Center for Brains Minds and Machines

ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life.

This object-recognition dataset stumped the world’s best computer vision models

Objects are posed in varied positions and shot at odd angles to spur new AI techniques.

December 10, 2019

An MIT-invented model demonstrates an understanding of some basic “intuitive physics” by registering “surprise” when objects in simulations move in unexpected ways, such as
rolling behind a wall and not reappearing on the other side.

Helping machines perceive some laws of physics

Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.

December 2, 2019

Eight distinguished researchers spoke at the 2019 Materials Day Symposium. Pictured here (l-r) are MIT professors Carl Thompson, Asu Ozdaglar, Elsa Olivetti, and Ju Li.

Materials Day talks examine the promises and challenges of AI and machine learning

The ability to predict and make new materials faster highlights the need for safety, reliability, and accurate data.

November 5, 2019

Josh Tenenbaum receives 2019 MacArthur Fellowship

Brain and cognitive sciences professor studies how the human mind is able to learn so rapidly.

September 25, 2019

Eduardo Undurraga, an assistant professor at the Pontifical Catholic University of Chile, runs a musical pitch perception experiment with a member of the Tsimane’ tribe of the Bolivian rainforest.

Perception of musical pitch varies across cultures

How people interpret musical notes depends on the types of music they have listened to, researchers find.

September 19, 2019

The primary region of the human auditory cortex (outlined in white) responds differently (blue) to natural sound when background noise is present.

Hearing through the clatter

Study reveals brain regions that respond differently to the presence of background noise, suggesting the brain progressively hones in on and isolates sounds.

September 9, 2019

Researchers have developed a flexible way of combining deep learning and symbolic reasoning to teach computers to write short computer programs. Here, Armando Solar-Lezama (left), a professor at CSAIL, speaks with graduate student Maxwell Nye.

Toward artificial intelligence that learns to write code

Researchers combine deep learning and symbolic reasoning for a more flexible way of teaching computers to program.

June 14, 2019

A computer model of vision created by MIT neuroscientists designed these images that can stimulate very high activity in individual neurons.

Putting vision models to the test

Study shows that artificial neural networks can be used to drive brain activity.

May 2, 2019

Artistic rendering of artificial intelligence and deep learning visual recognition systems

For better deep neural network vision, just add feedback (loops)

The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.

April 29, 2019

Josh Tenenbaum named Innovator of the Year by R&D Magazine

Professor honored for work on the nature and origins of intelligence in the human mind and applying that knowledge to build human-like intelligence in machines.

December 19, 2018