Machine learning

Researchers at MIT’s Microsystems Technology Laboratories have built a low-power chip specialized for automatic speech recognition. With power savings of 90 to 99 percent, it could make voice control practical for relatively simple electronic devices.

Voice control everywhere

Low-power special-purpose chip could make speech recognition ubiquitous in electronics.

February 13, 2017

MIT grad student Joy Buolamwini (left) meets author Margot Lee Shetterly at an MIT screening of "Hidden Figures."

Joy Buolamwini wins national contest for her work fighting bias in machine learning

Media Lab graduate student selected from over 7,300 entrants, awarded $50,000 scholarship in contest inspired by the film "Hidden Figures."

January 17, 2017

The Ethics and Governance of Artificial Intelligence Fund will support work around the world that advances the development of artificial intelligence in the public interest.

MIT Media Lab to participate in $27 million initiative on AI ethics and governance

Fund establishes a global initiative to advance artificial intelligence research for the public good.

January 10, 2017

“Say there are toxic gases leaking in a building, and you need to close a valve inside, but it’s dangerous to send people in,” says Sangbae Kim, associate professor of mechanical engineering at MIT. “Now, there is no single robot that can do this kind of job. I want to create a robotic first responder that can potentially do more than a human and help in our lives.”

Taking a leap in bioinspired robotics

Mechanical engineer Sangbae Kim builds animal-like machines for use in disaster response.

December 16, 2016

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and its Laboratory for Information and Decision Systems have designed a new algorithm that makes it much more practical to select diverse subsets from a much larger dataset.

Data diversity

Preserving variety in subsets of unmanageably large data sets should aid machine learning.

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

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

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

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

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

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