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.
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Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
SENSE.nano symposium highlights the importance of sensing technologies in medical studies.
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.
“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.
The new machine-learning system can generate a 3D scene from an image about 15,000 times faster than other methods.
A new AI-powered, virtual platform uses real-world physics to simulate a rich and interactive audio-visual environment, enabling human and robotic learning, training, and experimental studies.
A deep learning model rapidly predicts the 3D shapes of drug-like molecules, which could accelerate the process of discovering new medicines.
Senior Shardul Chiplunkar aims to be a translator between the tech world and the rest of society.
MIT spinoff Fitnescity makes it easier for users to schedule health tests, work with physicians, and interpret results.
Professor Daniel Jackson explores conceptual clarity and a new theory of software design in his book “The Essence of Software.”
A new machine-learning model could enable robots to understand interactions in the world in the way humans do.
Mechanical engineers are using cutting-edge computing techniques to re-imagine how the products, systems, and infrastructures we use are designed.
New work on linear-probing hash tables from MIT CSAIL could lead to more efficient data storage and retrieval in computers.