Table of Contents

SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKS

Introduction

What is AI?

AI Epoch’s: Waves of Compute

AI Hype Cycle – Current and Emerging Technologies

AI - End-To-End (E2E) Process – Turning Data into Actionable Insights

Microsoft Azure - AI E2E Platform

AI Development Operations (DevOps) Loop for Data Science

AI –Performance and Computational Notations

AI for Greater Good – Solving Humanity and Societal Challenges

References

Standard Processes and Frameworks

Digital Transformation

Digital Feedback Loop

Insights Value Chain

The CRISP-DM Process

Building Blocks of AI - Major Components of AI

AI Reference Architectures

References

SECTION II - DATA SOURCES AND ENGINEERING TOOLS

Data – Call for Democratization

Call for Action

The Last Mile - Constrained Compute Devices AND "AI Chasm"

References

Machine Learning Frameworks and Device Engineering

Machine Learning Device Deployments

xRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) Framework

Circular Buffers

AI Democratization – "Crossing the Chasm"

References

Device Software and Hardware Engineering Tools

Software Engineering Tools

Hardware and Engineering Tools

Libraries

References

SECTION III - MODEL DEVELOPMENT AND DEPLOYMENT

Supervised Models

Decision Trees

XGBoost

Random Forrest

Naïve Bayesian

Linear Regression

Kalman Filter

References

Unsupervised Models

Hierarchical Clustering

K-Means Clustering

References

SECTION IV - DEMOCRATIZATION AND FUTURE OF AI

National Strategies

National Technology Strategies for Serving People

The United Nations AI Technology Strategy

The role of the UN

AI in the Hands of People

References

Future

Democratization of Artificial Intelligence for the Future of Humanity

Dedication

Acknowledgement

Preface

Appendix

Index

About the Author

Chandrasekar Vuppalapati graduated from San Jose State University Masters Program, specializing Software Engineering, and completed his Master of Business Administration from Santa Clara University, Santa Clara, California, USA. He is a Software IT Executive and Entrepreneur with diverse experience in Software Technologies, Enterprise Software Architectures, Cloud Computing, Data Analytics, Internet of Things (IoT), and Software Product & Program Management. Chandra has held engineering architectures and product leadership roles at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies, a Bell Laboratories Company. He teaches Software Engineering, Large Scale Analytics, Data Science, Mobile Technologies, Cloud Technologies, and Web & Data Mining for Masters program in San Jose State University. Chandra has also held market research, strategy and technology architecture advisory roles in Cisco Systems, Lam Research and performed Principal Investigator role for Valley School of Nursing where he connected Nursing Educators & Students with Virtual Reality technologies. He has authored several international conference papers and published book on Building Enterprise IoT Applications. Chandra has served as Chair in numerous technology and advanced computing conferences such as: IEEE Oxford, UK, IEEE Big Data Services 2017, San Francisco USA, Future of Information and Communication Conference 2018, Singapore and Intelligent Human Systems Integration (IHSI) 2020, Modena, Italy.