Machine learning

ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life.

This object-recognition dataset stumped the world’s best computer vision models

Objects are posed in varied positions and shot at odd angles to spur new AI techniques.

December 10, 2019

Dixia Fan stands in front of the ITT holding some of the structures towed in the tank to study VIVs, which are important for engineering offshore ocean structures like marine drilling risers that connect underwater oil wells to the surface.

Intelligent Towing Tank propels human-robot-computer research

A novel experimental facility integrates automation and active learning, illuminating a path to accelerated scientific discovery.

December 9, 2019

Clockwise from upper left: Asu Ozdaglar, Joel Voldman, Arvind, and Antonio Torralba

Restructuring the MIT Department of Electrical Engineering and Computer Science

The Institute's largest academic department reorganizes with new leadership as part of the formation of the MIT Schwarzman College of Computing.

December 5, 2019

An MIT-invented model demonstrates an understanding of some basic “intuitive physics” by registering “surprise” when objects in simulations move in unexpected ways, such as
rolling behind a wall and not reappearing on the other side.

Helping machines perceive some laws of physics

Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.

December 2, 2019

With their model, researchers were able to generate on-demand brain scan templates of various ages (pictured) that can be used in medical-image analysis to guide disease diagnosis.

Producing better guides for medical-image analysis

Model quickly generates brain scan templates that represent a given patient population.

November 26, 2019

"In Event of Moon Disaster" reimagines the story of the first moon landing as if the Apollo 11 astronauts had not been able to return to Earth. It was created to highlight the concern about computer-based misinformation, or "deepfakes."

MIT art installation aims to empower a more discerning public

With “In Event of Moon Disaster,” the MIT Center for Advanced Virtuality aims to educate the public on deepfakes with an alternative history of the moon landing.

November 25, 2019

Daniela Rus (far right), director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), moderated a panel on dispelling the myths of AI technologies in the workplace. The AI and the Work of the Future Congress was co-organized by CSAIL, the MIT Initiative on the Digital Economy, and the MIT Work of the Future Task Force.

MIT conference focuses on preparing workers for the era of artificial intelligence

As automation rises in the workplace, speakers explore ways to train students and reskill workers.

November 22, 2019

DeepRole, an MIT-invented gaming bot equipped with “deductive reasoning,” can beat human players in tricky online multiplayer games where player roles and motives are kept secret.

Bot can beat humans in multiplayer hidden-role games

Using deductive reasoning, the bot identifies friend or foe to ensure victory over humans in certain online games.

November 19, 2019

“We believe that AI plays a key role now, and in the future development of the region, if it’s used in the right way,” says Omar Costilla Reyes, one of four MIT graduate students working to help Latin America adopt artificial intelligence technologies. Pictured here (left to right) are Costilla Reyes, Emilia Simison, Pedro Antonio Colon-Hernandez, and Guillermo Bernal.

Students push to speed up artificial intelligence adoption in Latin America

To help the region catch up, students organize summit to bring Latin policymakers and researchers to MIT.

November 19, 2019

In lane-merging scenarios, a system developed at MIT could distinguish between altruistic and egoistic driving behavior.

Predicting people's driving personalities

System from MIT CSAIL sizes up drivers as selfish or selfless. Could this help self-driving cars navigate in traffic?

November 18, 2019

A new tool reveals what AI models leave out in recreating a scene. Here, a GAN, or generative adversarial network, has dropped the pair of newlyweds from its reconstruction (right) of the photo it was asked to draw (left).

Visualizing an AI model’s blind spots

New tool highlights what generative models leave out when reconstructing a scene.

November 8, 2019

Brian Storey, director of accelerated materials design and discovery at Toyota Research Institute, speaks at the MIT MRL Materials Day Symposium.

Driving toward a healthier planet

Materials Day speaker Brian Storey describes how the Toyota Research Institute is embracing machine learning to advance the use of electric vehicles.

November 7, 2019