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Hierarchical Temporal Memory (HTM) Jeff Hawkins May 10, 2006 IBM A new computational paradigm based on cortical theory.

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Presentation on theme: "Hierarchical Temporal Memory (HTM) Jeff Hawkins May 10, 2006 IBM A new computational paradigm based on cortical theory."— Presentation transcript:

1 Hierarchical Temporal Memory (HTM) Jeff Hawkins May 10, 2006 IBM A new computational paradigm based on cortical theory

2

3 Pipe Dream Driven By Greed Mother Of All Markets Today’s PDA Market Indicator

4 Not in our Lifetime Any Moment Now Today’s Cognitive Computing Indicator

5  Not in our lifetime - Decades of effort - AI - neural networks - fuzzy logic - 5 th generation project - decade of the brain - Not much success - vision, language, robotics - Brain is very complex

6  Not in our lifetime - Decades of effort - AI - neural networks - fuzzy logic - 5 th generation project - decade of the brain - Not much success - vision, language, robotics - Brain is very complex  Any moment now - Neocortex: - Fast - Flexible - Robust years of data - Anatomical, physiological - Mathematics - Common cortical algorithm - Cortical Theory (HTM)

7 WorldHTM/CortexSenses People Cars Buildings Words Songs Ideas patterns

8 WorldHTM/CortexSenses People Cars Buildings Words Songs Ideas patterns “Causes”“Beliefs” cause cause cause cause cause cause6 0.08

9 Causes Representations of Causes HTM What does an HTM do? 1 Discover causes in the world 2 Infer causes of novel input 3 Predict future 4 Direct motor behavior

10 Sensory data Belief HTMs use a hierarchy of memory nodes

11 Sensory data Beliefs HTMs use a hierarchy of memory nodes Each node:Discovers causes (of its input) Passes beliefs up Passes predictions down

12 Sensory data Beliefs HTMs use a hierarchy of memory nodes Each node:Discovers causes (of its input) Passes beliefs up Passes predictions down Each node:Stores common sequences Changing sensory data forms stable beliefs at top Stable beliefs at top form changing sensory predictions

13 1) Why does hierarchy make a difference? 2) How does each node discover and infer causes?

14 Why does hierarchy make a difference? 1) Shared representations lead to generalization and efficiency

15 Why does hierarchy make a difference? 1) Shared representations lead to generalization and efficiency 2) HTM hierarchy matches spatial and temporal hierarchy of causes in world

16 Why does hierarchy make a difference? 1) Shared representations lead to generalization and efficiency 2) HTM hierarchy matches spatial and temporal hierarchy of causes in world 3) Belief propagation techniques ensure all nodes quickly reach mutually compatible beliefs

17 80% woof 20% meow 70% pig image 30% cat image 90% cat CPT Belief Propagation

18 Why does hierarchy make a difference? 1) Shared representations lead to generalization and efficiency 2) HTM hierarchy matches spatial and temporal hierarchy of causes in world 3) Belief propagation techniques ensure all nodes quickly reach mutually compatible beliefs 4) Affords mechanism for attention

19 How does each node discover causes?

20 1) Learn common spatial patterns 2) Learn common sequences of spatial patterns

21 How does each node discover causes? 1) Learn common spatial patterns (things that happen at the same time are likely to have a common cause)

22 How does each node discover causes? 1) Learn common spatial patterns Common patterns: remember Uncommon patterns: ignore

23 How does each node discover causes? 1) Learn common spatial patterns 2) Learn common sequences of spatial patterns

24 How does each node discover causes? Common sequence: assign to cause time Common sequence: assign to cause Uncommon sequence: ignore 1) Learn common spatial patterns 2) Learn common sequences of spatial patterns

25 How does each node discover causes? 1) Learn common spatial patterns 2) Learn common sequences 3) Use context from above in hierarchy

26 Do HTMs really work?

27 4 pixels Level 1 Level 2 Level 3 Simple HTM vision system (32x32 pixel)

28 Training images

29 Training images CorrectIncorrect

30

31 Correctly recognized images

32 Numenta Plan 1) Develop a detailed computational theory of neocortical function (HTM) On Intelligence (Times Books, 2004) HTM white paper, Biological mapping paper, August 2006

33 Numenta Plan 1) Develop a detailed computational theory of neocortical function (HTM) 2) Develop a software platform for HTM applications

34 Net list Supervisor Dev Tools Configurator Trainer Debugger Supervisor API Node Processor 2 Node Processor N : Gigabit switch Fileserver Node Processor Numenta Platform Run time environment

35 Numenta Plan 1) Develop a detailed computational theory of neocortical function (HTM) 2) Develop a software platform for HTM applications Multiple processor/server architecture Optimized C++ routines Developer toolset with flexible scripting using Python Supports Linux + MacOS. Windows to come. Build a community of developers  Early access partners, 2 nd meeting end of May 2006 Beta release early 2007

36 Numenta Plan 1) Develop a detailed computational theory of neocortical function (HTM) 2) Develop a software platform for HTM applications 3) Test HTM with a machine vision system

37 Numenta Machine Vision System  Robust Object Recognition From Natural Images  Recognition Task Defined  Data collection in process Highly realistic 3D models and textures used to generate sequences 90,000 images and 102 sequences collected to date Each image has accurate alpha channel for programmatic 2D modifications

38 HTM Applications 1) What humans find easy and computers hard - vision, language, robotics - many apps from security to self-driving cars - extend with new senses, IR, sonar, radar… 2) Discovering causes in unusual worlds - geology, markets, weather, physics, genetics

39 HTM Capabilities 1) Discover causes 2) Inference 3) Prediction 4) Behavior Beyond biology 1) Faster 2) Larger 3) Exotic senses

40 (white paper posted this week)

41 Not in our Lifetime Any Moment Now Today’s Cognitive Computing Indicator

42 Thank _ _ _

43 HTM HTM models world, including hardwired motor behaviors world motor HTM Representations of motor behavior are auto-associatively paired with motor generators world motor

44 Hierarchical Temporal Memory 1) Powerful, flexible, robust 2) Can be applied to many problems - vision - language - robotics - manufacturing - business modeling - market modeling - network modeling - resource exploration - weather prediction - math, physics

45 Discovering and inferring causes has proven to be very difficult, e.g. - visual pattern recognition - language understanding - machine learning Sensory data ? Beliefs (of causes)

46  “What is conspicuously lacking is a broad framework of ideas within which to interpret these different approaches.”  Francis Crick, 1979

47 Belief Propagation

48 “maybe diagonal line, maybe vertical line”

49 Belief Propagation “maybe diagonal line, maybe vertical line” “maybe diagonal line, maybe vertical line”


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