Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new framework called CommPlan that, rather than telling robots exactly when and how to communicate, gives them a few high-level principles for good etiquette and then leaves it to the robot to make decisions that would allow it to finish the task as efficiently as possible.
Love is Blind's AI/data scientist Cameron Hamilton speaks with CSAIL's Rachel Gordon on his role as an AI/data scientist, finding love on the hugely popular Netflix show, and where he sees the future of digital dating.
According to MIT CSAIL's Dr. Amar Gupta, whose work concentrates on telemedicine, there are currently many barriers to making health care better, safer, and more affordable for everyone, despite government claims that electronic health records would revolutionize the system.
“What we were trying to do in this work is to explain how perception can be so much richer than just attaching semantic labels on parts of an image, and to explore the question of how do we see all of the physical world,” says Josh Tenenbaum, a professor of computational cognitive science and a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM).
“I want society to truly embrace machine learning,” says Madry, a recently tenured professor in the Department of Electrical Engineering and Computer Science. “To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.”
Training interactive robots may one day be an easy job for everyone, even those without programming expertise. Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. In the workplace, you could train robots like new employees, showing them how to perform many duties.
“There still isn’t a unified way to predict how well a neural network will perform given certain factors like the shape of the model or the amount of data it’s been trained on,” says Jonathan Rosenfeld, who recently developed a new framework on the topic with colleagues at MIT CSAIL.
Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world’s most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.
