<P><STRONG>SECTION I -- INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKS</STRONG></P><P>Introduction</P><P>What is AI? </P><P>AI Epoch's: Waves of Compute</P><P>AI Hype Cycle -- Current and Emerging Technologies</P><P>AI -- End-To-End (E2E) Process -- Turning Data into Actionable Insights</P><P>Microsoft Azure -- AI E2E Platform</P><P>AI Development Operations (DevOps) Loop for Data Science</P><P>AI -Performance and Computational Notations</P><P>AI for Greater Good -- Solving Humanity and Societal Challenges</P><P>References</P><P></P><B><P>Standard Processes and Frameworks</P></B><P>Digital Transformation</P><P>Digital Feedback Loop</P><P>Insights Value Chain</P><P>The CRISP-DM Process</P><P>Building Blocks of AI -- Major Components of AI</P><P>AI Reference Architectures</P><P>References</P><B><P>SECTION II -- DATA SOURCES AND ENGINEERING TOOLS</P><P>Data -- Call for Democratization </P></B><P>Call for Action</P><P>The Last Mile -- Constrained Compute Devices AND "AI Chasm"</P><P>References</P><B><P></P><P>Machine Learning Frameworks and Device Engineering</P></B><P>Machine Learning Device Deployments</P><P>xRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) Framework</P><P>Circular Buffers</P><P>AI Democratization -- "Crossing the Chasm"</P><P>References</P><P></P><B><P>Device Software and Hardware Engineering Tools</P></B><P>Software Engineering Tools</P><P>Hardware and Engineering Tools</P><P>Libraries</P><P>References</P><P><B>SECTION III -- MODEL DEVELOPMENT AND DEPLOYMENT</P><P>Supervised Models </P></B><P>Decision Trees</P><P>XGBoost</P><P>Random Forrest</P><P>Naïve Bayesian</P><P>Linear Regression</P><P>Kalman Filter</P><P>References</P><P></P><B><P>Unsupervised Models</P></B><P>Hierarchical Clustering</P><P>K-Means Clustering</P><P>References</P><B><P>SECTION IV -- DEMOCRATIZATION AND FUTURE OF AI</P><P>National Strategies </P></B><P>National Technology Strategies for Serving People</P><P>The United Nations AI Technology Strategy </P><P>The role of the UN </P><P>AI in the Hands of People </P><P>References</P><B><P></P><P>Future </P></B><P>Democratization of Artificial Intelligence for the Future of Humanity </P><P>Dedication</P><P>Acknowledgement</P><P>Preface</P><P></P><B><P>Appendix </P><P></P><P>Index</P></B>

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.

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