Democratization of Artificial Intelligence for the Future of Humanity

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

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.