Bridging the AI Gap for Intelligence AnalystsBen Conklin

Matt Woodlief

James Jones

Intelligence Imperative for AI

Our SecurityIs Increasingly Challenged

Requiring Multi-Jurisdiction, Multi-AgencyAnd Global Operations

Domestic Terrorism

New Adversaries

Organized Crime

Natural DisastersEnergy Security

Refugee CrisisCyber Warfare

Political CrisisBorder Security

Non-State Actors

First Age 1944-1962

• Rise of communism

• Human intelligence

• Single threat

Second Age1962-2001

• Cold war; nation-states

• Technical collection

• Single threat

Third Age2001-2014

• Terrorism & regional conflict

• Info tech, UAV’s and web tools

• Threats on the homeland

Fourth Age2014+

• Terror, cyber and WMD

• Multi-int & big data analytics – with AI

• Diverse distributed groups

First professional intelligence Integrated intelligenceDistributed intelligenceStovepipe intelligence

Increasing Change and Rates of Change

The Intelligence Cycle

Requirements

Tasking

Collection

Processing

Exploitation

Dissemination

Traditional EmphasisNew Emphasis

Fourth Age

Digital Transformation

Digital Automation

Simultaneous

Sequential Workflows

Realizing the 4th Age of Intelligence…….Requires Intelligence Organizations to transform

Integrated Operations

Digital Transformation of IntelligenceOrganizations Need to Re-envision Their Workflows

Creating Smart, Dynamic Decision Making

“Collaboration happens at the speed of trust”

Massive Transformation . . .Interconnected Information, Processes, and Workflows . . .

. . . All Happening at the Same Time

…Using Analytics to Drive Decision Making

Location Intelligence Helps Us Understand . . .

. . . Everything

Simultaneous, CollaborativeTrusted

Drivers for ChangeLiving in an Era of Constant Change Integrated

Operations

Information

Technology

Tradecraft

Advancing the art and science to address growing threats

Digital Transformation

Simultaneous, CollaborativeTrusted

Drivers for ChangeLiving in an Era of Constant Change Integrated

Operations

Information

Technology

Tradecraft

Advancing the art and science to address growing threats

Digital Transformation

Imagery

Unstructured

Weather

Demographics

3DStructured

Observations

Foundation Intelligence

PsychographicsFull-Motion Video

Crowdsourcing

IoTReal-Time

Persistent Surveillance

Dark Web

SaaS

Dev Ops

MicroservicesWeb Services

Cloud

Big DataAI

Machine Learning

Distributed Computing

Containerization

Deep LearningVirtualization Distributed Analysis

ABI Data ExplorationStructured

Observations Immersive VisualizationMulti-Int

Predictive Modeling

Analyst Augmentation

Monitor Research

SearchDiscover

ABI

Shift to Discovery Focus

Known

Unknown

Known

Unknown

Locations and Targets

Be

hav

iors

an

d S

ign

atu

res

Fourth Age

Activity-Based Intelligence & Structured Observation ManagementPersist and Repeat Intelligence In A Time-Dominant Environment

Activity-Based Intelligence

Structured ObservationManagement

Discovery of Objects and Relationships

IdentifiesKnowledge Gaps

SOM:• Organizing Model for Known

Information• Monitoring, warning, and reporting

ABI:• Set of methods for discovering

the unknown• Discovery and understanding• Develops new objects

SOM Database

• ABI to explore unknowns

• SOM for capturing known

Simultaneous, CollaborativeTrusted

Drivers for ChangeLiving in an Era of Constant Change Integrated

Operations

Information

Technology

Tradecraft

Advancing the art and science to address growing threats

Digital Transformation

Imagery

Unstructured

Weather

Demographics

3DStructured

Observations

Foundation Intelligence

Full-Motion Video

IoTReal-Time

Digital Twin

Dark Web

SaaS

Dev Ops

MicroservicesWeb Services

Cloud

Big DataAI

Machine Learning

Distributed Computing

Containerization

Deep LearningVirtualization Distributed Analysis

ABI Data Exploration

Analytics

Location Intelligence

Structured Observations Immersive VisualizationMulti-Int

Predictive Modeling

Analyst Augmentation

Digital Twin

Space and Time

Context and Content

Decision Making

Structured Observation Management (SOM)

