As 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…mehr
As 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.
Daeyeol Lee received his undergraduate degree in economics from Seoul National University in South Korea, and his doctoral degree in neuroscience from the University of Illinois at Urbana-Champaign in the United States. He has held faculty appointments at Wake Forest University, University of Rochester, and Yale University, before joining the Zanvyl Krieger Mind/Brain Institute at the Johns Hopkins University as a Bloomberg Distinguished Professor in 2019. His research focuses on the brain mechanisms of decision making and high-level cognition.
Inhaltsangabe
Preface 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
Preface 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
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