Description
Book SynopsisAs man-made machines become more powerful and smarter, will their intelligence eventually exceed our own? To accurately predict how the relationship between human and artificial intelligence will change in the future, it is essential to understand the origin and limits of human intelligence. In Birth of Intelligence, distinguished neuroscientist Daeyeol Lee tackles these pressing fundamental issues. Lee reveals how intelligence is the ability of a biological agent to solve complex decision-making problems in diverse and unpredictable environments. Furthermore, understanding how intelligent behavior emerges from interaction among multiple learning systems will provide valuable insights into the ultimate nature of human intelligence.
Trade ReviewLee maintains that to understand intelligence it is essential to understand how the brain works, and perforce to become more aware of the recent advances in the field of neuroscience. Furthermore, he has done a great job of discussing, in an extremely readable way, a wide range of important topics shedding light on the nature of intelligence: the difference between animal and human intelligence; the strengths and limitations of artificial intelligence; parallels between the relationship of an employer to its agent and the relationship between genes and the brain; the role of learning in the development of intelligence; and the key role of social intelligence in human life overall. While the text is not light reading, the writing is so accessible that even the careful general reader will gain valuable understanding of what intelligence is and what it does from the perspective of an expert. * R. Bharath, Emeritus, Northern Michigan University, CHOICE *
This book addresses two fundamental questions * what it means to be intelligent and why it is important for biological systems to be intelligent. Drawing on key discoveries in neuroscience, computation, psychology, biology, and economics, Lee explains that a flexible ability to deal with the unexpected is central to intelligence and that such a capacity is inextricably linked to the biological imperative for replication and reproduction. There are books about intelligence and books about brains but this is the only one to explain how knowing about the workings of the Venus fly trap, the transistor, RNA, the agency dilemma, and Martian rovers can be useful for understanding either." Matthew Rushworth, FRS, DPhil, Professor of Cognitive Neuroscience, University of Oxford *
In this engaging book, celebrated neuroscientist Daeyeol Lee provides an accessible but authoritative introduction to the core sciences of mind and brain. Building on this, he offers a penetrating and novel argument concerning the differences between biological and artificial intelligence. The book not only contributes key points to one of the most important debates of our time, but also provides an entree into this discussion for both non-experts and experts alike. In this way, Lee helps to create a space for informed and constructive debate concerning the future of our technology, and our relationship with it." * Matthew Botvinick, MD, PhD, Director of Neuroscience Research, DeepMind and Honorary Professor, Gatsby Computational Neuroscience Unit, University College London *
This ambitious book addresses the complex subject of intelligence. It is an account by a leader on the frontiers of neuroscience and psychology that is crackling with ideas and presented within a new framework of the critical role of intelligence in evolution. The author is engaged in the most up-to-date studies on the broad topic of decision neuroscience. His narrative shows amazing mastery of the essential topics, across a wide range of fields, including psychology, neuroscience, mathematics, probability theory, economic theory, evolution, philosophy, and artificial intelligence. These are all knitted together by a logical sequence of chapters and an engaging narrative style to give new insights into the neural basis of intelligence." * Gordon M. Shepherd, MD, DPhil, Professor Emeritus in Department of Neuroscience, Yale University School of Medicine *
Table of ContentsPreface Chapter 1. Levels of Intelligence What is Intelligence? Intelligence without neurons: bacteria to plants How does a nervous system work? Reflexes: simple behavior Limitations of reflexes Connectome Multiple controllers for muscles Eye movements: a case study Many behaviors are social Chapter 2. Brain and Decision Making Utility theory Time and uncertainty Indecision: Buridan's ass Limitations of the utility theory Happiness Utility theory and the brain Meaning of action potentials Evolution of utilities Chapter 3. Artificial Intelligence Brain versus computer Will computers outperform human brains Synapse vs. transistor Hardware vs. software AI on Mars Is Sojourner still alive? Autonomous AI AI and utilities Robot society and swarm intelligence Chapter 4. Self-replicating machine Self-replicating machines Natural history of self-replicating machines Multi-talented proteins Multicellular organisms Brain evolution Evolution and Development Chapter 5. Brain and Genes Division of labor and delegation Principal-agent relationship Brain's incentive Chapter 6. Why learning? Diversity of learning Classical conditioning: a salivating dog Law of effect and instrumental conditioning: a curious cat Instrumental meets classical Instrumental and classical clash Knowledge: latent learning and place learning Chapter 7. Brain for Learning Neurons and learning Search for the engram Hippocampus and basal ganglia Reinforcement learning theory Pleasure chemical: dopamine Reinforcement learning and knowledge Regret and orbitofrontal cortex Regret neurons Chapter 8. Social Intelligence and Altruism Game theory Death of game theory? Iterative prisoner's dilemma Pavlov strategy Cooperating society Dark side of altruism Predicting the behaviors of others Recursive mind Social brain Default cognition: anthropomorphization Chapter 9. Intelligence and Self Paradox of self-knowledge Meta-cognition and meta-selection Cost of intelligence Chapter 10. Conclusion: Questions for Artificial Intelligence
