MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.
For the MIT Schwarzman College of Computing dean, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing.
Associate professor and principal investigator with the MIT Schwarzman College of Computing’s Science Hub discusses the future of robotics and the importance of industry-academia collaborations.
“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.
The computer-vision technique behind these maps could help avoid contrail production, reducing aviation’s climate impact.
Researchers surveyed 100 high-performing companies to determine which of them are leading adopters of machine intelligence and data analytics, and how they succeed.
Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
New technique applied to small computer chips enables efficient vision and detection algorithms without internet connectivity.