ResearchNeocortical theory Algorithms
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Catalyst for machine intelligence
Brains, Data, and Machine Intelligence
Jeff [email protected]
“If you invent a breakthrough so computers can learn, that is worth 10 Microsofts”
"If you invent a breakthrough so computers that learn that is worth 10 Micros
1) What principles will we use to build intelligent machines?
2) What applications will drive adoption in the near and long term?
Machine Intelligence
1) Flexible (universal learning machine)
2) Robust
3) If we knew how the neocortex worked, we would be in a race to build them.
Machine intelligence will be built on the principles of the neocortex
What the Cortex Does
data streamretina
cochlea
somatic
The neocortex learns a model from sensory data
- predictions - anomalies - actions
The neocortex learns a sensory-motor model of the world
“Hierarchical Temporal Memory” (HTM)
1) Hierarchy of nearly identical regions
- across species- across modalities
retina
cochlea
somatic2) Regions are mostly sequence memory
- for inference- for motor
3) Feedforward: Temporal stability
Feedback: Unfold sequences
data stream
2.5 mm
Cortex
Layers
Layers & Columns
2/3456
2/3
4
5
6
Sequence memory: high-order inference
Sequence memory: sensory-motor inference
Sequence memory: motor generation
Sequence memory: attention
Cortical Learning Algorithm (CLA)
CLA is a cortical model, not another “neural network”
2/3
4
5
6
L4 and L2/3: Feedforward Inference
Copy of motor commands
Sensor/afferent data
Learns sensory-motor transitions
Learns high-order transitions Stable
Predicted
Pass throughchanges
Un-predicted
Layer 4 learns sensory-motor transitions.Layer 3 learns high-order transitions.
These are universal inference steps.They apply to all sensory modalities.
Next higher region
A-B-C-DX-B-C-Y
A-B-C- ? DX-B-C- ? Y
2/3
4
5
6
L5 and L6: Feedback, Behavior and Attention
Learns sensory-motor transitions
Learns high-order transitions
Well understoodtested / commercial
Recalls motor sequences
Attention
90% understoodtesting in progress
50% understood
10% understood
Feed
forw
ard
Feed
bac
k
How does a layer of neurons learn sequences?
2/3
4
5
6
Learns high-order transitions
First:- Sparse Distributed Representations- Neurons
Cortical Learning Algorithm (CLA)
Sparse Distributed Representations (SDRs) • Many bits (thousands)
• Few 1’s mostly 0’s• Example: 2,000 bits, 2% active
• Each bit has semantic meaningLearned
01000000000000000001000000000000000000000000000000000010000…………01000
Dense Representations• Few bits (8 to 128)• All combinations of 1’s and 0’s• Example: 8 bit ASCII
• Bits have no inherent meaningArbitrarily assigned by programmer
01101101 = m
SDR Properties
1) Similarity: shared bits = semantic similarity
subsampling is OK
3) Union membership:
Indices12|10
Is this SDRa member?
2) Store and Compare: store indices of active bits
Indices12345|40
1)2)3)
….10)
2%
20%Union
Model neuronFeedforward
100’s of synapses“Classic” receptive fieldContext
1,000’s of synapsesDepolarize neuron“Predicted state”
Active Dendrites
Pattern detectorsEach cell can recognize100’s of unique patterns
Neurons
Learning TransitionsFeedforward activation
Learning TransitionsInhibition
Learning TransitionsSparse cell activation
Time = 1Learning Transitions
Time = 2Learning Transitions
Learning TransitionsForm connections to previously active cells.Predict future activity.
This is a first order sequence memory.It cannot learn A-B-C-D vs. X-B-C-Y.
Learning TransitionsMultiple predictions can occur at once.A-B A-C A-D
High-order Sequences Require Mini-columnsA-B-C-D vs. X-B-C-Y
A
X B
B
C
C
Y
D
Before trainingA
X B’’
B’
C’’
C’
Y’’
D’
After training
Same columns,but only one cell active per column.
