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 may be able to replicate 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), the course will explore 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. Such a representation enables and reflects computations that detect objects and their interactions, interpret distances, relative order, and movement; it includes planning of saccades, navigation, grasping, and abstract scene understanding. The lectures include empirical studies on humans and primates using psychophysical, imaging, and physiological tools.
Projects will begin in early to mid-October after surveying multiple lectures and auditing the recitations where PostDocs and Graduate Students from different labs will propose several projects. 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 very hands-off and will provide general guidance, ideally making the student have a more independent research role. Finally, as this is a CI-M class, students will also be expected to present their projects at the end of the class. 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/22724
For potential project mentors:
Participation by postdocs and senior graduate students in mentoring undergraduates in this class is, of course, voluntary. The potential advantages are
- to gain experience in teaching / mentoring
- to be able to mention this experience in their CV (we will give TA certificates to mentors in 9.58)
- to be able to identify promising undergrads for UROPs projects