MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
The challenge involves than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
Predictions from the OncoNPC model could enable doctors to choose targeted treatments for difficult-to-treat tumors.
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.
The dataset, being collected as part of a US Coast Guard science mission, will be released open source to help advance naval mission planning and climate change studies.
A new technique helps a nontechnical user understand why a robot failed, and then fine-tune it with minimal effort to perform a task effectively.
