A New Theory of Neocortex and Its Implications for Machine Intelligence

TTI/Vanguard, All that Data

February 9, 2005

Jeff Hawkins

Director

The Redwood Neuroscience Institute

Intelligence Paradigms

Artificial Intelligence (AI) 1940s - 1980s- ignores biology- computer programs- emulate human behavior

Neural Networks 1970s - 1990s- mostly ignores biology- networks of “neurons”- classify spatial patterns

Intelligence Paradigms

Artificial Intelligence (AI) 1940s - 1980s- ignores biology- computer programs- emulate human behavior

Neural Networks 1970s - 1990s- mostly ignores biology- networks of “neurons”- classify spatial patterns

“Real Intelligence” 2005 –- biologically derived- hierarchical temporal memory- pattern prediction

Hierarchical Temporal Memories (HTMs)

A Fundamental technology

Automatically discover causes in complex systems

Predict future behavior of complex systems

Can build super-human intelligence (not C3PO)

- faster- more memory- novel senses

Agenda

Introduction to neocortex

What does the neocortex do?

How does it do it?

Can we express this mathematically?

How do we build it?

What problems can be solved?

Agenda

Introduction to neocortex

What does the neocortex do?

How does it do it?

Can we express this mathematically?

How do we build it?

What problems can be solved?

Agenda

Introduction to neocortex

What does the neocortex do?

How does it do it?

Can we express this mathematically?

How do we build it?

What problems can be solved?

1) The neocortex is a memory system.

2) Through exposure, it builds a model the world.

3) The neocortical memory model predicts future eventsby analogy to past events.

Reptilian brain

Reptilian brain

Sophisticatedsenses

Behavior

Mammalian brain

Reptilian brain

Sophisticatedsenses

Behavior

Neocortex

Human brain

Reptilian brain

Sophisticatedsenses

Complexbehavior

Neocortex

Agenda

Introduction to neocortex

What does the neocortex do?

How does it do it?

Can we express this mathematically?

How do we build it?

What problems can be solved?

http://brainmapping.loni.ucla.edu/BMD_HTML/SharedCode/slides/fMRIBasics/img021.gif

Hierarchical connectivity

touchmotor

audition vision

spatiallyspecific

spatiallyinvariant

fastchanging

slowchanging

“features”“details”

“objects”

touchmotor

audition vision

Prediction

touchmotor

audition vision

Prediction across senses

touchmotor

audition vision

Sensory/motor integration

touchmotor

audition vision

touchmotor

audition vision

touchmotor

audition vision

What does each region do?

?

touchmotor

audition vision

What does each region do?

Every region:

1) Stores sequences

2) Passes sequence “name” up

3) Predicts next element

4) Converts invariant predictioninto specific prediction

5) Passes specific prediction “down”

Hierarchical cortex captures hierarchical structure of world

- sequences of sequences - structure within structure

Unanticipated events rise up the hierarchy until some region can interpret it.

Hippocampus is at the top.Novel inputs that cannot be explained as part of known structure automatically rise to the top.

HC

Unanticipated events rise up the hierarchy until some region can interpret it.

Hierarchical Temporal MemoriesCan Explain Many Psychological Phenomena

- Creativity, Intuition, Prejudice

- Thought

- Consciousness

- Learning

How does a region work - biology

Every region:

1) Stores sequences

2) Passes sequence “name” up

3) Predicts next element

4) Converts invariant predictioninto specific prediction

5) Passes specific prediction “down”

Agenda

Introduction to neocortex

What does the neocortex do?

How does it do it?

Can we express this mathematically?

How do we build it?

What problems can be solved?

All inputs and outputs from a memory region are probability distributions

Lower regions

Higher regions

Learning

SA(xt,xt+1,...)

SB(xt,xt+1,...)

Lower regions

C

Higher regions

C = causes or context

S = sequences

X = input

X

P(S|C)

Recognition without context

SA(xt,xt+1,...)

SB(xt,xt+1,...)

Lower regions

P(C)

Higher regions

X

P(S|C)

Recognition with context can lead to new interpretation

SA(xt,xt+1,...)

SB(xt,xt+1,...)

Lower regions

C1

Higher regions

X

P(S|C)

C1

Passing a belief down the hierarchy

SA(xt,xt+1,...)

SB(xt,xt+1,...)

Lower regions

Higher regions

Xt

P(S|C)

C

f ( Xt, P(S|C) )

C

Predicting the future

SA(xt,xt+1,...)

SB(xt,xt+1,...)

Lower regions

C

Higher regions

Xt

P(S|C)

C

f ( Xt+1, P(S|C) )

Belief Propagation can determine most likely causes of inputin a hierarchy of conditional probabilities

P(Z1|Y1) P(Z2|Y1) P(Z3|Y1) P(Z4|Y1)

P(Y1|X) P(Y2|X)

P(X)

System Architecture

4 pixels

Level 1

Level 2

Level 3

Recognition : Examples

Correctly Recognized

“Incorrectly” recognized

Correctly Recognized Test Cases

Prediction/Filling-in : Example1

Prediction/Filling-in : Example2

What’s new?

HierarchicalNeocognitronHMaxSeemore, Visnet

Sequence memoryauto-associative memoriessynfire chains

Prediction/feedbackHMMsART

Sensory/motor integrationBiologically derived/constrained/testable

Agenda

Introduction to neocortex

What does the neocortex do?

How does it do it?

Can we express this mathematically?

How do we build it?

What problems can be solved?

Hierarchical Temporal Memories (HTMs)

A Fundamental technology

Automatically discover causes in complex systems

Predict future behavior of complex systems

Can build super-human intelligence (not C3PO)

- faster- more memory- novel senses

What problems can be solved with HTMs?

Traditional AI applications

- Vision- Language- Robotics

Novel modeling applications

- markets- weather- demographics- protein folding- gene interaction- mathematics- physics

www.stanford.edu/~dil/invariance/

www.OnIntelligence.org

Thank ---

Learning sequences

L5/matrix thalamus/L1 auto-associative loop

Creating a sequence “name”

Turning an invariant prediction into a specific prediction