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Unpacking the “black box” to build better AI models
... Image Caption Stefanie Jegelka, a newly-tenured associate professor in the Department of Electrical Engineering ... loved about MIT, from the very beginning, was that the people really care deeply about research and creativity. That is what I ...
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Building explainability into the components of machine-learning models
... in the model contribute to its prediction. For example, if a model predicts a patient’s risk of developing cardiac disease, a physician ... types of users, from artificial intelligence experts to the people affected by a machine-learning model’s prediction. They also offer ...
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Driverless platoons
MIT engineers have studied a simple vehicle-platooning scenario and determined the best ways to deploy ... drag when they drive close together. But assembling a vehicle platoon to deliver packages between distribution centers, or to ... Professor of Aeronautics and Astronautics at MIT. “People who study these systems only look at efficiency metrics like delay and ...
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Signal Processing for Social Good
... Caption LIDS alum Kush R. Varshney visits Barauli, a small village in District Aligarh, India. Article Author ... opportunity that comes with it) is now within reach of people at the bottom of the pyramid—like the customer I visited, a driver and ...
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Machine learning experts from around the world compete to improve cancer immunotherapy
... Marios Gavrielatos had never participated in a machine learning competition when he decided to enter the Eric and Wendy ... General Hospital (MGH) to run the challenge. Over 900 people registered for the first part of the competition — making it ...
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Building Blocks of Generalizable Autonomy
... efficient representation and inference mechanisms. Arguably, a cognitive concept or a dexterous skill should be reusable across task ... Member at the Vector Institute where he leads the Toronto People, AI, and Robotics (PAIR) research group. Animesh is affiliated with ...
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Algorithm helps artificial intelligence systems dodge “adversarial” inputs
A deep-learning algorithm developed by MIT researchers is designed to help ... handle unpredictable interactions in the real world. “People can be adversarial, like getting in front of a robot to block its ... Everett says. “How can a robot think of all the things people might try to do, and try to avoid them? What sort of adversarial models ...
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Avoiding shortcut solutions in artificial intelligence
A model might make a shortcut solution and learn to identify images of cows by focusing on the ... but very practical questions that are really important to people who are trying to deploy these networks,” says Joshua Robinson, a PhD ...
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A step toward safe and reliable autopilots for flying
MIT researchers developed a machine-learning technique that can autonomously drive a car or fly a plane through a very difficult “stabilize-avoid” scenario, ... has been a longstanding, challenging problem. A lot of people have looked at it but didn’t know how to handle such high-dimensional ...
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The Climate Pocket: Exploring Local Climate Impacts with Physics-informed Machine Learning
... LIDS Lounge Abstract Would a global carbon tax reduce the flood risk at MIT? The answer to this question ... In this talk, I will introduce The Climate Pocket: a climate education simulation that leverages fast machine learning (ML)-based ... He also windsurfs poorly, jams, jokes, and loves meeting new people. ...