How machine learning models can amplify inequities in medical diagnosis and treatment
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
MIT Schwarzman College of Computing
MIT researchers combine deep learning and physics to fix motion-corrupted MRI scans
The challenge involves than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
AI models are powerful, but are they biologically plausible?
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
AI model can help determine where a patient’s cancer arose
Predictions from the OncoNPC model could enable doctors to choose targeted treatments for difficult-to-treat tumors.
Using AI to protect against AI image manipulation
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
A simpler method for learning to control a robot
Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.
A faster way to teach a robot
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.
Armando Solar-Lezama named inaugural Distinguished College of Computing Professor
EECS professor appointed to new professorship in the MIT Schwarzman College of Computing.
AI helps household robots cut planning time in half
PIGINet leverages machine learning to streamline and enhance household robots’ task and motion planning, by assessing and filtering feasible solutions in complex environments.
AI helps household robots cut planning time in half
PIGINet leverages machine learning to streamline and enhance household robots’ task and motion planning, by assessing and filtering feasible solutions in complex environments.