Full Description
The author tells the untold story behind artificial intelligence. From early discoveries about how neurons communicate to today's powerful neural networks, deep learning and generative AI systems, this book traces how ideas from neuroscience shaped the machines that now shape our world. Written in clear, accessible language, it reveals how concepts such as learning, perception, and memory were first observed in biology and later translated into algorithms. More importantly, it shows how modern AI is now influencing the way scientists think about the brain, intelligence, and even what it means to be human. Based on realistic research rather than hype, the book is meant for anyone curious about where artificial intelligence truly came from—and what its growing role means for our future.
The book is written to serve both upper-level undergraduate and postgraduate students seeking conceptual grounding in AI beyond technical manuals, and academics and researchers in AI, neuroscience, philosophy, ethics, and social science, examining the origins, meaning, and societal implications of intelligent systems. The book should benefit students, researchers, engineers, and social scientists by providing historical grounding, conceptual clarity, and a framework for understanding AI as part of a longer scientific tradition. It builds on foundational neuroscience and AI research while contributing a novel interdisciplinary approach.
Contents
Dedication.- Foreword.- Preface.- Acknowledgements.- Part I. Early Sparks.- 1. The Black Stain: The Stain that Made Neurons Visible.- 2.The Squid's Secret: How an Axon Revealed the Mathematics of Thought.- 3. The Logical Neuron: The Switch that Made Thought Computable.- 4. Cells that Fire Together, Wire Together: Hebb and the Birth of Learning.- 5. Room-Sized Neural Net: Rosenblatt's Perceptron and the First AI Hype.- Lines in a Little Cat's Brain: How the Cortex Taught Machines to See.- Part II. Winters & Revival.- 7. When Intelligence Hit aWall: The Summer Vision Project and XOR.- 8. Gradient Descent: The Forgotten Math that Revived Neural Nets.- 9. Associative Memory: How Neural Networks Learned to Remember Patterns.- 10. The Algorithm that Refused to Die: Backpropagation's Second Chance.- 11. LeNet & The Quiet Wins: How Cheques Trained the First CNNs.- 12 The Kernel Era: When SVMs Eclipse Neural Nets.- Part III. When It All Clicked.- 13. Engineering the Leap: ReLU, Dropout, GPUs, and the Big Data Boom.- 14. ImageNet Night: AlexNet's Shock Victory.- 15. Deeper Without Dying: ResNets and the Era of Scaling.- 16. Move 37: AlphaGo's Creative Strike in Seoul.- 17. The Generative Turn: From GANs to ChatGPT.- 18. Attention Is All You Need: The Transformer Revolution.- Part IV. The Next Mind.- 19. Organoid Intelligence: Mini-Brains in a Dish.- 20. The Atlas of the Brain: Mapping Minds at Global Scale.- 21. Synthetic Minds with Agentic AI: AI that Designs AI.- 22 Self-Aware Machines: The Question of Artificial Consciousness.- 23 Beyond Silicon: Neuromorphic, Quantum, and Edge Sparks.- 24. The Edge of Control: Rivalry, Ambition, and the Perils of AI.- Glossary.- Index.- References.
