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

CBMM's Sam Gershman recipient of Schmidt Sciences Polymath Program Awards Grant

The Schmidt Sciences Polymath Program funds high-risk, innovative projects by providing multi-year grants to top researchers in critical areas from AI to climate change.
King Harald showed off eight international award winners

Norway's King Harald showed off this yea'rs receipients of the Kavli prize including CBMM's Nancy Kanwisher (MIT) and Winrich Freiwald (The Rockefeller University) for their contirbutions to neuroscience.
NeurIPS: Concordia Contest 2024

In collaboration with colleagues from Google DeepMind, MIT, UC Berkeley, and UCL, the Cooperative AI Foundation is excited to present the Concordia Contest as part of NeurIPS 2024.
CBMM's Kanwisher and Freiwald named Kavli Prize Laureates in Neuroscience

"for the discovery of a highly localized and specialized system for representation of faces in human and non-human primate neocortex."
Natural language boosts LLM performance in coding, planning, and robotics [MIT News]

Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.
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

Schmidt Sciences Polymath Program Awards $2.5M Grants to Six Pioneering Researchers [...

September 19, 2024

The Schmidt Sciences Polymath Program funds high-risk, innovative projects by providing multi-year grants to top researchers in critical areas from AI to climate change. NEW YORK—Six groundbreaking...

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Seminars + events

Research Meeting: Computational Feasibility of Artificial Human-Level Intelligen...

October 29, 2024 - 4:00 pm

Room 45-792

Eran Malach, Harvard University

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CBMM spotlight

Wiktor Młynarski: Statistics of Natural Sounds

Wiktor Młynarski: Statistics of Natural Sounds

Associated Research Thrust(s):

Theoretical Frameworks for Intelligence, Exploring Future Directions

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Highlighted publications

CBMM Memo No:

149

Y. Gan, Galanti, T., Poggio, T., and Malach, E., “On the Power of Decision Trees in Auto-Regressive Language Modeling”. 2024.

CBMM Memo No:

148

Y. Gan and Poggio, T., “For HyperBFs AGOP is a greedy approximation to gradient descent”. 2024.

T. Poggio and Fraser, M., “Compositional sparsity of learnable functions”, Bulletin of the American Mathematical Society, vol. 61, pp. 438-456, 2024.

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Featured video

Embedded thumbnail for Conveying Tasks to Computers: How Machine Learning Can Help

Conveying Tasks to Computers: How Machine Learning Can Help

Abstract: It is immensely empowering to delegate information processing work to machines and have them carry out difficult tasks on our behalf. But programming computers is hard. The traditional...

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