Computer Science and Artificial Intelligence Laboratory (CSAIL)

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

Dean Dan Huttenlocher has been working on developing the organizational structure of the new MIT Stephen A. Schwarzman College of Computing. He answers three questions about building the college and offers a glimpse into the new college headquarters.

3 Questions: Dan Huttenlocher on the formation of the MIT Schwarzman College of Computing

The inaugural dean shares an update on the process of building a college.

November 26, 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

An MIT-invented model efficiently and simultaneously optimizes control and design of soft robots for target tasks, which has traditionally been a monumental undertaking in computation. The model, for instance, was significantly faster and more accurate than state-of-the-art methods at simulating how quadrupedal robots (pictured) should move to reach target destinations.

How to design and control robots with stretchy, flexible bodies

Optimizing soft robots to perform specific tasks is a huge computational problem, but a new model can help.

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

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

"What does it mean," Aidinoff asks "when civil rights become about access to computers and the internet? When lack of internet access is considered a form of poverty? These questions were getting under my skin," he says. "I wanted to know how social and economic policy were tied to changing ideas about technology."

PhD student Marc Aidinoff explores how technology impacts public policy

Historian's research focuses on understanding how visions for social and economic policy are tied to changing ideas about technology.

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

Even in unexplored waters, an MIT-developed robotic system can efficiently sniff out valuable, hard-to-find spots to collect samples from. When implemented in autonomous boats deployed off the coast of Barbados (pictured), the system quickly found the most exposed coral head —meaning it was located in the shallowest spot — which is useful for studying how sun exposure impacts coral organisms.

Autonomous system improves environmental sampling at sea

Robotic boats could more rapidly locate the most valuable sampling spots in uncharted waters.

November 4, 2019

MIT and Toyota researchers have designed a new model that weighs various uncertainties and risks to help autonomous vehicles determine when it’s safe to merge into traffic at intersections with objects obstructing views, such as buildings blocking the line of sight.

Better autonomous “reasoning” at tricky intersections

Model alerts driverless cars when it’s safest to merge into traffic at intersections with obstructed views.

November 4, 2019

In a study using machine-generated art to understand what makes a picture memorable, researchers found that the images people remembered best had bright colors, simple backgrounds, and subjects centered prominently in the frame.

What makes an image memorable? Ask a computer

An artificial intelligence model developed at MIT shows in striking detail what makes some images stick in our minds.

November 1, 2019

One modular robotic cube snaps into place with rest of the M-blocks.

Self-transforming robot blocks jump, spin, flip, and identify each other

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, robots can self-assemble to form various structures with applications including inspection.

October 30, 2019

MIT engineers have developed a system for autonomous vehicles that senses tiny changes in shadows on the ground to determine if there’s a moving object coming around the corner, such as when another car is approaching from behind a pillar in a parking garage.

Helping autonomous vehicles see around corners

By sensing tiny changes in shadows, a new system identifies approaching objects that may cause a collision.

October 27, 2019

MIT News | Massachusetts Institute of Technology

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