DANIEL WILSON AND BEN CONKLIN. Integrating AI with Foundation Intelligence for Actionable Intelligence

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2 DANIEL WILSON AND BEN CONKLIN Integrating AI with Foundation Intelligence for Actionable Intelligence

3 INTEGRATING AI WITH FOUNDATION INTELLIGENCE FOR ACTIONABLE INTELLIGENCE

4 in an arms race for artificial intelligence - Dr. Anthony Vinci, NGA

5 MICROSOFT IBM AMAZON GOOGLE SAP Cloud ML Azure ML Cognitive Services Azure Bot Service Watson ML Service DSX Cognitive Computing Amazon ML Cloud AI Cloud ML (TF) Leonardo SAP Analytics Cloud On-premise ML ML Server SPSS ML for z/os Leonardo SAP Predictive Analytics Enhanced Products Office 365 PowerPoint Outlook.. Predictive Maintenance Targeted Marketing.. Demand Forecasting, Recommendations, Search, Merchandising Placement, Fraud.. Search, Ads, Gmail, Translation, YouTube, Maps.. Fraud Mgmt, SAPHIRE, S/4HANA, Fieldglass, Total Workforce Insight New Products Cortana Assistant -- Alexa, Prime Air Delivery Drones, Grocery Stores Google Assistant --

6 Caffe Theano Object Tracking CNTK Artificial Intelligence Object Detection scikit-learn Computer Vision Cognitive Computing Random Forest GeoAI TensorFlow Machine Learning Deep Learning Neural Networks T-SNE Natural Language Processing Keras Dimensionality Reduction Support Vector Machines

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9 1950 s 1960 s Artificial Intelligence 1970 s 1980 s 1990 s Machine Learning 2000 s 2010 s Deep Learning

10 Artificial Intelligence Video game behavioral AI Natural Language Processing Computer Vision Theano IBM Watson Machine Learning CNTK Deep Learning Convolutional Neural Networks Keras TensorFlow scikit-learn Robotics

11 Applications of Machine Learning ecommerce Cyber Intrusion Detection Spam Filtering Fraud Detection Transportation Management Facial Recognition

12 Why Now? 1. More Data 2. More Compute 3. Better Algorithms

13 ML Value Prediction Automation Anomaly Detection Root-cause Identification

14 GIS users have been doing machine learning Classification Prediction Clustering GIS

15 Integration of Machine Learning and Deep Learning with GIS GIS

16 Deep Learning Amazing Rate of Improvement Image Recognition IMAGENET Pedestrian Detection CALTECH Object Detection KITTI Convolutional Neural Networks (CNNs)

17 Real-Time Land Cover Classification Microsoft Cognitive Toolkit

18 Use Artificial Intelligence to find system faults Predictive Maintenance IBM Watson

19 Real-Time Object Recognition from Video TensorFlow

20 GIS and Natural Language Processing Integration

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22 This is not either human 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

23 Technology Drivers for Advanced Analytics 1. More Data 2. More Compute 3. Better Algorithms

24 Categories of Machine Learning in GIS Classification Prediction Clustering GIS

25 Machine Learning Tools in GIS Classification Maximum Likelihood Classification Random Trees Support Vector Machine Clustering Spatially Constrained Multivariate Clustering Multivariate Clustering Density-based Clustering Image Segmentation Hot Spot Analysis Cluster and Outlier Analysis Space Time Pattern Mining Prediction Empirical Bayesian Kriging Areal Interpolation EBK Regression Prediction Ordinary Least Squares Regression and Exploratory Regression Geographically Weighted Regression

26 Prediction Using the known to estimate the unknown Use Case: Accurately predict impacts of climate change on local temperature using global climate model data In ArcGIS: Empirical Bayesian Kriging, Areal Interpolation, EBK Regression Prediction, Ordinary Least Squares Regression and Exploratory Regression, Geographically Weighted Regression

27 Clustering 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? In ArcGIS: Spatially Constrained Multivariate Clustering, Multivariate Clustering, Density-based Clustering, Image Segmentation, Hot Spot Analysis, Cluster and Outlier Analysis, Space Time Pattern Mining

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

29 Integration of Machine Learning and Deep Learning with GIS GIS

30 Demo: Transfer Learning

31 Enterprise Approach to Machine Learning Leveraging Geography for Improved Understanding Analyst Tools Data Conditioning and Management Visualization and Exploration Modeling Real-Time Raster Distributed Analytics Vector

32 Data Conditioning Extract, Transform, Enrich, Georeference, Validate Making Data Ready for Analysis And Use in Apps Imagery Point Clouds Unstructured Text Vector Foundation Data Sensor Feeds Data Lakes

33 Visualization and Exploration Exploratory Data Analysis Visualization

34 Modeling and Spatial Analytics Develop and Capture new tradecraft Spatio-Temporal Modeling Data Science Analytic Services Python Machine Learning & Artificial Intelligence

35 Real-Time Analytics Workflow Situational Awareness Analysis Alerting Visualization Contextualization Collection Real-Time Analytics GeoFencing Aggregation Detection Filter Big Data Archive

36 Raster Analytics Workflow Change Detection Feature Extraction Visualization Collection Dynamic Image Processing Ortho-on-the-fly Classification Feature Extraction Mosaicking

37 Vector Analytics Workflow Classification, Clustering, Prediction Link Analysis Predictive Analytics Patternof-Life Trend Analysis Big Data Analytics Space-Time Hot Spots Density Proximity Big Data Archive

38 Demo: Event Prediction

39 Behaviors and Signatures Known Unknown Applying to Intelligence Problems Locations and Targets Known Research Search Monitor Discover Unknown

40 The Intelligence Cycle Requirements Dissemination Tasking Modern Emphasis Traditional Emphasis Exploitation Collection Processing

41 Leveraging Foundation Intelligence Observations Social Data Landscape Data Cultural Data

42 Actionable Intelligence Observe Speed is the key Speed is relative Act OODA loop Orient Decide

43 Certainty 100% Intelligence X 0% 0 Data Time & Resources

44 Certainty Reduce Time to Action 100% Intelligence X 0% 0 Data Time & Resources

45 Implementing Analytic Platform Impacts on Entire Organization Plan for Evolving Structure Prepare Infrastructure Collaborate with Industry Enhance Analyst Tradecraft Focus on Verification and Validation