Machine learning

“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

"The terms ‘typical’ and ‘atypical’ angina should be abandoned, as they do not correlate with disease and may perpetuate stereotypes based on sex," Media Lab researcher Karthik Dinakar and his colleagues conclude.

Machine learning shows no difference in angina symptoms between men and women

Finding could help overturn the prevailing notion that men and women experience angina differently.

November 6, 2019

Eight distinguished researchers spoke at the 2019 Materials Day Symposium. Pictured here (l-r) are MIT professors Carl Thompson, Asu Ozdaglar, Elsa Olivetti, and Ju Li.

Materials Day talks examine the promises and challenges of AI and machine learning

The ability to predict and make new materials faster highlights the need for safety, reliability, and accurate data.

November 5, 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

For last-mile delivery, robots of the future may use a new MIT algorithm to find the front door, using clues in their environment.

Technique helps robots find the front door

Navigation method may speed up autonomous last-mile delivery.

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

Bryan Reimer, an AgeLab research scientist and the associate director of the New England University Transportation Center, was honored for his work developing a better understanding of how people engage with vehicle automation.

Bryan Reimer receives human factors innovator award

MIT AgeLab research engineer directs a team that studies in-vehicle automation, robotics, AI, and the mechanics of driver attention, among other topics.

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

Using a supercomputing system, MIT researchers developed a model that captures what global web traffic could look like on a given day, including previously unseen isolated links (left) that rarely connect but seem to impact core web traffic (right).

Supercomputer analyzes web traffic across entire internet

Modeling web traffic could aid cybersecurity, computing infrastructure design, Internet policy, and more.

October 27, 2019

A key to compiling the novel Omnipush dataset was building modular objects (pictured) that enabled the robotic system to capture a vast diversity of pushing behavior. The central pieces contain markers on their centers and points so a motion-detection system can detect their position within a millimeter.

Pushy robots learn the fundamentals of object manipulation

Systems “learn” from novel dataset that captures how pushed objects move, to improve their physical interactions with new objects.

October 21, 2019

At left, EdgeConv, a method developed at MIT, successfully finds meaningful parts of 3D shapes, like the surface of a table, wings of an airplane, and wheels of a skateboard. At right is the ground-truth comparison.

Deep learning with point clouds

Research aims to make it easier for self-driving cars, robotics, and other applications to understand the 3D world.

October 21, 2019

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Students push to speed up artificial intelligence adoption in Latin America

Predicting people's driving personalities

Visualizing an AI model’s blind spots

Driving toward a healthier planet

Machine learning shows no difference in angina symptoms between men and women

Materials Day talks examine the promises and challenges of AI and machine learning

Autonomous system improves environmental sampling at sea

Better autonomous “reasoning” at tricky intersections

Technique helps robots find the front door

What makes an image memorable? Ask a computer

Bryan Reimer receives human factors innovator award

Helping autonomous vehicles see around corners

Supercomputer analyzes web traffic across entire internet

Pushy robots learn the fundamentals of object manipulation

Deep learning with point clouds

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Machine learning