The Center for Brains, Minds & Machines

Memorial Symposium for Patrick Henry Winston

Advances in Cognitive Systems announces a memorial symposium in honor of the 2019 Herbert A. Simon Prize for Advances in Cognitive Systems winner Patrick Henry Winston.
CBMM Research of Deep Networks featured in PNAS Publication

Recent advances give theoretical insight into why deep learning networks are successful. CBMM Director Tomaso Poggio, postdoc Andrzej Banburski and graduate student Qianli Liao look into the "black box" of deep learning to provide insights.
CBMM's Josh Tenenbaum - Keynote Speaker for RSS 2020

The RSS Keynote on July 15 was delivered by Josh Tenenbaum, Professor of Computational Cognitive Science at MIT BCS, CSAIL. Titled “It’s all in your head: Intuitive physics, planning, and problem-solving in brains, minds and machines”.
Netflix series features CBMM Researchers

Looking for something good to watch tonight? Netflix has released "Babies: Part 2" which includes CBMM PIs Rebecca Saxe, MIT (P2:E1 What Babies Know) and Laura Schulz, MIT (P2:E6 Toddlers).
The generalization problem of DNNs recognizing whether a pixel is inside or outside a closed curve.

In CBMM Memo 105, CBMM researchers solve the generalization issues by teaching the network using simple curves before seeing the most complex ones, which facilitates learning the “coloring routine”.
Rebecca Saxe adds Guggenheim Fellowship to her already remarkable CV

Congratulations to CBMM's Rebecca Saxe on her Guggenheim Fellowship, which will allow her to pursue her work “under the freest possible conditions.”

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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

Neural pathway crucial to successful rapid object recognition in primates [MIT News]

October 20, 2020

Recurrent processing via prefrontal cortex, necessary for quick visual object processing in primates, provides a key insight for developing brain-like artificial intelligence. Alli Gold | School of...

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Seminars + events

scientific figures

Research Meeting: A deep generative model for isometric embedding to quantitativ...

November 10, 2020 - 4:00 pm

Dr. Keizo Kato, Fujitsu Laboratories Ltd., and Dr. Akira Nak...

Abstract: To analyze high-dimensional and complex data in the real world, deep generative models,...

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CBMM spotlight

Gemma Roig: Object Recognition

Gemma Roig: Object Recognition

Associated Research Thrust(s):

Theoretical Frameworks for Intelligence

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Highlighted publications

K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Vis...

CBMM Memo No:

112

T. A. Poggio and Liao, Q., “Implicit dynamic regularization in deep networks”. 2020.

CBMM Memo No:

111

S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “On the Capability of Neural Networks to Ge...

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Featured video

Embedded thumbnail for Panel Discussion on the relationship between engineering and science in CBMM and the field

Panel Discussion on the relationship between engineering and science in CBMM and the field

CBMM hosted a panel discussion with Profs. Jim DiCarlo, Tomaso A Poggio, and Joshua Tenenbaum, who discussed and debate the relationship between engineering and science in CBMM and the field: We all...

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