Data

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

MIT launches Institute-wide survey on commuting behaviors

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

“The goal of all this is to present the interesting stuff to the data scientists so that they can more quickly address all these new data sets that are coming in,” says Max Kanter MEng ’15.

Automating big-data analysis

With new algorithms, data scientists could accomplish in days what has traditionally taken months.

October 21, 2016

“MIT is wonderful because in addition to [finding] an amazing home in economics, I can talk with and learn from a lot of people on the science side,” says Heidi Williams, the Class of 1957 Career Development Associate Professor in MIT’s Department of Economics.
MIT professor Heidi Williams

Making a splash in health care economics

Heidi Williams builds all-new data sets to answer questions about innovation and biomedical research.

October 18, 2016

MIT News | Massachusetts Institute of Technology

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