DANIEL WILSON AND BEN CONKLIN

Integrating AI with Foundation Intelligence

for Actionable Intelligence

INTEGRATING AI WITH FOUNDATION INTELLIGENCE

FOR ACTIONABLE INTELLIGENCE

“…in an arms race for artificial intelligence”

- Dr. Anthony Vinci, NGA

Cloud ML

New Products

Azure MLCognitive ServicesAzure Bot Service

Watson ML ServiceDSXCognitive Computing

Amazon ML Cloud AICloud ML (TF)

LeonardoSAP Analytics Cloud

ML Server SPSSML for z/OS

-- --

LeonardoSAP Predictive Analytics

Office 365PowerPointOutlook..

Predictive MaintenanceTargeted Marketing..

Demand Forecasting,Recommendations, Search, Merchandising Placement, Fraud..

Search, Ads, Gmail, Translation, YouTube, Maps..

Fraud Mgmt, SAPHIRE, S/4HANA, Fieldglass, Total Workforce Insight

Cortana Assistant -- Alexa, Prime Air Delivery Drones, Grocery Stores

Google Assistant --

On-premise ML

Enhanced Products

MICROSOFT IBM AMAZON GOOGLE SAP

Neural Networks

TensorFlow

CNTK

Natural Language Processing

Cognitive Computing

GeoAI

Computer Vision

Dimensionality Reduction

Object Detection

Support Vector Machines

Object Tracking

Keras

Theano scikit-learn

T-SNE

Random Forest Machine Learning

Deep Learning

Artificial IntelligenceCaffe

Machine Learning

Deep Learning

Artificial Intelligence

1950’s

1960’s

1970’s

1980’s

1990’s

2000’s

2010’s

Machine Learning

Deep Learning

Artificial Intelligence

CNTK TensorFlowTheano

Natural Language Processing

Video game behavioral AI

Robotics

Keras

ConvolutionalNeural Networks

IBM Watson

scikit-learn

Computer Vision

Applications of Machine Learning

eCommerce Spam Filtering

Fraud Detection Transportation Management Facial Recognition

Cyber Intrusion Detection

Why Now?

3. Better Algorithms1. More Data 2. More Compute

ML ValuePrediction

Automation Anomaly Detection Root-cause Identification

GIS users have been doing machine learning

GIS

Classification

Clustering

Prediction

Integration of Machine Learning and Deep Learning with GIS

GIS

Amazing Rate of Improvement

Image Recognition

IMAGENETPedestrian Detection

CALTECHObject Detection

KITTI

Convolutional Neural Networks (CNNs)

Deep Learning

Real-Time

Land Cover Classification

Microsoft Cognitive Toolkit

Use Artificial Intelligence to find system faults

Predictive Maintenance

IBM Watson

Real-Time Object Recognition from Video

TensorFlow

GIS and Natural Language Processing Integration

“This is not eitherhuman analysis or artificial intelligence, it's got to be

some combination of the two.”

Adm. Mike Rogers, Director, National Security Agency & Commander of U.S. Cyber Command

Technology Drivers for Advanced Analytics

3. Better Algorithms1. More Data 2. More Compute

Categories of Machine Learning in GIS

GIS

Classification

Clustering

Prediction

Machine Learning Tools in GIS

• 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

• Spatially Constrained Multivariate Clustering

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

Classification Prediction

Using the known to estimate the unknown

Use Case: Accurately predict impacts of climate change on local temperature using

global climate model data

Prediction

In ArcGIS: Empirical Bayesian Kriging, Areal Interpolation, EBK Regression Prediction, Ordinary Least Squares

Regression and Exploratory Regression, Geographically Weighted Regression

The grouping of observations based on similarities of values or locations

Use Case: Given the nearly 50,000 reports of traffic between 5pm and 6pm in Los Angeles (from

Traffic Alerts by Waze), where are traffic zones that can be used to elicit feedback from current

drivers in the area?

Clustering

In ArcGIS: Spatially Constrained Multivariate Clustering, Multivariate Clustering, Density-based Clustering, Image Segmentation,

Hot Spot Analysis, Cluster and Outlier Analysis, Space Time Pattern Mining

The process of deciding to which category an object should be assigned based on a training dataset

Use Case: Classify impervious surfaces to help effectively prepare for storm and flood

events based on the latest high-resolution imagery

Classification

In ArcGIS: Maximum Likelihood Classification, Random Trees, Support Vector Machine

Integration of Machine Learning and Deep Learning

with GIS

GIS

Demo: Transfer Learning

Enterprise Approach to Machine LearningLeveraging Geography for Improved Understanding

Distributed Analytics

VectorRasterReal-Time

Analyst Tools

ModelingData Conditioning and

Management

Visualization andExploration

Vector

UnstructuredText

Imagery

Data Lakes

FoundationData

Point Clouds

Sensor Feeds

Data ConditioningExtract, Transform, Enrich, Georeference, Validate

Making Data Ready for AnalysisAnd Use in Apps

Visualization and ExplorationExploratory

Data Analysis

Visualization

Modeling and Spatial AnalyticsDevelop and Capture new tradecraft

Python

Data ScienceSpatio-TemporalModeling

Analytic Services

Machine Learning& Artificial Intelligence

Real-Time Analytics Workflow

Real-Time

Analytics

Situational

Awareness

Analysis Alerting

Big Data

Archive

Visualization

Collection

Contextualization

• GeoFencing

• Aggregation

• Detection

• Filter

Raster Analytics Workflow

Dynamic Image

ProcessingCollection

Visualization

Change

Detection

Feature

Extraction

• Ortho-on-the-fly

• Classification

• Feature Extraction

• Mosaicking

Vector Analytics Workflow

Classification, Clustering, Prediction

Big Data

Archive

Pattern-

of-Life

Link

Analysis

Trend

Analysis

• Space-Time

• Hot Spots

• Density

• Proximity

Big Data

Analytics

Predictive

Analytics

Demo: Event Prediction

Applying to Intelligence Problems

Research Search

Monitor Discover

Known

Unknown

Kn

ow

n

Un

kno

wn

Locations and Targets

Be

hav

iors

an

d S

ign

atu

res

The Intelligence Cycle

Requirements

Tasking

Collection

Processing

Exploitation

Dissemination

Modern Emphasis Traditional Emphasis

Leveraging Foundation Intelligence

Cultural Data

Landscape Data

Social Data

Observations

Observe

Orient

Decide

Act

Actionable Intelligence

OODA loopSpeed is the key

Speed is relative

Cert

ain

ty

0%

100%

Time & Resources0

Data

Intelligence

X

Cert

ain

ty

0%

100%

Time & Resources0

Data

X

Intelligence

Reduce Time to Action

Impacts on Entire Organization

Implementing Analytic Platform

Plan for

Evolving Structure

Collaborate with

Industry

Prepare Infrastructure

Enhance Analyst

Tradecraft

Focus on

Verification and

Validation