1st Edition

Democratization of Artificial Intelligence for the Future of Humanity

By Chandrasekar Vuppalapati Copyright 2021
    388 Pages 245 B/W Illustrations
    by CRC Press

    388 Pages 245 B/W Illustrations
    by CRC Press

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    Artificial intelligence (AI) stands out as a transformational technology of the digital age. Its practical applications are growing very rapidly. One of the chief reasons AI applications are attaining prominence, is in its design to learn continuously, from real-world use and experience, and its capability to improve its performance. It is no wonder that the applications of AI span from complex high-technology equipment manufacturing to personalized exclusive recommendations to end-users. Many deployments of AI software, given its continuous learning need, require computation platforms that are resource intense, and have sustained connectivity and perpetual power through central electrical grid.

    In order to harvest the benefits of AI revolution to all of humanity, traditional AI software development paradigms must be upgraded to function effectively in environments that have resource constraints, small form factor computational devices with limited power, devices with intermittent or no connectivity and/or powered by non-perpetual source or battery power.

    The aim this book is to prepare current and future software engineering teams with the skills and tools to fully utilize AI capabilities in resource-constrained devices. The book introduces essential AI concepts from the perspectives of full-scale software development with emphasis on creating niche Blue Ocean small form factored computational environment products.

    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

    Biography

    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.