Human Observations

using Smart Forms

Trained AI Classifiers

Common Ontology

Real-Time Services

TransformingFoundation Intelligence

• Product Centric

• Monolithic

• Out-of-Date

Traditional

• User Defined and Composable

• Decision Ready

• Real-Time

Modern

Authoritative and Trusted

Simultaneous, CollaborativeTrusted

Drivers for ChangeLiving in an Era of Constant Change Integrated

Operations

Information

Technology

Tradecraft

Advancing the art and science to address growing threats

Digital Transformation

Imagery

Unstructured

Weather

Demographics

3DStructured

Observations

Foundation Intelligence

PsychographicsFull-Motion Video

Crowdsourcing

IoTReal-Time

Persistent Surveillance

Dark Web

Distributed Analysis

ABI Data Exploration

Analytics

Location Intelligence

Structured Observations Immersive VisualizationMulti-Int

Predictive Modeling

Analyst Augmentation

SaaS

Dev Ops

Web Services

Cloud

Big DataAI

Machine Learning

Distributed Computing

Deep Learning

SOCOMSTRATCOM

Edge Portal

PACOM

Army GPC

AF DGS

USMC

DCGS

AGO

NATO SHAPE

EUCOM

CENTCOM

Connecting Organizations and Individuals

A Geospatial Cloud is Emerging

Providing Critical Spatial Infrastructure

Geospatial Cloud

IC GIS Portal

Computer Vision

Natural Language Processing

Prediction

Anomaly Detection

Human Machine Team

Target Identification & Tracking

AI, ML, and Deep Learning

SOM

Empirical Bayesian Kriging Regression Prediction

Training Data Preparation

Spatial Machine Learning

ArcGISPythonNotebooks

AI, ML, and Deep Learning

Anomaly Detection

Object Detection

Change Detection

ArcGIS has Machine Learning Tools

ArcGIS

Classification

Clustering

Prediction

Machine Learning in ArcGIS

Machine Learning Tools in ArcGIS

• Maximum Likelihood Classification

• Random Trees• Support Vector Machine

Clustering

• Empirical Bayesian Kriging• Areal Interpolation• EBK Regression Prediction• Ordinary Least Squares

Regression and Exploratory Regression

• Geographically Weighted Regression

• Forest Based Prediction• Spatially Constrained Multivariate Clustering

• Multivariate Clustering• Density-based Clustering• Image Segmentation• Hot Spot Analysis• Cluster and Outlier Analysis• Space Time Pattern Mining

Classification Prediction

Prediction and Anomaly Detection

Crash Prediction: Predicting Probability of

Crash per Time and Location

Fast ETA Prediction: Predicting ETA from Thousands

and Millions of points to Locations in Seconds

Similarly,

we can

Predict

Probability

of Attacks

per Location

Optimize Taskforce Deployment, Understand

Reachability & Explore Hundreds of Scenarios very

Quickly

Target Identification

Helping Us Understand, Predict, and Make Decisions . . .. . . at Many Scales

SensorIntegration

HumanMovement

Land Cover

AI and Computer VisionProvide Real-Time Global Intelligence from Imagery

AI and Computer Vision

Target

Identification

& Tracking

Anomaly

Detection

Surface to Air

Missile

Launcher Sites

Detection

SOM:

Activity

Identification

via Car

Counting per

Area & Time

Hidden Road

Detection

High Resolution Change

Detection

Detecting New Piers

Bandar Abbas, IR

QueuingDashboards identify EEI Patterns to Drive Action

Task Sensors InterdictionReadiness Threat

TippingAI Computer Vision Detects Targets

AI Models Observations SOMAI Inference

Fleet in Port

Drydock Repairs

SubsLoadingMunitions

Mini-SubInactivity

SortingEEI are filtered from SOM

SOM EEI

Buffer Proximity GeoFence

GeoEventServer

Range Intersect Time Gap

SpatiotemporalFilters

Integrating AI into Intelligence ProductionSOM empowers AI to find Essential Elements of Information (EEI) for Predictive Analysis

