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Category: Computer Science and Artificial Intelligence Laboratory (CSAIL)

How well do explanation methods for machine-learning models work? | MIT News
January 18, 2022

Imagine a team of physicians using a neural network to detect cancer in mammogram images. Even if this machine-learning model seems to be performing well, it might be focusing on image features that are accidentally correlated with tumors, like a watermark or timestamp, rather than actual signs of tumors. To test these models, researchers use “feature-attribution methods,” techniques that are supposed to tell them...

The promise and pitfalls of artificial intelligence explored at TEDxMIT event | MIT News
January 12, 2022

Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT.  Attendees were entertained and challenged as they explored “the good and bad of computing,” explained CSAIL Director Professor Daniela Rus, who organized the event with...

Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world | MIT News
December 17, 2021

Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Data, Systems, and Society. As an undergraduate, Wu won MIT’s toughest robotics competition, and as a graduate student took the University of California at Berkeley’s first-ever course on deep reinforcement learning. Now back at MIT, she’s working to improve the flow of robots in Amazon...

Nonsense can make sense to machine-learning models | MIT News
December 16, 2021

For all that neural networks can accomplish, we still don’t really understand how they operate. Sure, we can program them to learn, but making sense of a machine’s decision-making process remains much like a fancy puzzle with a dizzying, complex pattern where plenty of integral pieces have yet to be fitted.  If a model was trying to classify an image of said puzzle, for example, it could encounter well-known, but...

From counting blood cells to motion capture, sensors drive patient-centered research | MIT News
December 16, 2021

Sensors and sensing systems — from devices that count white blood cells to technologies that monitor muscle coordination during rehabilitation — can positively impact medical research, scientists said at the 2021 SENSE.nano Symposium. The virtual event focused on how sensing technologies are enabling current medical studies and aiding translation of their findings to improve human health. Featuring leaders from...

Machine-learning system flags remedies that might do more harm than good | MIT News
December 9, 2021

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death. Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing the optimal therapy can be a difficult task. For...

Machines that see the world more like humans do | MIT News
December 8, 2021

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it. Move that computer vision system to a self-driving...

A system for designing and training intelligent soft robots | MIT News
December 7, 2021

Let’s say you wanted to build the world’s best stair-climbing robot. You’d need to optimize for both the brain and the body, perhaps by giving the bot some high-tech legs and feet, coupled with a powerful algorithm to enable the climb.  Although design of the physical body and its brain, the “control,” are key ingredients to letting the robot move, existing benchmark environments favor only the latter....

Technique enables real-time rendering of scenes in 3D | MIT News
December 7, 2021

Humans are pretty good at looking at a single two-dimensional image and understanding the full three-dimensional scene that it captures. Artificial intelligence agents are not. Yet a machine that needs to interact with objects in the world — like a robot designed to harvest crops or assist with surgery — must be able to infer properties about a 3D scene from observations of the 2D images it’s trained on....

Generating a realistic 3D world | MIT News
December 7, 2021

While standing in a kitchen, you push some metal bowls across the counter into the sink with a clang, and drape a towel over the back of a chair. In another room, it sounds like some precariously stacked wooden blocks fell over, and there’s an epic toy car crash. These interactions with our environment are just some of what humans experience on a daily basis at home, but while this world may seem real, it isn’t. A new...

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