Center for Brains Minds and Machines

To evaluate infants’ intuition regarding what other people value, researchers showed them videos in which an agent (red bouncing ball) decides whether it’s worth the effort to leap over an obstacle to reach a goal (blue cartoon character).

How badly do you want something? Babies can tell

Ten-month-old infants determine the value of a goal from how hard someone works to achieve it.

November 23, 2017

“What’s really cool about eye tracking is it lets you see things that you’re not consciously aware of,” Professor Josh Tenenbaum says. “When psychologists and philosophers have proposed the idea of counterfactual simulation, they haven’t necessarily meant that you do this consciously. It’s something going on behind the surface, and eye tracking is able to reveal that.”

How we determine who’s to blame

Before assigning responsibility, our minds simulate alternative outcomes, study shows.

October 17, 2017

Researchers found that babies who watched an adult struggle to complete tasks before succeeding tried harder at their own difficult task, compared to babies who saw an adult succeed without effort.

Babies can learn that hard work pays off

Study finds infants try harder after seeing adults struggle to achieve a goal.

September 21, 2017

A brain slice that shows the hippocampus cells that were activated by the new stimulation technique (bottom image, lighter green areas on the left)

A noninvasive method for deep brain stimulation

Electrodes placed on the scalp could help patients with brain diseases.

June 1, 2017

Most applications of deep learning use “convolutional” neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster feeds data to multiple nodes (orange and green) of the next layer.

Explained: Neural networks

Ballyhooed artificial-intelligence technique known as “deep learning” revives 70-year-old idea.

April 14, 2017

Mobileye co-founder Amnon Shashua PhD '93 delivered a lecture on autonomous driving at MIT on March 27.

Looking ahead to the future of computer-driven cars

Amnon Shashua PhD ’93, co-founder of Mobileye, discusses challenges associated with autonomous vehicles in MIT visit.

April 13, 2017

Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines, has long thought that the brain must produce “invariant” representations of faces and other objects, meaning representations that are indifferent to objects’ orientation in space, their distance from the viewer, or their location in the visual field.

How the brain recognizes faces

Machine-learning system spontaneously reproduces aspects of human neurology.

December 1, 2016

“Our understanding of our environment is largely shaped by our memory for what’s currently out of sight,” says Caroline Robertson, a postdoc at MIT’s McGovern Institute for Brain Research and a junior fellow of the Harvard Society of Fellows. “What we were looking for are hubs in the brain where your memories for the panoramic environment are integrated with your current field of view....

How the brain builds panoramic memory

Neuroscientists identify brain regions key to linking different views of our surroundings.

September 8, 2016

Decoding hidden dreams

Neuroscientists decrypt the sleeping brain to reveal hidden memories.

August 30, 2016

English is the most used language on the Internet, and globally most of the people who speak or write in English are non-native speakers. A new database of annotated English sentences written by non-native speakers could help improve how computers handle the spoken or written language of non-native English speakers.

First major database of non-native English

Resource could yield linguistic insights, practical applications for non-native English speakers.

July 29, 2016

Researchers have developed a computational model of visual representation intended to reflect the way the brain works.

Machines that learn like people

Algorithms could learn to recognize objects from a few examples, not millions; may better model human cognition.

December 23, 2015

Humans and machines were given an image of a novel character (top) and asked to produce new versions. A machine generated the nine-character grid on the left.

Computer system passes “visual Turing test”

Character-drawing machines can fool human judges.

December 10, 2015

Students from 10 colleges and universities listen to a lecture by Eric Wang at the seven-day Quantitative Methods Workshop, held at MIT from Jan. 2–9.

A week at MIT

Workshop on quantitative methods in biology draws diverse undergrads from across the country.

January 13, 2015

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