Computer Science and Artificial Intelligence Laboratory (CSAIL)

MIT researchers fed their physics prediction system videos of collisions, shown as screenshots in (a) and (b), which were then converted into simulations generated by the 3-D physics engine, shown in (c) and (d).

Computer model matches humans at predicting how objects move

“3-D physics engine” from the Computer Science and Artificial Intelligence Laboratory simulates the human brain to infer physical properties.

January 4, 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

“In health care, there’s this gigantic world of unstructured data that needs to be translated into useable information,” says Paul Nemirovsky, who co-founded dMetrics with Ariadna Quattoni.

Detecting consumer decisions within messy data

Software analyzes online chatter to predict health care consumers’ behavior.

December 21, 2015

For each image, the MemNet algorithm creates a heat map identifying its most memorable and forgettable regions.

Deep-learning algorithm predicts photos’ memorability at “near-human” levels

Future versions of an algorithm from the Computer Science and Artificial Intelligence Lab could help with teaching, marketing, and memory improvement.

December 16, 2015

CSAIL shows off demos to 150 high-schoolers for “Hour of Code”

Robots, 3-D printers, and a surprise guest aim to get students excited about computer science.

December 14, 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

This self-folding microbot was among the many innovations that emerged from CSAIL in 2015.

Data, drones, and 3-D-printed hearts

Looking back on the year that was: Highlights from the Computer Science and Artificial Intelligence Lab in 2015.

December 8, 2015

“Tor operates under the assumption that there’s not a global adversary that’s paying attention to every single link in the world,” Nickolai Zeldovich says. “Maybe these days this is not as good of an assumption.”

Untraceable communication — guaranteed

New untraceable text-messaging system comes with statistical guarantees.

December 7, 2015

Clockwise from top left: Professors Karl Berggren, Edmund Bertschinger, Victor Zue, and Gerald Fink have been named AAAS Fellows.

Four MIT faculty elected 2015 AAAS Fellows

Berggren, Bertschinger, Fink, and Zue are among those recognized for efforts toward advancing science.

November 23, 2015

D. Fox Harrell, associate Professor of digital media, has appointments in MIT Comparative Media Studies/Writing, and the MIT Computer Science and Artificial intelligence Laboratory.

Designing virtual identities for empowerment and social change

D. Fox Harrell receives $1.35 million in grant funding to advance research at the intersection of social science and digital technology.

November 18, 2015

NASA's "Valkyrie" robot is 6 feet tall and weighs 290 pounds. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory will test and develop the bot for future space missions.

NASA gives MIT a humanoid robot to develop software for future space missions

Team led by Professor Russ Tedrake of CSAIL to develop algorithms for 6-foot-tall “Valkyrie” robot to travel to Mars and beyond.

November 17, 2015

hand holding a smartphone with city lights on the screen

Streamlining mobile image processing

Technique for mobile image processing in the cloud cuts bandwidth use by more than 98 percent.

November 13, 2015

MIT Libraries explore 17th-century postal archive in "Signed, Sealed, and Undelivered"

2,600 recently rediscovered early modern letters to be analyzed in groundbreaking international digital humanities project.

November 10, 2015

An example of the Non-Local Variations algorithm that automatically detects and visualizes small deformations between repeating structures in a single image. On the left is the original image. In the right image, the variability in the shape of the corn’s kernels is reduced, and the misalignment of rows is corrected.

Amplifying — or removing — visual variation

Algorithms could offer new tools for graphics software or reveal structural defects.

November 5, 2015

MIT CSAIL researchers found that pictograms, when employed appropriately, can dramatically improve information recall.

How to make better visualizations

Eye-tracking research reveals which types of visuals actually get the message across.