Our main goal is developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.
Understanding how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines is arguably one of the greatest challenges in science and technology. Our group brings together computer scientists, cognitive scientists, and neuroscientists to create a new field—the Science and Engineering of Intelligence.
Visual recognition is affected by changes in visual appearance (pose, scale, rotation) that do not affect a scene’s semantic category. We introduce a method for using sets of traNational Science Foundationorming examples, to learn representations that are robust to these changes.
Spatio-temporal convolutional networks are a good model of how visual cortex represents the actions of others, and thinking about robustness to complex transformations, is key to uncovering how human visual cortex is organized.
Josh Tenenbaum, a professor of brain and cognitive sciences at MIT, directs research on the development of intelligence at the Center for Brains, Minds, and Machines, a multiuniversity, multidisciplinary project based at MIT that seeks to explain and replicate human intelligence.
In the past 10 years, the best-performing artificial-intelligence systems — such as the speech recognizers on smartphones or Google’s latest automatic translator — have resulted from a technique called “deep learning.”
