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Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world
Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
Systems scientists find clues to why false news snowballs on social media
A new model shows that the more polarized and hyperconnected a social network is, the more likely misinformation will spread.
Nonsense can make sense to machine-learning models
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
Machine learning speeds up vehicle routing
Strategy accelerates the best algorithmic solvers for large sets of cities.
Machine-learning system flags remedies that might do more harm than good
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
Machines that see the world more like humans do
A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.
EECS graduate women’s research summit increases research visibility and strengthens community
Summit features the latest research of women and other underrepresented genders in MIT EECS, along with an opportunity to network, share experiences, and learn.
Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices
New technique applied to small computer chips enables efficient vision and detection algorithms without internet connectivity.
A system for designing and training intelligent soft robots
“Evolution Gym” is a large-scale benchmark for co-optimizing the design and control of soft robots that takes inspiration from nature and evolutionary processes.
Technique enables real-time rendering of scenes in 3D
The new machine-learning system can generate a 3D scene from an image about 15,000 times faster than other methods.
