The Neuroscience of Intelligence
Richard J. Haier
A pioneer in the research on human intelligence presents the scientific insights on the neuroscience of intelligence, the recent advances in genetics and its impact on intelligence, as well as the latest progress in neuroimaging and its implications for intelligence research.
On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines
Jeff Hawkins and Sandra Blakeslee
The authors explore what is intelligence, accordingly they state: "Prediction is not just one of the things your brain does. It is the primary function of the neocortex, and the foundation of intelligence." A good overview of the theory of human intelligence and why Artificial Intelligence will be different.
Gödel, Escher, Bach: An Eternal Golden Braid
Douglas R. Hofstadter
GEB spans and draws parallels in mathematics, linguistics, computer science, and cognitive science with a literary quality few science books have achieved. Highly recommended.
A User's Guide to Thought and Meaning
A very good introduction to the future of the ordinary perspective on thought and meaning.
Thinking, Fast and Slow
A full catalogue of the biases, shortcuts, and cognitive illusions to which humans regularly succumb. Kahneman makes it clear that Homo economicus — the rational model of human behaviour beloved of economists — is as fantastical as a unicorn.
Principles of Neural Science
Eric R. Kandel, James H. Schwartz, Thomas M. Jessell, Steven A. Siegelbaum, and A. J. Hudspeth
Throughout this book the authors document the central principle that all behavior is an expression of neural activity and illustrate the insights into behavior that neural science provides.
Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins
For twenty years he was the world's top-rated chess player, in Deep Thinking he shares an optimistic, insightful, and fascinating expert view on chess, "an enduring symbol of intellectual prowess and strategic thinking," and on Artificial Intelligence and the thinking machines that are transforming every part of our lives in both positive and negatives ways. Kasparov embraces the former and states: "We must face [the] fears in order to get the most out of our technology and to get the most of ourselves."
Unified Theories of Cognition
A terrific book, still relevant today, by one of the founders of Artificial Intelligence and Cognitive Science. The book provides an exploration of the nature of mind and recommendations for a unified theory across disciplines.
The Quest for Artificial Intelligence: A History of Ideas and Achievements
Nils J. Nilsson
The definitive history of artificial intelligence (AI), from the dreams of early pioneers to the achievements of modern research (up to 2010).
Artificial Intelligence: A Modern Approach
Stuart Russell and Peter Norvig
A book that every AI practitioner should read — It is frequently mentioned as the leading textbook in Artificial Intelligence. Used in over 1300 universities in over 110 countries. It is the 22nd most cited computer science publication on Citeseer (and 4th most cited publication of the 21st century).
Rationality and the Reflective Mind
Keith E. Stanovich
A must read book on the differences between rationality and intelligence. Stanovich considers the implications of the differences in individuals’ responses to problems and outlines his thinking on the difference between intelligence and rationality. The main innovation of the book is to distinguish between the algorithmic mind and the reflective mind, within the broad category of type 2 processes.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto
A clear and detailed account of the key ideas and algorithms of reinforcement learning. The authors discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Circuits of the Mind
Leslie G. Valiant
A rigorous computational approach to the cortex guided and informed by neuroscience findings.
Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David