IF 40 active columns, 10 cells per columnTHEN 1040 ways to represent the same input in different contexts
Cortical Learning Algorithm (CLA)aka Cellular Layer
Converts input to sparse representations in columnsLearns transitionsMakes predictions and detects anomalies
Applications1) High-order sequence inference L2/32) Sensory-motor inference L43) Motor sequence recall L5
Capabilities- On-line learning- High capacity- Simple local learning rules- Fault tolerant- No sensitive parameters
Basic building block of neocortex/machine intelligence
Anomaly Detection Using CLA
CLA
Encoder
SDR Prediction errorTime averageHistorical comparison
Metric + Time Anomaly
score
CLA
Encoder
SDR Prediction errorTime averageHistorical comparison
Metric + Time Anomaly
score
.
.
.
CloudWatch
AWS
Grok for AWS Users
- Automated model creation via web, CLI, API
- Breakthrough anomaly detection
- Dramatically reduces false positives/negatives
- Supports auto-scaling and custom metrics
CustomerInstances & Services
- Mobile client
- Instant status check
- Mobile OS and email alerts
- Drill down to determine severity
Grok for AWS Mobile UI
Sorted by anomaly score Continuously updated Continuously learning
What Can the CLA/Grok Detect?
Sudden changes
Slow changes Subtle changes in regular data
What Can the CLA/Grok Detect?
Patterns that humans can’t seeChanges in noisy data
CEPT.at - Natural Language Processing Using SDRs and CLA
Document corpus(e.g. Wikipedia)
128 x 128
100K “Word SDRs”
- =Apple Fruit Computer
MacintoshMicrosoftMacLinuxOperating system….
Sequences of Word SDRs
Training setfrog eats fliescow eats grainelephant eats leavesgoat eats grasswolf eats rabbitcat likes ballelephant likes watersheep eats grasscat eats salmonwolf eats micelion eats cowdog likes sleepelephant likes watercat likes ballcoyote eats rodentcoyote eats rabbitwolf eats squirreldog likes sleepcat likes ball---- ---- -----
Word 3Word 2Word 1
Sequences of Word SDRs
Training set
eats“fox”
?
frog eats fliescow eats grainelephant eats leavesgoat eats grasswolf eats rabbitcat likes ballelephant likes watersheep eats grasscat eats salmonwolf eats micelion eats cowdog likes sleepelephant likes watercat likes ballcoyote eats rodentcoyote eats rabbitwolf eats squirreldog likes sleepcat likes ball---- ---- -----
Sequences of Word SDRs
Training set
eats“fox”
rodent
1)Word SDRs created without supervision
2)Semantic generalizationSDR: lexicalCLA: grammatic
3)Commercial applicationsSentiment analysisAbstractionImproved text to speechDialog, Reporting, etc.www.Cept.at
frog eats fliescow eats grainelephant eats leavesgoat eats grasswolf eats rabbitcat likes ballelephant likes watersheep eats grasscat eats salmonwolf eats micelion eats cowdog likes sleepelephant likes watercat likes ballcoyote eats rodentcoyote eats rabbitwolf eats squirreldog likes sleepcat likes ball---- ---- -----
Cept and Grok use exact same code base
eats“fox”
rodent
Source code for:- Cortical Learning Algorithm- Encoders- Support libraries
Single source tree (used by GROK), GPLv3
Active and growing community
Hackathons
Education Resources
www.Numenta.org
NuPIC Open Source Project
Research- Implement and test L4 sensory/motor inference- Introduce hierarchy (?)- Publish
NuPIC- Grow open source community- Support partners, e.g. IBM, CEPT
Grok- Create commercial value for CLA
Attract resourcesProvide a “target” and market for HW
- Explore new application areas
Goals For 2014
1) The neocortex is as close to a universal learning machine as we can imagine
2) Machine intelligence will be built on the principles of the neocortex
3) HTM is an overall theory of neocortex
4) CLA is a building block
5) Near term applicationsanomaly detection, prediction, NLP
6) Participate www.numenta.org
Future of Machine Intelligence
Future of Machine Intelligence
Definite- Faster, Bigger- Super senses- Fluid robotics- Distributed
hierarchyMaybe- Humanoid robots- Computer/Brain
interfaces for all
Not- Uploaded brains- Evil robots
Why Create Intelligent Machines?
Thank You
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