CBMM, NSF STC | The Center for Brains, Minds & Machines

More Than 500 Researchers, Physicians Rally at Boston Common To Protest Trump’s Attacks on Research Funding [The Harvard Crimson]

More than 500 researchers, physicians, and students rallied at Boston Common on Friday to protest President Donald Trump’s efforts to cut federal funding for scientific research, lay off federal employees, and eliminate diversity, equity, and more.
Infant-like learning boosts AI efficiency and adaptability [Devdiscourse]

Infants naturally learn about the world through a process of incremental understanding, building foundational concepts before tackling more complex ones.
The LericiPea “Liguri nel Mondo” 2024 Award

Started in 1994 and always supported by the Liguria Region, is awarded to Tomaso Poggio, an internationally renowned scientist, born in Genoa, Ligurian pride in the world.
2024 Nobel Prize Winner in Chemistry Has MIT Ties

Sir Demis Hassabis spent more than a year at The Center for Biological & Computational Learning (CBCL), a direct parent of the Center for Brains, Minds, and Machines, researching with CBMM's Director Tomaso Poggio and others.
2024 Nobel Prize Winner in Physics Has MIT Ties

Congratulations to John J. Hopfield who spent a year in the Center for Biological Information Processing (CBIP) a grandparent of CBMM.
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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Videos | Support Us

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 training approach could help AI agents perform better in uncertain conditions [MIT...

January 29, 2025

Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed. Adam Zewe | MIT News A home robot trained to perform household tasks in a...

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Seminars + events

Quest | CBMM Special Seminar: Adi Shamir

April 25, 2025 - 4:00 pm

Singleton Auditorium (46-3002)

Adi Shamir, The Paul and Marlene Borman Professorial Chair o...

TBA

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CBMM spotlight

Alan Yuille: The Visual System

Alan Yuille: The Visual System

Associated Research Thrust(s):

Vision and Language, Theoretical Frameworks for Intelligence, Exploring Future Directions

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Highlighted publications

K. Tiwary, Young, A., Tasneem, Z., Klinghoffer, T., Dave, A., Poggio, T., Nilsson, D. - E., Cheung, B., and Raskar, R., “What if Eye..? Computational...

CBMM Memo No:

152

Q. Liao, Ziyin, L., Gan, Y., Cheung, B., Harnett, M., and Poggio, T., “Self-Assembly of a Biologically Plausible Learning Circuit”. 2024.

CBMM Memo No:

151

A. Pinto, Rangamani, A., and Poggio, T., “On Generalization Bounds for Neural Networks with Low Rank Layers”. 2024.

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Featured video

Embedded thumbnail for A Theory of Appropriateness with Applications to Generative Artificial Intelligence

A Theory of Appropriateness with Applications to Generative Artificial Intelligence

Abstract: What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with our...

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