Data

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 spinout Benchling is bringing life science researchers a cloud-based platform that integrates different types of lab software into one place, with aims of making research and development easier, quicker, and more collaborative.

“Google Docs” for life sciences accelerates discovery

Cloud-based platform assists in managing, sharing data facilitates research.

December 7, 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

The Lincoln Laboratory Supercomputing Center in Holyoke, Massachusetts, is home to the only zero-carbon emissions supercomputer on the 2016 TOP500 list of the world's most powerful supercomputers.

Lincoln Laboratory's supercomputing system ranked most powerful in New England

New Dell EMC petaflop-scale supercomputer has 6 times more processing power and 20 times more bandwidth than its predecessor.

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

Common Sensing co-founders Richard Whalley (left) and James White

Data-driven diabetes management

Smart insulin-pen cap lets patients and doctors track dosage data to better manage treatment.

November 16, 2016

The TechCon conference, hosted by MIT and USAID's Higher Education Solutions Network from Nov. 10 - 12, convened researchers and innovators to collaborate on solving global issues.

At TechCon, science-based solutions for world issues

Innovators and researchers tackle global issues through actionable research and collaboration.

November 15, 2016

Ali Jadbabaie is the JR East Professor of Engineering in the Department of Civil and Environmental Engineering, the associate director of the Institute for Data, Systems, and Society (IDSS), and the director of one of its parts, the Sociotechnical Systems Research Center. “Our focus is on addressing large societal problems, whether they be power systems and energy systems, or social networks, or...

Tackling society’s big problems with systems theory

Ali Jadbabaie seeks to optimize large-scale systems, from social networks to teams of people or devices.

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

Research from Senseable City Lab’s HubCab project on the potential for taxi sharing shows how more than 150 million taxi trips connect the city of New York in a given year. Yellow represents taxi pickups, while blue indicates drop-offs.

A future of shared mobility

MIT and Uber team up to study the potential for shared rides and vehicles to reshape urban mobility.

November 8, 2016

Information Protection @ MIT: Your source for securing data

Website provides access to policies, resources, and guides for safeguarding sensitive data at the Institute.

November 7, 2016

Real estate innovation by the numbers

Real Estate Innovation Lab to promote the future of urban development by showing investors how it can work.

November 3, 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

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