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

Advancing the Cooperative Intelligence of Language Model Agents Hackathon

On the first weekend of September (6th-8th September 2024) the Cooperative AI Foundation and Apart Research will be running a two-day hackathon on Advancing the Cooperative Intelligence of Language Model Agents.
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.
Artificial intelligence. Thus speaks (and spoke) the pioneer Tomaso Poggio [La Stampa]

History and future of ChatGPT and its sisters in the book just published by the Italian scientist who became famous at the Massachusetts Institute of Technology in Boston. «The genie is out of the bottle, but humans are more dangerous than robots»
Building Intelligent Machines Helps Us Learn How Our Brain Works [Scientific American]

Designing machines to think like humans provides insight into intelligence itself
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

AI Rules in Two Popular MBL Courses [Maring Biology Laboratory]

September 5, 2024

Everybody is talking about some form of artificial intelligence, or AI, these days. But at MBL, two courses have been taking students into real-world, scientific uses of AI for years. Both courses...

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Seminars + events

Quest | CBMM Seminar Series - Conveying Tasks to Computers: How Machine Learning...

September 10, 2024 - 4:00 pm

Singleton Auditorium (46-3002)

Michael Littman, Brown University

Abstract: It is immensely empowering to delegate information processing work to machines and have...

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CBMM spotlight

Liz Spelke: Core Cognition

Liz Spelke: Core Cognition

Associated Research Thrust(s):

Development of Intelligence

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Highlighted publications

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.

T. Galanti, Xu, M., Galanti, L., and Poggio, T., “Norm-based Generalization Bounds for Sparse Neural Networks”, in NeurIPS 2023, New Orleans, 2023.

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Featured video

Embedded thumbnail for Towards Understanding the Implicit Regularization Effect of SGD

Towards Understanding the Implicit Regularization Effect of SGD

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