Computer Science and Artificial Intelligence Laboratory (CSAIL)

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

CoolThink@JC will target 16,000 students at 32 primary schools across the city of Hong Kong, offering tools and expertise to boost computational thinking abilities. Insights from the initiative, being done in collaboration with MIT and others, will eventually inform the development of curriculum for all Hong Kong teachers and students.

Teaching Hong Kong students to embrace computational thinking

Powered by MIT expertise and regional universities, CoolThink@JC will offer training to primary teachers and students at 32 schools.

November 15, 2016

A new cordless virtual reality device consists of two directional "phased-array" antennas, each less than half the size of a credit card.

Enabling wireless virtual reality

Developed at Computer Science and Artificial Intelligence Laboratory, “MoVR” system allows VR headsets to communicate without a cord

November 14, 2016

Information extraction — or automatically classifying data items stored as plain text — is a major topic of artificial-intelligence research.

Artificial-intelligence system surfs web to improve its performance

“Information extraction” system helps turn plain text into data for statistical analysis.

November 10, 2016

A system developed by researchers at MIT and Stony Brook University should make it easier for researchers to solve complex computational problems using dynamic programming optimized for multicore chips — without the expertise that such programming typically requires.

Faster programs, easier programming

New system lets nonexperts optimize programs that run on multiprocessor chips.

November 7, 2016

An autonomous mobility scooter and related software were designed by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the National University of Singapore, and the Singapore-MIT Alliance for Research and Technology (SMART).

Driverless-vehicle options now include scooters

Self-driving scooter demonstrated at MIT complements autonomous golf carts and city cars.

November 7, 2016

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have devised a way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions.

Making computers explain themselves

New training technique would reveal the basis for machine-learning systems’ decisions.

October 27, 2016

MIT News | Massachusetts Institute of Technology

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