Computer Science and Artificial Intelligence Laboratory (CSAIL)

MapD is a type of commonly used database-management system modified to run on GPUs instead of CPUs, which power most traditional database-management systems. MapD can process billions of data points in milliseconds, making it 100 times faster than those traditional systems, and visualizes that data near instantaneously.

Split-second data mapping

New type of database-analytics platform queries and maps billions of data points in milliseconds.

January 11, 2017

“There’s a close correspondence between the behavior of networks of computers or other devices like mobile phones and that of biological systems,” says MIT professor Nancy Lynch. “We’re trying to find problems that can benefit from this distributed-computing perspective, focusing on algorithms for which we can prove mathematical properties.”

Model sheds light on purpose of inhibitory neurons

Study suggests computational role for neurons that prevent other neurons from firing.

January 9, 2017

A new MIT system first creates a graph of vehicles (circles) and ride requests (stars) in a city, and then uses a method called “integer linear programming” to compute the best assignment of vehicles to trips.

How ride-sharing can improve traffic, save money, and help the environment

Study shows carpooling apps could reduce congestion by a factor of three while still serving the same number of people.

January 4, 2017

In 2016, MIT CSAIL researchers worked on a range of projects in robotics, theory, wireless technology, software systems, and other disciplines.

Ingestible robots, glasses-free 3-D, and computers that explain themselves

A look at 16 of the coolest things that happened at the Computer Science and Artificial Intelligence Laboratory in 2016.

December 16, 2016

A new technique devised by MIT researchers can take data sets with huge numbers of variables and find approximations of them with far fewer variables.

Making big data manageable

Technique shrinks data sets while preserving their fundamental mathematical relationships.

December 14, 2016

“I see starting a company not only as an option, but the option,” says graduate student Keertan Kini. “It’s a way to make sustainable change in the world.”

Helping policy and technology work together

Engineering grad student Keertan Kini is working to strengthen the intersection of policy and technology.

December 12, 2016

Clockwise from top left: MIT professors Erik Demaine, Fredo Durand, Daniel Jackson, and William Freeman.

Four CSAIL researchers named ACM fellows

Association for Computer Machinery cites Erik Demaine, Fredo Durand, William Freeman, and Daniel Jackson for having "provided key knowledge" to computing.

December 8, 2016

MIT researchers have developed a new approach to training speech-recognition systems that doesn’t depend on transcription. Instead, their system analyzes correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings.

Learning words from pictures

System correlates recorded speech with images, could lead to fully automated speech recognition.

December 6, 2016

A four-rotor "bunnycopter" developed at MIT's Computer Science and Artificial Intelligence Laboratory features propellers at different heights.

Design your own custom drone

CSAIL system lets users design and fabricate drones with a wide range of shapes and structures.

December 5, 2016

“We make sense of the world through stories, and we remember the world through experience. That’s how it works as human beings. I am telling a story and making people live an experience at the same time,” says MIT Visiting Artist Karim Ben Khelifa.

Face to face with "The Enemy"

Visiting Artist Karim Ben Khelifa creates immersive experiences using virtual and augmented reality to humanize soldiers in global conflicts.

December 2, 2016

The researchers’ neural network was fed video from 26 terabytes of video data downloaded from the photo-sharing site Flickr. Researchers found the network can interpret natural sounds in terms of image categories. For instance, the network might determine that the sound of birdsong tends to be associated with forest scenes and pictures of trees, birds, birdhouses, and bird feeders.

Computer learns to recognize sounds by watching video

Machine-learning system doesn’t require costly hand-annotated data.

December 2, 2016

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

Given a still image from a scene, new deep-learning algorithm can create a brief video that simulates the future of that scene.

Creating videos of the future

Given a still image, CSAIL deep-learning system generates videos that predict what will happen next in a scene.

November 29, 2016

Tamara Broderick, assistant professor of electrical engineering and computer science at MIT, was a presenter at the 2016 Machine Intelligence Summit.

Meeting of the minds for machine intelligence

Industry leaders, computer scientists, and venture capitalists gather to discuss how smarter computers are remaking our world.

November 22, 2016

2016 MIT AAAS Fellows: (clockwise from top left) William Green, Wolfgang Ketterle, Howard Shrobe, and Elly Nedivi.

Four MIT faculty elected 2016 AAAS Fellows

Green, Ketterle, Nedivi, and Shrobe are among those recognized for their efforts toward advancing science.

November 21, 2016

MIT News | Massachusetts Institute of Technology

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