The problem of human intelligence — its nature, how the brain produces it, 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 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, or planning that span across these domains?

This class provides instruction on the mechanistic basis of intelligence, focusing on answering the question of how the brain produces intelligent behavior and how we are beginning to replicate aspects of human intelligence in machines. We will focus on a quantitative computational approach, combining experimental techniques in neuroscience and cognitive science with computational modeling to elucidate the computational architecture of human intelligence.

Through lectures by the various members of the Center for Brains, Minds, and Machines (CBMM) and of the Quest, the course will explore recent progress in  understanding the problem of intelligence. Ironically, our understanding of intelligent and biological machines is lagging the development of impressive applications. The lectures include empirical studies not only of artificial systems but also of  humans and primates using psychophysical, imaging, and physiological tools.