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

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1 1 Machine Learning & Artificial Intelligence: Transforming Defense, Intelligence, & Security Dr. Denis Garagić Chief Scientist Machine Learning September 27, 2017 Credit: agsandrew / Fotolia

2 2 Advances in AI and Autonomy Lead to New Era of Human-Machine Collaboration and Combat Teaming Human-centric Injection of AI to Make Machines Smart Enough to Help Their Human Users Efficient Learning Machines 1 st 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 Credit: aroma360.com

3 3 How Do We Do It? 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

4 4 Solving Tomorrow s Defense, Intelligence, & Security Problems Requires Fundamental Machine Learning Theory Development Using New Paradigms

5 5 Activity-Based Intelligence Answers Difficult Analytic Questions Track Data Normalcy Learning Behavior Prediction Real-Time Performance Analysis, Anomaly Detection, & Behavior Prediction Anomaly Detection Autonomy & Resilience for Uninhabited Vehicle Perception & Control Adaptive Cyber Security & Network Intrusion Detections Detection, Recognition, & Response

6 6 Classifying Activity of Humans from Noisy, Sparse Radar Observations Becomes Possible Small Boats Humans Patrolling Animals Grazing Finding Slow Moving Targets Under Foliage is Hard, Monitoring Their Behavior is Many-fold More Challenging Human and Pack Animals 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

7 7 Probabilistic Inference Automatically Aligns Geospatial Vehicle Motion Data Heatmap of Raw MOVINT Overlaid on Google Earth MOVINT Heatmaps (Sub-regions) Aligned Results Raw MOVINT Accuracy Insufficient for Location- Based Analytics Requiring High Precision / Recall Geospatial Queries Strip Mall (Starbucks, gym, etc.) McDonald s Process Process Uncover Parking Lot Activity Hidden in Raw Data Access Points & Drive-Thru Lane Emerge from Noisy Data

8 8 Adaptive Generative Machine Learning Enables Applications Requiring Both Proactive (Online) & Forensic (or Batch) Data Analysis Data 2 GPixels Reasoning by Bayesian Machine Learning Intelligence 100K Tracks Registered Data Target Objects Classified Targets Tracks Preprocessing Segmentation Classification Tracking

9 9 Adaptive Reasoning Recognizes Known & Unknown Objects from Multi-Modality Data 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

10 10 Combination of Probabilistic & Deep Learning Approaches Performs Actionable Intelligence Discovery & Exploitation from Multi-Sensor Streaming Data 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

11 11 Closed-Loop Rapid Learning and Reaction Capabilities Intelligently Counter Threats Radar / Radio Threats Detection & Characterization Response Optimization Rapidly Characterize Emerging Agile Threats Radars, Radio Communication Devices, Interference (Jammers) Effectiveness Assessment Synthesize & Optimize Electronic Countermeasures Assess Response Effectiveness

12 Run Length (Trading Days) 12 Machine Learning Predictions Make Otherwise Unobservable Aspects of Economic Threats to National Security Observable MEND rebels attack an oil barge & seize 9 hostages Militants kidnap 4 foreign workers Norwegian rig offshore Nigeria attacked & 16 crew members kidnapped Baker Hughes executive killed MEND attack on 4/20 Examine Causes for Predicted Changes Missing Ground Truth (Reported News) Significant Historical Events (Nigeria 2006) Companies Start Exit Nigeria Due to Delta Kidnapping Chaos Ground Truth News Media Reports Median of Run Length Predictive Distribution 1) MEND killed 10 Nigerian soldiers 2) MEND abducted 7 foreign workers 3) Nigerian soldiers attacked militant camp, in ensuing battle 9 Nigerian soldiers killed MEND Set Off 2 Bombs in Southern Nigeria Jan Apr Jul Oct Jan

13 13 Non-Acoustic Speech Communication Possible via Mouthed-Speech Understanding & Transcription + - Covert / Discreet Communication in Dangerous Environments Communication in Super-Noisy Environments Communication for Speech-Disordered Individuals Intended to be:

14 14 Defense, Intelligence, & Security Present Broad Range of Challenges for Machine Learning & AI Too Much Data Too Little Data Massive Data Centers Extremely Low Size Weight & Power Devices Exquisite Data Noisy Late Data Minutes / Hours / Days for Human Decisions Sub-Second Decisions by Machines Intended to be:

15 15 Thank you BAE Systems Technology Solutions Dr. Denis Garagić Burlington, MA, USA

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