Big data

MIT Professor Daniela Rus, director of CSAIL, said the goal of a new SystemsThatLearn@CSAIL initiative is "to create a new generation of AI tools that are deeply rooted in systems."

CSAIL launches artificial intelligence initiative with industry

SystemsThatLearn@CSAIL aims to develop new “human-like systems” for data science and other fields.

April 6, 2017

A new system called Data Civilizer automatically finds connections among many different data tables and allows users to perform database-style queries across all of them. The results of the queries can then be saved as new, orderly data sets that may draw information from dozens or even thousands of different tables.

Taming data

System finds and links related data scattered across digital files, for easy querying and filtering.

January 19, 2017

MapD is a type of commonly used database-management system modified to run on GPUs instead of CPUs, which power most traditional database-management systems. MapD can process billions of data points in milliseconds, making it 100 times faster than those traditional systems, and visualizes that data near instantaneously.

Split-second data mapping

New type of database-analytics platform queries and maps billions of data points in milliseconds.

January 11, 2017

A new MIT system first creates a graph of vehicles (circles) and ride requests (stars) in a city, and then uses a method called “integer linear programming” to compute the best assignment of vehicles to trips.

How ride-sharing can improve traffic, save money, and help the environment

Study shows carpooling apps could reduce congestion by a factor of three while still serving the same number of people.

January 4, 2017

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

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

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

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

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

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

MIT News | Massachusetts Institute of Technology

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