HMAX Model
(Many properties similar to
HTMs)
• It can not predict forward in time. HTM can do that.

15. Old Wine in New Glass?
Ref: http://numenta.com/htm-overview/education/HTM_Comparison.pdf
General-
Purpose
Probabilistic
Models
• Baysian Networks
• Energy-based
Models
(HTMs do not treat them as rivals
but tools in its toolbox.)
• HHHMs (Hierarchical Hidden Markov Model, a special
form of Baysian Network)
have hierarchy only in space. HTM has hierarchy in space and time.
• Boltzman Machine and Helhmholtz Machine
do not include temporal aspects of data in the model and do not make
any assumptions about hierarchy.
Non-
Generative
Models
• Support Vector
Machines (SVMs)
• Classic Neural
Networks
• Slow Feature
Analysis (Many properties
similar to HTM)
• They are typically supervised. HTMs are fundamentally
unsupervised.
• They are not able to generate data for predictions. HTM can do
that.
Empirical
Neurobiological
Model
• HMAX Model
(Many properties similar to
HTMs)
• It can not predict forward in time. HTM can do that.

16. Old Wine in New Glass?
Ref: http://numenta.com/htm-overview/education/HTM_Comparison.pdf
General-
Purpose
Probabilistic
Models
• Baysian Networks
• Energy-based
Models
(HTMs do not treat them as rivals
but tools in its toolbox.)
• HHHMs (Hierarchical Hidden Markov Model, a special
form of Baysian Network)
have hierarchy only in space. HTM has hierarchy in space and time.
• Boltzman Machine and Helhmholtz Machine
do not include temporal aspects of data in the model and do not make
any assumptions about hierarchy.
Non-
Generative
Models
• Support Vector
Machines (SVMs)
• Classic Neural
Networks
• Slow Feature
Analysis (Many properties
similar to HTM)
• They are typically supervised. HTMs are fundamentally
unsupervised.
• They are not able to generate data for predictions. HTM can do
that.
Empirical
Neurobiological
Model
• HMAX Model
(Many properties similar to
HTMs)
• It can not predict forward in time. HTM can do that.

17. HTM in the List of AI Projects
Some AI Projects
Brain simulation
• Blue Brain Project, HNeT (Holographic Neural Technology), Hierarchical Temporal
Memory
Cognitive architectures
• CALO , SHIAI (Semi Human Instinctive Artificial Intelligence) , Virtual Woman
Games
• Chinook, Deep Blue , FreeHAL, TD-Gammon
Knowledge and reasoning
• Cyc , Eurisko, Open Mind Common Sense, Questsin, SNePS, Watson.
Motion and manipulation
• Cog, Grand Challenge 5
Natural language processing
• AIML, A.L.I.C.E., ELIZA, InfoTame, Jabberwacky, KAR-Talk, PARRY, Proverb, SHRDLU, START, CSAIL,
SYSTRAN, Texai
Planning
• O-Plan.
Ref: http://en.wikipedia.org/wiki/List_of_artificial_intelligence_projects

18. Points
• Ground. (A rational motive for a belief or action - wordweb)
• Rule. (Theory/technology)
• Ball.
• Bat.
• Umpire.
• Player.
• Powerplay.
Image:
http://blogs.trb.com/news/specials/newsillustrated/blog/2009/08/lauderhill_stadium_lets_play_c.html

19. HTM Algorithm
HTM Cortical Learning Algorithm
Jeff Hawkins,
November 2010,
“Advances in Modeling Neocortex and its impacts on machine intelligence”
Link: http://tinyurl.com/4reyyzf
Zeta 1: First generation node algorithms
Image: “HOW THE BRAIN MIGHT WORK: A HIERARCHICAL AND
TEMPORAL MODEL FOR LEARNING AND RECOGNITION “, Dileep
George, 2008, Stanford University

20. HTM Network
Zeta 1: First generation node algorithms
Image: “HOW THE BRAIN MIGHT WORK: A HIERARCHICAL AND TEMPORAL MODEL FOR LEARNING
AND RECOGNITION “, Dileep George, 2008, Stanford University

