Computer vision models have learned to identify objects in photos so accurately that some can outperform humans on some datasets. But when those same object detectors are turned loose in the real world, their performance noticeably drops, creating reliability concerns for self-driving cars and other safety-critical systems that use machine vision.
Cameras and computers together can conquer some seriously stunning feats. Giving computers vision has helped us fight wildfires in California, understand complex and treacherous roads — and even see around corners.
Jointly part of the School of Engineering and Schwarzman College of Computing, EECS is now composed of three overlapping sub-units in electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D), which brings together computer science-heritage AI and machine learning with electrical engineering-heritage information and decision systems to exploit their significant synergies. The department will remain responsible for Course 6.
MIT researchers have designed a model that demonstrates an understanding of some basic “intuitive physics” about how objects should behave. The model could be used to help build smarter artificial intelligence and, in turn, provide information to help scientists understand infant cognition.
