The Center for Brains, Minds & Machines

Josh McDermott seeks to replicate the human auditory system

Computer models that mimic humans’ extraordinary hearing abilities could improve treatments for hearing loss. MIT neuroscientist Josh McDermott hopes to develop computational models that can perform sophisticated auditory tasks as well as the human brain.
Tomaso Poggio to speak at the Bower Award Webinar

Prof. Poggio will give a talk at the Bower Award for Neural Networks for Machine Learning symposium honoring Kunihiko Fukushima on April 28, 2021 at 9:00AM ET. Click to see full list of speakers and register for the webinar.
Memory as a cornerstone of intelligence

Memory permits the reuse of past cognitive computations. Work by CBMM's Dasgupta and Gershman have suggested that memory may be much more ubiquitous, permeating many other computational systems.
"Biological and Computer Vision" by Gabriel Kreiman

This new book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence.
IBM-MIT launch new AI challenge to push the limits of object recognition models

IBM-MIT collaboration announces a challenge for the computer vision community to develop robust models for object recognition, demonstrating accurate predictions on ObjectNet images.
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

Understanding the differences between biological and computer vision [TechTalks]

May 10, 2021

by Ben Dickson Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Since the early years of artificial intelligence, scientists have dreamed...

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Seminars + events

Photo of Prof. Earl Miller (PILM, MIT)

CBMM Brains, Minds, and Machines Seminar Series: Something Else About Working Me...

May 11, 2021 - 4:00 pm

Hosted via Zoom

Prof. Earl K. Miller, Picower Institute for Learning and Mem...

Host: Prof. Matt Wilson (MIT) Abstract: Working memory is the sketchpad of consciousness, the...

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CBMM spotlight

Kim Scott: Online Infant Preferential Looking

Associated Research Thrust(s):

Development of Intelligence

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Highlighted publications

CBMM Memo No:

116

O. Kunhardt, Deza, A., and Poggio, T., “The Effects of Image Distribution and Task on Adversarial Robustness”. 2021.

CBMM Memo No:

115

A. Banburski, De La Torre, F., Pant, N., Shastri, I., and Poggio, T., “Distribution of Classification Margins: Are All Data Equal?”. 2021.

CBMM Memo No:

114

T. Poggio, “From Associative Memories to Powerful Machines”. 2021.

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Featured video

Embedded thumbnail for Compositional Generative Networks &amp; Adversarial Examiners: Beyond the Limitations of Current AI

Compositional Generative Networks & Adversarial Examiners: Beyond the Limitations of Current AI

Current AI visual algorithms are very limited compared to the robustness and flexibility of the human visual system. These limitations, however, are often obscured by the standard performance...

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