The problem of intelligence—its nature, how it is produced by the brain, and how it could be replicated in machines—is a deep and fundamental problem that cuts across multiple scientific disciplines. Philosophers have studied intelligence for centuries, but it is only in the last several decades that developments in science and engineering have made questions such as these approachable: How does the mind process sensory information to produce intelligent behavior, and how can we design intelligent computer algorithms that behave similarly? What is the structure and form of human knowledge—how is it stored, represented, and organized? How do human minds arise through the processes of evolution, development, and learning? How are the domains of language, perception, social cognition, planning, and motor control combined and integrated? Are there common principles of learning, prediction, decision making, or planning that span across these domains? Through lectures by members of the Massachusetts Institute of Technology's Center for Brains, Minds, and Machines, this course explores recent progress in building and understanding a representation of the environment, which is rich enough to allow us to act on the world around us and to react to events that take place in it. Also, such a representation enables and reflects computations that detect objects and their interactions and interpret distances, relative order, and movement; it enables planning of saccades, navigation, grasping, and abstract scene understanding. The lectures include empirical studies in humans and primates using psychophysical, imaging, and physiological tools. We discuss an integrative approach, combining experimental techniques in neuroscience and cognitive science with computational modeling in order to elucidate the architecture of intelligence.
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