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 -- and in particular in the last 3 years -- 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, addressing the questions of how the brain produces intelligent behavior and how we are beginning to replicate aspects of human intelligence in machines. There are now intelligent machines  that pass reasonable forms of the Turing test. How does their intelligence compare to human intelligence? Is something still missing? If yes, what? 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 and how much it depends on the properties and limitations of the biophysics of neurons and synapses.

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 significantly 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.

For students:

Project choices will be presented early on in the year. Students will later, independently or in teams of 2, work on their projects for the rest of the semester with moderate supervision/assistance from their mentor. Mentors' role this semester will be mainly concentrated in the hands of the TAs. As this is a CI-M class, students will  be expected to present their projects at the end of the semester. Instruction and practice in oral and written communication will be provided. The class is thus a great opportunity for students who plan to apply to graduate school or pursue a career in industry research, since it is important in both cases to be able to communicate research effectively both orally and in writing. Classes will be held in person unless otherwise specified.

Canvas Website: https://canvas.mit.edu/courses/28355