A thorough examination of how to design and build the next generation of intelligent machines to solve social and environmental problems
Part I: Knowledge, Wisdom and Values
Section I: From Early Thinker to Social Scientists
Chapter 1. Knowledge and Wisdom Across Cultures
Chapter 2. From Philosophers to Knowledge Machines
Chapter 3. Affirmative Knowledge and Positive Human Nature
Chapter 4. Negative Knowledge and Aggressive Human Nature
Chapter 5. Role of Devices, Computers and Networks
Section II: Information Machines and Social Progress
Chapter 6. Recent Changes to the Structure of Knowledge
Chapter 7. Origin and Structure of Knowledge Energy
Chapter 8. Bands of Knowledge
Chapter 9. Frustums of Artificial Behavior
Chapter 10. Computer-Aided Knowledge Design and Validation
Section III: Knowledge Science and Social Influence
Chapter 11. Knowledge and Information Ethics
Chapter 12. From Primal Thinking to Potential Computing
Chapter 13. Action (VF) → (*) ← Object (NO) Based Processors and
Machines
Chapter 14. Aphorism and Truism in Knowledge Domain
Chapter 15. Timing Sequences and Influence of Time
Part II: Summary
Section I. The Scientific basis for Knowledge Flow
Chapter 16. General Flow Theory of Knowledge
Chapter 17. Transmission Flow Theory of Knowledge
Chapter 18. Quantum Flow Theory of Knowledge
Chapter 19. Inspiration Flow Theory of Knowledge
Chapter 20. Dynamic Nature of Knowledge: Fragmentation and Flow
Section II: Preface
Chapter 21. Knowledge Potential and Utility
Chapter 22. Elements of Knowledge as Elements in Nature
Chapter 23. Knowledge Element Machine Design: Pathways of Knowledge
in Machines
Chapter 24. Elements of Knowledge in Societies
Chapter 25. Role of Human Discretion in Society and Its Impact on
Ecosystems
Section III: Preface
Chapter 26. Scientific Foundations of Knowledge
Chapter 27. Real Space, Knowledge Space and Computational Space
Chapter 28. General Structure of Knowledge (no*→ vf and vf*→
no)
Chapter 29. The Architecture of a Mind-Machine
Chapter 30. The Architecture of a Medical Machine
Syed V. Ahamed taught at the University of Colorado for 2 years before joining Bell Laboratories. After 15 years of research, he returned to teaching as a Professor of Computer Science at the City University of New York. The author has been a Telecommunications consultant to Bell Communications Research, AT&T Bell Laboratories and Lucent Technologies for the last 25 years. He received numerous prizes for his papers from IEEE. He was elected a Fellow of the IEEE for his seminal contribution to the simulation and design studies of the High-speed Digital Subscriber Lines. He has authored and coauthored several books in two broad areas of intelligent AI-based broadband multimedia networks and computational framework for knowledge.
"Information Science is on the cusp of defining the transition from Big Data to Knowledge. This movement is being fueled by an urgency in addressing grand challenges in fields as diverse as health, public safety and climate change. Domain experts in these fields are looking to information science to provide a quantitative basis for solving hard problems in their data-intensive fields....Prof. Ahamed’s book represents a rigorous and optimistic declaration of this revolutionary trend. I recommend it heartily to teachers and students in communications and computing, and to those in pursuit of incisive mathematical philosophy." --From the Foreword by Professor Dr. Nikil Jayant, Eminent Scholar (Emeritus), Georgia Research Alliance
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