The Center for Brains, Minds & Machines

Brains, Minds & Machines Summer Course 2023 Applications Now Open!

An intensive three-week course will give advanced students a “deep end” introduction to the problem of intelligence – how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines.
Sprouting a STEM Community 2023

The second major Sprouting event sponsored by the National Science Foundation, and the MIT Center for Brains, Minds, and Machines. This Hackathon will be a program given to spark interest in Science, Technology, Engineering and Math (STEM) in Puerto Rico.
Understanding going under [MIT Technology Review]

By unlocking the secrets of anesthesia, Professor Emery Brown could help shed light on brain diseases, hibernation, and possibly even human consciousness.
Spotify wants to understand your body on music [Popular Science]

“The project is showing that you can actually study this stuff in the wild. The conditions here were as realistic as you can get considering these were people that were just living their lives,” study co-author Josh McDermott, tells PopSci.
Researchers Crack Open Machine Learning's Black Box to Shine a Light on Generalization [hackster.io]

Flying in the face of recent rival studies, these scientists point to generalization as key to order-of-magnitude performance gains
Brains, Minds + Machines Summer Course | MBL Woods Hole, MA

An intensive three-week course which gives a “deep end” introduction to the problem of intelligence.

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The Center for Brains, Minds, and Machines (CBMM)
is a multi-institutional NSF Science and Technology Center
dedicated to the study of intelligence - how the brain produces intelligent
behavior and how we may be able to replicate intelligence in machines.

The Center for Brains, Minds and Machines (CBMM) is a multi-institutional NSF Science and Technology Center dedicated to the study of intelligence - how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines.

Summer course students working / Marine Biology Lab, Woodshole, MA

Brains, Minds, and Machines Summer Course

A CBMM flagship program, the Brains, Minds, and Machines Summer Course is an immersive 3-week course that provides a broad introduction to research on human & machine intelligence, combining three core disciplines — computation, neuroscience, and cognition — where students learn about common empirical & theoretical methods used in the field, and compelling results of this research, with an emphasis on the scientific mission of the Center.read more »

Prof. Nancy Kanwisher lecturing to a room full of students

The Science of Intelligence Learning Hub

Explore the science of intelligence through lectures, tutorials, and courses offered by the CBMM community, and experiment with modeling and data analysis tools used in neuroscience, cognitive science, and AI, to study intelligence in brains and machines!read more »

Featured news

New insights into training dynamics of deep classifiers [MIT News]

March 8, 2023

MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks. A new study from...

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Seminars + events

CBMM Research Meeting: Do we really need to train by minimizing a loss functiona...

March 28, 2023 - 4:00 pm

McGovern Reading Room (46-5165)

H. N. Mhaskar - Claremont Graduate University, Claremont.

The fundamental problem of machine learning is often formulated as the problem of function...

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CBMM spotlight

Hector Penagos: Memories

Hector Penagos: Memories

Associated Research Thrust(s):

Neural Circuits for Intelligence, Co-Investigators

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Highlighted publications

M. Xu, Rangamani, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neu...

A. Rangamani, Rosasco, L., and Poggio, T., “For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability”, Analysis...

M. Xu, Rangamani, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neu...

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Featured video

Embedded thumbnail for Quantifying and Understanding Memorization in Deep Neural Networks

Quantifying and Understanding Memorization in Deep Neural Networks

Abstract: Deep learning algorithms are well-known to have a propensity for fitting the training data very well and memorize idiosyncratic properties in the training examples. From a scientific...

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