21. HTM Cortical Learning Algorithm
Some Terminologies
– Cell States
• 3 output states
– Active from feed-forward input
– Active from lateral input
– Inactive
– Dendrite Segments
• Proximal dendrite segment
• Distal dendrite segments
– Synapses
• Potential Synapses
• Permanence
Reference: Chapter 2: HTM Cortical Learning Algorithms
http://numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf

22. HTM Cortical Learning Algorithm
Each HTM region does 3 things
1. Form a sparse distributed
representation of the input.
2. Form a representation of the
input in the context of
the previous inputs.
3. Form a prediction based on
the current input in the context
of previous inputs.
Reference: Chapter 2: HTM Cortical Learning Algorithms.
http://numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf

23. HTM Cortical Learning Algorithm
Context
Representation of the input in the context of the previous inputs.
Reference: Chapter 2: HTM Cortical Learning Algorithms.
http://numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf
The image has 70 columns.
Each column has 4 cells. So it can save 470
Contexts
by graying any combination of its columns’ cells.
িকর্েকট বল ।
বাংলায় বল ।
I have eight oranges.
I ate eggs.

24. SpatialPoolerTemporalPooler
Region
An HTM Region
Idea Reference: Chapter 2: HTM Cortical Learning Algorithms.
http://numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf

25. Input Sensory Data or Output from Lower Region
Input

26. Input Sensory Data or Output from Lower Region
Input&HTMRegion

27. Input Sensory Data or Output from Lower Region
FeedForwardInput

28. Input Sensory Data or Output from Lower Region
InhibitionRadius

29. Input Sensory Data or Output from Lower Region
InhibitionRadius

30. Predictive State No Predictive States
InputtoPredictiveState

31. Active State Active All Cells in Column
InputtoPredictiveState

32. InputtoPredictiveState

33. Points
• Ground. (A rational motive for a belief or action - wordweb)
• Rule. (Theory/technology.)
• Ball. (Problems that fit.)
• Bat.
• Umpire.
• Player.
• Powerplay.
Image source: http://www.gettyimages.com/detail/89127087/Photographers-Choice

34. Problem Properties
Spatial Hierarchy
Data generated by common
sub-causes are highly
correlated.
In image, adjacent pixels more highly
correlated than distant pixels.
Ref: http://numenta.com/htm-overview/education/ProblemsThatFitHTMs.pdf
Image: http://www.impactlab.net/2007/04/13/building-brainlike-computers/
Temporal Hierarchy
Higher level causes vary more slowly
compared to lower-level causes.
In music:
– Lower level individual notes changes very rapidly.
– Notes are combined into musical phrase.
– Phrases are combined into musical section.
– Sections are combined into a symphony.

35. Points
• Ground. (A rational motive for a belief or action – wordweb.)
• Rule. (Theory/technology.)
• Ball. (Problems that fit.)
• Bat. (Existing tools.)
• Umpire.
• Player.
• Powerplay.
A display depicting the history of the cricket bat
http://www.south-africa-tours-and-travel.com/cricket-south-africa.html

36. Numenta Legacy Software
2 Categories
• NuPIC (Numenta Platform for Intelligent Computing)
– Numenta Runtime Engine.
– NuPIC Tools.
– Vision Framework.
• Vision Software
– Vision demo applications
• People tracking demo
• Sample vision networks
– Creating your own vision system
• Vision Toolkit
• Numenta Web Services
Ref: http://numenta.com/legacysoftware.php

37. Points
• Ground. (A rational motive for a belief or action – wordweb.)
• Rule. (Theory/technology.)
• Ball. (Problems that fit.)
• Bat. (Existing tools.)
• Umpire. (People behind.)
• Player.
• Powerplay.
David Shepherd on dreaded Nelson.
http://www.espncricinfo.com/magazine/content/story/149880.html

38. Numenta Leadership Team
Subutai Ahmad
VP of Engineering
BS, Computer Science, Cornell
University
PhD, Computer Science, University of
Illinois, Urbana-Champaign.
Donna Dubinsky
Founder,
Chief Executive
Officer,
Board Chair
B.A. Yale University,
History.
M.B.A., Harvard
Business School.
Jeff Hawkins
Founder
Co-founder: Palm and Handspring,
Architect: PalmPilot and Treo
smartphone.
Book: On Intelligence. (2004)
With Dileep George and Donna Dubinsky,
founded Numenta in 2005.
B.S. Electrical Engineering, Cornell
University, 1979.
Elected to the National Academy of
Engineering in 2003.
Dileep George
Founder
Led the development of
the first generation of
algorithms for
Numenta's HTM
technology. 2005 - 2010
Stanford UniversityPh.D in
Electrical Engineering, 2008
Redwood Neuroscience
InstituteResearch Fellow,
2003 - 2005