Object Detection

End to End GeoAI Life Cyclewith Imagery

ImageryAccess

Imagery Prep

DataLabelling

ConsumeModels

Run Inference at SCALE

Feedback Loop

TakeAction

Deploy Models to Production

NLP With Location IntelligenceAutomatic Location Extraction from Textual Content

80%Of Data is Unstructured

GIS and Natural Language Processing Integration

NLPTools

LocationContext

Text & Voice

SyntaxDiscourse

Semantics Speech

CoordinatesCustom Locations

User defined keywords

LocationsPeople/Organizations

EventsDates

Relationships

ArcGIS Pro 2.3

Native EsriCapability

Third PartyIntegration

Natural Language Processing

What are you looking for?

What is the best tool?

How is it best used?

• Data is at least somewhat understood• Data is static• Data contains identifiable and

repeating patterns• Little to no programming experience

available/needed

• Data is not well understood• Data is either static or streaming• Data does not contain identifiable

and/or repeating patterns• Programming experience needed

Integrating Unstructured Functionality with ArcGIS

LocateXT

• Extension for ArcGIS Desktop and Enterprise

• Uses regular expressions to search for coordinates in a variety of formats

• Ability to create/use custom locators/gazatteers

• Define custom keywords and stop words for document scraping

Using LocateXT to ETL Data

NLP Integration

• Numerous 3rd Party tools exist

- Open Source

- Proprietary / As A Service

• Identify and extract named entities

• Link entities and create semantic relationships

• Organizes data into an ontology

• Classify sentiment, topic identification, noun-phrase/verb extraction

Apps

DesktopAPIs

NLTK

ArcGIS NLP Tools

Entities and Relationships

Entities (spatial)Saudi Arabia285 Fulton St, New York,NY 1000734 10 9.51N 73 14 32.78EHadhramaut, Yemenapproximately 5 miles northwest of Baqubah

Entities (non-spatial)Osama bin LadenTerroristUS EmbassyUS Special ForcesAugust 20, 199866 cruise missiles

LinksOsama Bin laden -- Saudi Arabia (birthplace)US Embassy – Kenya

EventsOsama bin Laden attacked World Trade CenterAbu Musab al-Zarqawi was killed June 7, 2006

NLP and ArcGIS

AI Workforce

Senior Leaders

• What AI Can Do

• Organization AI Strategies

• Resource Allocation

Managers

• Directing Projects

• Resource Allocation

• Progress Tracking

• Project Delivery

• AI Product Managers

Technical Staff

• Knowledge of comprehensive AI

• Machine Learning engineers

• Data Engineers

• Data Scientists

Training, Workforce Development and Retention

Machine Learning Engineers

1. Responsible for building computational systems that can improve the performance of a task.

2. Research and implement appropriate ML algorithms and tools

3. Runs machine learning tests and experiments

4. Re-train models as necessary

5. Systems and solutions optimiztions

ArcGIS Support

• ArcGIS PRO + Image Analyst Extension AI/ML toolset

• ArcGIS Enterprise Raster Analytics

• ArcGIS Notebook Server

• ArcGIS Learn

• ArcGIS Big Data Toolkit

Data Engineers

• Take guidance from the Machine Learning Engineer

• Identification of best data sources for the problem set

• Staging and management of data for labeling, training and dessimination

• Implementation of ML solutions

• Interpretation of the results and presentation

• ArcGIS Support

- ArcGIS Enterprise – Image Server + Raster Analytics

- ArcGIS Dashboards

- ArcGIS PRO + Image Analyst Extension

Data Scientists

• Build Machine Learning Models

• Create / Develop Machine Learning Algorithms

• Iterate on models to create optimal performance

• Interact and integrate Machine Learning Frameworks

• ARCGIS Support

• ArcGIS PRO + Image Analyst Extension AI/ML toolset

• ArcGIS Enterprise Raster Analytics

• ArcGIS Notebook Server

• ArcGIS Learn

• ArcGIS Big Data Toolkit

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Bridging the AI Gap for Intelligence AnalystsBen Conklin

Matt Woodlief

James Jones