Building Intelligent MachinesChetan Surpur

MVTSA Technology Symposium – April 24, 2015

Agenda

• Machine Intelligence• The human neocortex• Research and applications• Future research

Agenda

• Machine Intelligence• The human neocortex• Research and applications• Future research

Agenda

• Machine Intelligence• The human neocortex• Research and applications• Future research

What Does the Neocortex Do?

Data streamretina

cochlea

somatic

The neocortex learns a model of the world, primarily through behavior.

Sensory arrays

Motor stream

The model is time-based and predictive.

• Uniform, heterogeneous• Plasticity in learning• Different regions are merely connected to different inputs and outputs• Hierarchical, like the world it models• Single learning algorithm

Cortical Theory

Hierarchy

Cellular layers

Mini-columns

Neurons: 5-10K synapses

Active dendritesLearning = new synapses

Remarkably uniform - anatomically - functionally

Sheet of cellsHTMHierarchical Temporal Memory

1) Hierarchy of identical regions2) Each region learns sequences3) Stability increases going up

hierarchy if input is predictable4) Sequences unfold going down

Questions

- What does a region do?- What do the cellular layers do?- How do neurons implement this?- How does this work in hierarchy?

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Agenda

• Machine Intelligence• The human neocortex• Research and applications• Future research

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Numenta Research Roadmap

Sensory-motor Sequences

High-order Sensory Sequences

Motor Sequences

Attention/Feedback

Streaming Data Applications

Capabilities: PredictionAnomaly detectionClassification

Extensively tested 2011-2013Commercial

Tested 2014

In research 2015

HTM

SDRs PredictionsAnomaliesClassification

Streaming Data Applications

Data stream

NumbersCategoriesDateTimeGPSWords

ApplicationsServersBiometricsMedicalVehiclesIndustrial equipmentSocial mediaComm. networks

Encoder

Turns data into Sparse Distributed

Representations(SDRs)

Grok: Anomaly Detection For Amazon Web Services

Unique value of HTM algorithms Automated model creation: configure hundreds of models in

minutes Continuously learning: automatically adapts to changes Detects sophisticated temporal anomalies

Anomaly in Geospatial Tracking Data (demo)

CLA

Encoder

SDRsPredictionAnomaly DetectionClassification

GPS+ Velocity

Encoder turns GPS data into SDRsWorks anywhere on Earth or in space

Anomaly in Geospatial Tracking Data (demo)

Direction anomaly

Learning a route

Speed anomalyPosition anomaly

Stock priceStock volumeTwitter volume

Companies sorted by unusual activity

Tweets reveal cause

Company monitorFull application mid 2015, free, open source

Document corpus(e.g. Wikipedia)

128 x 128

“Word SDRs”

- =

Apple Fruit Computer

MacintoshMicrosoftMacUnixOperating system….

Natural Language +

Training set

eats“fox”

rodent

1) Unsupervised Learning

2) Semantic Generalization

3) Many Applications

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

HTM

Server metric anomalies

Geospatial tracking

Natural languagesearch/prediction

Company monitor

These HTM Applications Use Exact Same Code Base

Agenda

• Machine Intelligence• The human neocortex• Research and applications• Future research

2/3

4

5

6

Numenta Research Roadmap

Sensory-motor Sequences

High-order Sensory Sequences

Motor Sequences

Attention/Feedback

Extensively tested 2011-2013Commercial

Tested 2014

In research 2015

NuPIC open source project www.Numenta.org

Research Transparency

- Algorithms are documented

- Multiple independent implementations

- Numenta’s software is open source (GPLv3)

- Numenta’s daily research code is online

- Active discussion groups for theory and implementation

Thank you!Questions?

Backup Slides

Sensory-motor sequences

Sensory-motor sequences

Sensory-motor sequences

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Goal Oriented Behavior via Feedback

Copy of motor commands

Sensor/afferent data

simple cells

complex cells

Stable feedback invokes union of sparse states in multiple sequences. (“Goal”)

Feedforward input selects one state and plays back motor sequence from there.

Sub-corticalmotor

simple cells

complex cells

Stable/Invariant patternfrom higher region

Higherregion

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