daniel wilson and ben conklin - esri · daniel wilson and ben conklin integrating ai with...
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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