Computer Science and Artificial Intelligence Laboratory (CSAIL)
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
Two professors and three additional alumni recognized for “dreaming up solutions to global challenges — advancing health, sustainability, and human connection.”
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
An interdisciplinary team of researchers thinks health AI could benefit from some of the aviation industry’s long history of hard-won lessons that have created one of the safest activities today.
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.
Speranza system brings hope to users that the package they download is functional software, not malware.