AI That Thinks is a visionary yet technically grounded exploration of how we move from narrow artificial intelligence to truly intelligent systems. AI shapes how intelligent systems are designed and understood. Understanding intelligence has become increasingly important.
This book traces the evolution of AI through symbolic reasoning, neural networks, computer vision, robotics, edge AI, and emerging quantum paradigms. It argues for a mechanism-agnostic definition of intelligence, the one based not on how a system is built, but on what it demonstrably does, which is to perceive, predict, learn, adapt, collaborate, and act under uncertainty. By connecting philosophy with engineering and theory with implementation, the book provides a rigorous yet accessible foundation for understanding what it would take to build machines that genuinely think.
By the end of this book, the readers will gain an understanding of intelligent systems and AI applications. The central thesis is that the future of AI will not belong to single paradigms, but to hybrid, embodied, distributed systems that integrate learning with reasoning, autonomy with accountability, and intelligence with physical reality. More than a reflection on current technology, this book offers a forward-looking blueprint for engineers, researchers, leaders, and innovators who seek to design the next era of intelligent systems.
What you will learn
● Learn how intelligence can be defined through behavior and capability.
● Understand how human cognition informs the design of intelligent machine systems.
● Understand how perception, prediction, and computer vision shape intelligent systems.
● Learn how sensing, planning, and control create embodied robotic intelligence.
● Examine the promise and practical limitations of quantum AI and quantum computing.
Who this book is for
This book is for engineers, researchers, technologists, business leaders, and forward-looking students who want to move beyond AI hype and understand how intelligent systems are truly built. It is for those who seek a deeper, interdisciplinary perspective—blending cognition, computation, robotics, distributed systems, and emerging technologies—to design, evaluate, or lead the next generation of AI that operates not just in theory, but in the real-world.
Table of Contents
1. Human Cognition to Machine Intelligence
2. Machines That Think
3. The Art of Computer Vision
4. Intelligent Mobile Robots
5. AI for All and All for AI
6. A Field Guide to Agentic AI
7. Edge AI as the Next Frontier of AI
8. Quantum AI
9. AI in Practice
10. The Final Remarks
Dr. Maharaj Mukherjee is a technologist, inventor, and AI thought leader whose career traces the evolution of AI from academic theory to enterprise-scale transformation. With a Ph.D. in computer and systems engineering from Rensselaer Polytechnic Institute, Dr. Mukherjee began his journey in AI when machine intelligence was still largely confined to research laboratories. Dr. Monideepa Roy is currently an associate professor in the School of Computer Engineering, at KIIT Deemed University, Bhubaneswar where she has been working since 2012. Her areas of interest include remote healthcare, mobile computing, recommender systems, and machine learning.
