Muscle signals can pilot a robot
CSAIL's Conduct-A-Bot system uses muscle signals to cue a drone’s movement, enabling more natural human-robot communication.
CSAIL's Conduct-A-Bot system uses muscle signals to cue a drone’s movement, enabling more natural human-robot communication.
MIT system cuts the energy required for training and running neural networks.
Automated tools can help emergency managers make decisions, plan routes, and quantify road damage at city scales.
A machine learning algorithm combines data on the disease's spread with a neural network, to help predict when infections will slow down in each country.
Congestion control system could help streaming video, mobile games, and other applications run more smoothly.
Using a photorealistic simulation engine, vehicles learn to drive in the real world and recover from near-crash scenarios.
With help from artificial intelligence, researchers identify hidden power of vitamin A and ordinary chewing gum glaze.
Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.
By observing humans, robots learn to perform complex tasks, such as setting a table.
System ensures hackers eavesdropping on large networks can’t find out who’s communicating and when they’re doing so.
MIT duo uses music, videos, and real-world examples to teach students the foundations of artificial intelligence.
A deep-learning model identifies a powerful new drug that can kill many species of antibiotic-resistant bacteria.
Tiny, battery-free ID chip can authenticate nearly any product to help combat losses to counterfeiting.
Through the Undergraduate Research Opportunities Program, students work to build AI tools with impact.
Flexible sensors and an artificial intelligence model tell deformable robots how their bodies are positioned in a 3D environment.