AI That Thinks

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.

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. Since then, he has remained continuously engaged with the field— shaping, applying, and advancing AI across academia, industrial research, and global enterprise systems.

His early academic career as a professor at the Indian Institute of Technology, Kharagpur, placed him at the forefront of computer vision research in the 1990s, where he led industrially funded projects on automated inspection systems for manufacturing. and an automatic guidance system for aerial vehicles. At a time when AI-driven visual intelligence was still emerging, he was already building real-world systems that merged geometric algorithms, perception, and learning—laying a foundation for today’s computer vision revolution.

Dr. Mukherjee then spent two decades at IBM Research, where his work spanned computational geometry, electronic design automation, IoT, and cognitive AI systems. As IBM pivoted toward its Smarter Planet vision and Watson-driven cognitive computing, he led and contributed to multiple AI and machine learning initiatives that translated research into scalable enterprise solutions. His ability to bridge deep mathematical theory with deployable platforms distinguished him as both a systems thinker and execution leader.

Currently serving as senior vice president and senior AI architect at Bank of America, Dr. Mukherjee drives mission-critical AI initiatives in financial services. From enterprise chatbots and knowledge graphs to digital twins, hybrid AI systems for outage prediction, large-scale SQL transliteration engines for cloud migration, and agentic AI frameworks for CFO automation, his work operates at the intersection of advanced AI, regulatory constraints, and enterprise-scale infrastructure. Few AI practitioners possess hands-on leadership experience spanning research labs, IoT ecosystems, graph databases, hybrid AI and machine learning architectures, and financial AI governance at a global scale.

A prolific inventor, Dr. Mukherjee holds 270+ granted U.S. patents along with numerous international patents. He is recognized as an IBM Master Inventor emeritus and was inducted into IBM’s Inventor Hall/Wall of Fame—distinctions reserved for the most impactful innovators in the company’s history. His patents span AI, geometric algorithms, IoT systems, and enterprise platforms, reflecting both technical depth and long-range technological vision.

In 2025, he was honored as one of the Top 100 Influential Leaders in AI at MachineCon in New York City—recognition that affirms his role not merely as a practitioner of AI, but as a strategic voice shaping its future direction. In the same year, Dr. Mukherjee also received the prestigious Institute of Electrical and Electronics Engineers (IEEE) Innovation Awards for outstanding contributions to the Financial Industry.

What uniquely positions Dr. Mukherjee to author AI That Thinks is not only his longevity in the field, but the breadth of his engagement with it. He has seen AI as an academic discipline, as an industrial research frontier, as a platform technology during the IoT and Smarter Planet era, and now as an enterprise-transforming force through generative and agentic AI. His career embodies the continuum of AI—from algorithms to architecture, from theory to productization, from laboratory prototypes to mission-critical financial systems.

AI That Thinks is written by someone who has not merely observed the rise of intelligent systems—but has helped build them, patent them, deploy them, govern them, and envision their next evolution.

● 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. She has over 20 years of experience in teaching at undergraduate and postgraduate levels as well as guiding several projects at the B.Tech and M.Tech levels. Seven Ph.D. scholars have been awarded their doctoral degrees under her guidance in the fields of healthcare, machine learning, deep learning and blockchain, and five more Ph.D. scholars are currently working under her supervision. Some of the courses she has taught in the B.Tech and M.Tech level include computational intelligence, machine learning, design of algorithms, programming in Java, and data structures. She has many research publications in reputed Scopus indexed conferences and journals. She has organized many international conferences and workshops and has been the Organizing Chair of the first two editions of the International Conference on Computational Intelligence and Networks CINE 2015 and 2016, ICMC 2019. She is also actively involved in the course committees for syllabus and curriculum design of new subjects that are being introduced at the B.Tech level to bridge the gap between industry and academia. She has also delivered many invited talks in various colleges on current topics of AI and machine learning.

She has also successfully supervised two short projects sponsored by Samsung, and one five year project in remote healthcare, as principal investigator, sponsored by MeiTy. Monideepa Roy did her bachelors and masters in mathematics from IIT Kharagpur, and her Ph.D. in CSE from Jadavpur University.