1st Edition
Foundation, Architecture, and Prototyping of Humanized AI A New Constructivist Approach
Humanized AI (HAI), emerging as the next of the AI waves, refers to artificial social beings that are very close to humans in various aspects, beings who are machine-race humans, not digital slaves. Foundation, Architecture, and Prototyping of HAI deploy a novel smalldata approach to vertically explore the spectrum of HAI.
Different from the popular big-data philosophy that is based on the rigid notion that the connotation of each concept is fixed and the same to everyone, this book treats understanding as a process from simple to complex, and uses the similarity principle to effectively deal with novelties. Combining the efficiency of the Behaviorists’ goal-driven approach and the flexibility of a Constructivists’ approach, both the architecture of HAI and the philosophical discussions arising from it are elaborated upon.
Advancing a unique approach to the concept of HAI, this book appeals to professors and students of both AI and philosophy, as well as industry professionals looking to stay at the forefront of developments within the field.
Part 1: Philosophy of Humanized AI in Plain Language
Chapter 1 The Human-Machine World to Create: Humanized AI
Chapter 2 The World We Live in and The World in Our Eyes
Chapter 3 The World in Our Mind: Fundamental Laws and Principles
Chapter 4 The World to Learn: Cognition and Learning
Part 2: Humanized AI and Its Approaches
Chapter 1 A Brief History of AI and Machine-Learning Methods
Chapter 3 Existing Approaches to Humanized AI
Chapter 4 New Approach To Humanized AI
Part 3: Architecture of Humanized AI
Chapter 1 Three Worlds and Virtual Reality
Chapter 2 First Designer Stance
Chapter 3 Dynamic Knowledge Representation
Chapter 4 Attention Mechanism and Attentive World
Chapter 5 Learning Mechanism and Knowledge Discovery
Chapter 6 Adaptive Response Mechanism
Chapter 7 Effective Teaching
Part 4: Prototyping Agents - Zda and Lia
Chapter 1 Functional and Logic Specifications
Chapter 2 Modularization of Humanized AI Architecture
Chapter 3 Implementations of Innate Mechanisms
Chapter 4 Miscellaneous
Glossary
Appendix: Tutorial to Common Methods for Narrow AI
References
Biography
Mark Chang, PhD, is the founder of AGInception and an adjunct professor of Biostatistics at Boston University. He is an elected fellow of the American Statistical Association, a co-founder of the International Society for Biopharmaceutical Statistics. He previously held various positions in pharmaceutical companies, including Scientific Fellow, executive Director, and Senior Vice President. He is an adaptive design expert and has extensive knowledge in AI for clinical trials. He has published 12 books on artificial intelligence & machine learning, adaptive clinical trial designs, biostatistics, and scientific principles.
