Artificial intelligence

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

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

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

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

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

The Media Lab has never looked spookier . . .

Scene at MIT: A nightmare on Ames Street

October 31, 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

A team, including researchers from MIT’s Computer Science and Artificial Intelligence Laboratory, has created a new set of algorithms that can efficiently fit probability distributions to high-dimensional data.

Finding patterns in corrupted data

New model-fitting technique is efficient even for data sets with hundreds of variables.

October 26, 2016

Researchers used sample images, like the ones on the top row, to identify several visual features that are highly correlated with judgments that a particular area is safe or unsafe. The left side shows a low level of safety while the right shows a high level. Highlighted areas on the middle row show “unsafe” areas while the bottom row shows “safe” areas in the image.

Quantifying urban revitalization

Combining cellphone data with perceptions of public spaces could help guide urban planning.

October 24, 2016

“One thing you realize working in this lab is that we are really far away from the robot apocalypse,” says MIT senior Sarah Hensley, who is working with a team of researchers to prepare Valkyrie, a humanoid robot, for future space missions.

Prepping a robot for its journey to Mars

Senior Sarah Hensley aims to improve Earth's most advanced humanoid robot, in advance of its trip to the Red Planet.

October 18, 2016

MIT grad students Mohammad Ghassemi (left) and Tuka Al-Hanai started MIT Connect, a platform that matches participants for face-to-face lunches according to their background, availability, and interests.

Algorithm connects students to the most interesting person they’ve never met

Tuka Al-Hanai and Mohammad Ghassemi have connected over 500 MIT students for more than 1,000 lunch meetings.

October 17, 2016

MIT Media Lab Director Joi Ito (left), WIRED Editor-in-Chief Scott Dadich (center), and U.S. President Barack Obama confer in the Roosevelt Room of the White House.

President Obama discusses artificial intelligence with Media Lab Director Joi Ito

One-on-one conversation in WIRED focuses on advancements in artificial intelligence and how society should respond to related concerns.

October 14, 2016

MIT researchers outfitted their cube robot with shock-absorbing “skins” (left) that transfer less than half of the energy that would normally be transferred to the ground.

3-D-printed robots with shock-absorbing skins

By “programming” customized soft materials, CSAIL team can 3-D print safer, nimbler, more durable robots.

October 3, 2016

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