Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.
Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.
Our research seeks to discover best practices for using avatars to enhance performance, engagement, and STEM identity development for diverse public middle and high school computer science students. As sites of our research we run workshops in which students learn computer science in fun, relevant ways, and develop self-images as computer scientists.
Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, a team of robots can self-assemble to form different structures with applications in inspection, disaster response, and manufacturing
For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.
Artificial intelligence (AI) in the form of “neural networks” are increasingly used in technologies like self-driving cars to be able to see and recognize objects. Such systems could even help with tasks like identifying explosives in airport security lines.