39. Numenta Board of Directors
Donna Dubinsky
Founder
Jeff Hawkins
Founder
Ed Colligan
Former President & Chief Executive Officer, Palm, Inc
Mike Farmwald
General Partner,
Skymoon Ventures
Harry Saal
Chairman of the Technical Committee
USDOJ v. Microsoft Consent Decree

40.
Gill Bejerano
Assistant Professor
Developmental Biology and Computer Science
Stanford University
James J. DiCarlo M.D., Ph.D.
Associate Professor of Neuroscience
Massachussetts Institute of Technology
William T. Freeman
Professor
Electrical Engineering and Computer Science
Massachussetts Institute of Technology
Andrew Y. Ng
Assistant Professor
Computer Science
Stanford University
Tomaso A. Poggio
Eugene McDermott Professor, McGovern Institute
Massachussetts Institute of Technology
Ref: http://numenta.com/about-numenta/people.php
Numenta Technical Advisory Board

41. Points
• Ground. (A rational motive for a belief or action – wordweb.)
• Rule. (Theory/technology.)
• Ball. (Problems that fit.)
• Bat. (Existing tools.)
• Umpire. (People behind.)
• Player. (Customers)
• Powerplay. Bangladesh Cricket Team on a Victory Lap
Image:
http://www.criclounge.com/news/1637/New-Zealand-is-Bangla-washed.

42. Some Customers
http://www.vitamindinc.com
http://www.edsa.com/
http://www.forbes.com/
Other development partners are working on
- detecting financial fraud,
- creating new web analytical tools, and
- analyzing images for digital pathology.
To learn more: video from Numenta's 2009 HTM Workshop.
For more info: http://numenta.com/about-numenta/customers.php

43. EDSA Power Analytics
http://www.edsa.com/
Ref: http://www.edsa.com/pa_articles/self_learning.php

44. http://www.atl.lmco.com/papers/1597.pdf
Lockheed Martin has been using and modifying HTM technology for several applications

45. http://www.atl.lmco.com/papers/1597.pdf
Multimodal Pattern Recognition with Hierarchical Temporal Memory (MPR-HTM)

46. Points
• Ground. (A rational motive for a belief or action – wordweb.)
• Rule. (Theory/technology.)
• Ball. (Problems that fit.)
• Bat. (Existing tools.)
• Umpire. (People behind.)
• Player. (Customers)
• Powerplay. (Demo)
Image: http://www.cricketupdates.org/what-is-meant-by-powerplaypp-in-cricket.html

47. Application Demo
vitamind
Site: www.vitamindinc.com
White Paper: http://www.vitamindinc.com/downloads/Vitamin%20D%20white%20paper.pdf
For press review: http://www.vitamindinc.com/press.html

48. Application Demo
vitamind
Site: www.vitamindinc.com
White Paper: http://www.vitamindinc.com/downloads/Vitamin%20D%20white%20paper.pdf
For press review: http://www.vitamindinc.com/press.html
Vitamind
Outlook
Picasa
Webcam/Network Cam
People
Detected Mail,
With detection image
By macro attached images
from the mails are saved in a directory.

49. Tools Demo
• NuPIC (Numenta Platform for Intelligent Computing)
• Numenta Vission Software
Vision Framework demo Vision Toolkit demo

50. Java Demo
Courtesy: teddybot http://numenta.com/phpBB2/viewtopic.php?t=1368
vincentvanrijn
Courtesy: vincentvanrijn http://numenta.com/phpBB2/viewtopic.php?t=1368

51. Hierarchical Temporal Memory
• Ground. (A rational motive for a belief or action – wordweb.)
• Rule. (Theory/technology.)
• Ball. (Problems that fit.)
• Bat. (Existing tools.)
• Umpire. (People behind.)
• Player. (Customers)
• Powerplay. (Demo)
Let’sPlay

52. Thanks
www.numenta.com
www.onintelligence.org