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© 2017 BAE SystemsApproved for Pubic Release; Distribution unlimited

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Machine Learning & Artificial Intelligence: Transforming Defense, Intelligence, & Security

Dr. Denis GaragićChief Scientist – Machine LearningSeptember 27, 2017

Credit: agsandrew / Fotolia


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• Human-centric Injection of AI to Make Machines Smart Enough to Help Their Human Users

• Efficient Learning Machines – 1st of 5 Third Offset Pillars

• Find Patterns

• Represent Domains & Problems

• React When Human Reaction Time Too Slow

• Multiple Communities of Interest Have AI Requirements: Advanced Electronics; Autonomy; Cyber; Electronic Warfare (EW); Sensors; Command, Control, Communications, Computers, and Intelligence (C4I); Space

Advances in AI and Autonomy Lead to New Era of Human-Machine Collaboration and Combat Teaming

Credit: aroma360.com


• Patterns Are Ubiquitous, Yet Often Obscure or Hidden –Finding & Exploiting Them Yields Critical Advantages in Many Situations

• Cognitive & Adaptive Systems Provide Such Advantages

• Incremental, Multi-modality, Multi-Domain Data-Driven Learning Approaches Are Required

• Continuous Learning During Execution Applies Previous Knowledge to Novel Situations

How Do We Do It?

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Solving Tomorrow’s Defense, Intelligence, & Security Problems Requires Fundamental Machine Learning Theory Development Using New Paradigms


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• Real-Time Performance Analysis, Anomaly Detection, & Behavior Prediction

• Autonomy & Resilience for Uninhabited Vehicle Perception & Control

• Adaptive Cyber Security & Network Intrusion Detections Detection, Recognition, & Response

Activity-Based Intelligence AnswersDifficult Analytic Questions

Normalcy Learning

Behavior Prediction

Anomaly Detection

Track Data


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• Finding Slow Moving Targets Under Foliage is Hard, Monitoring Their Behavior is Many-fold More Challenging

• Consistent Observation of Individual Movers – Human or Otherwise – is Not Realistic

• Accurate Estimation of Type & Intent of Current Activity Behavior of Moving Groups Supports Additional Objectives

Classifying Activity of Humans from Noisy, Sparse Radar Observations Becomes Possible

Human and Pack Animals

Small Boats

Humans Patrolling

Animals Grazing


Probabilistic Inference Automatically Aligns Geospatial Vehicle Motion Data

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Process

Process

Heatmap of Raw MOVINT Overlaid on Google Earth

Aligned Results

Uncover Parking Lot

Activity Hidden in Raw Data

Access Points &

Drive-Thru Lane

Emerge from Noisy

Data

MOVINT Heatmaps(Sub-regions)

Strip Mall (Starbucks, gym, etc.)

McDonald’s

Raw MOVINT Accuracy Insufficient for Location-Based Analytics Requiring

High Precision / Recall Geospatial Queries


Data

2 GPixels

Intelligence

100K TracksReasoning by Bayesian Machine Learning

Classification TrackingPreprocessing Segmentation

Registered

Data

Target

Objects

Classified

TargetsTracks

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Adaptive Generative Machine Learning Enables Applications Requiring Both Proactive (Online) & Forensic (or Batch) Data Analysis


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• Learns / Adapts Under Uncertainty from Limited Multi-Modality & Multi-View Observations

• Learns Compact Representations of Objects of Interest Online

• Performs Accurate Object Recognition Within Small Onboard Size, Weight, & Power (SWaP) Envelope

Adaptive Reasoning Recognizes Known & Unknown Objects from Multi-Modality Data


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• Automated Generation of Relevant Reports from Time-Varying Multi-Modality (e.g., Visual, Radar, Signals) Scenes

• Integration of Learning & Language Understanding Reasons Over Activities (e.g., Space & Time Relationships; Object Interactions; Other Contextual Information) in Scenes

• Multi-Object Detection & Tracking, Encoder & Decoder Modules to Process Imagery & Generate Descriptions

Combination of Probabilistic & Deep Learning Approaches Performs Actionable Intelligence Discovery & Exploitation from Multi-Sensor Streaming Data


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• Rapidly Characterize Emerging Agile Threats – Radars, Radio Communication Devices, Interference (Jammers)

• Synthesize & Optimize Electronic Countermeasures

• Assess Response Effectiveness

Closed-Loop Rapid Learning and Reaction Capabilities Intelligently Counter Threats

Detection & Characterization

Response Optimization

Effectiveness Assessment

Radar / Radio Threats


Machine Learning Predictions Make Otherwise Unobservable Aspectsof Economic Threats to National Security Observable

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Ground TruthNews Media Reports

Militants kidnap 4 foreign workers

MEND rebels attack an oil barge & seize 9

hostages Baker Hughes’ executive killed

Norwegian rig offshore Nigeria attacked & 16 crew members kidnapped

Companies Start Exit Nigeria Due to Delta

Kidnapping Chaos

1) MEND killed 10 Nigerian soldiers2) MEND abducted 7 foreign workers3) Nigerian soldiers attacked militant camp, in ensuing battle 9 Nigerian soldiers killed

MEND Set Off 2 Bombs in Southern

Nigeria

Examine Causes for Predicted Changes –Missing Ground Truth (Reported News)

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Significant Historical Events (Nigeria 2006) Median of Run Length Predictive Distribution

MEND attack on 4/20

Jan Apr Jul JanOct

© 2017 BAE SystemsIntended to be: Approved for Pubic Release; Distribution unlimited

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• Covert / Discreet Communication in Dangerous Environments

• Communication in Super-Noisy Environments

• Communication for Speech-Disordered Individuals

Non-Acoustic Speech CommunicationPossible via Mouthed-Speech Understanding & Transcription

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© 2017 BAE SystemsIntended to be: Approved for Pubic Release; Distribution unlimited

Defense, Intelligence, & Security Present Broad Range of Challengesfor Machine Learning & AI

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Extremely Low Size Weight & Power Devices

Too Much Data Too Little Data

Massive Data Centers

Exquisite Data Noisy Late Data

Minutes / Hours / Days for Human Decisions Sub-Second Decisions by Machines


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Thank you

BAE Systems – Technology Solutions

Dr. Denis Garagić

Burlington, MA, USA

[email protected]

machine learning & artificial intelligence: transforming ... · artificial intelligence:...

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