Democratization of artificial intelligence for the future of humanity /
Chandrasekar Vuppalapati.
- 1st.
- 1 online resource : illustrations (black and white)
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
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.