AI helps robots manipulate objects with their whole bodies
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
SMART launches research group to advance AI, automation, and the future of work
Mens, Manus and Machina (M3S) will design technology, training programs, and institutions for successful human-machine collaboration.
Machine-learning system based on light could yield more powerful, efficient large language models
MIT system demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
M'Care and MIT students join forces to improve child health in Nigeria
The effort aims to transform micronutrient dosing to children by harnessing the power of data.
Artificial intelligence for augmentation and productivity
The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.
MIT researchers combine deep learning and physics to fix motion-corrupted MRI scans
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
How machine-learning models can amplify inequities in medical diagnosis and treatment
MIT researchers investigate the causes of health care disparities among underrepresented groups.
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.
The curse of variety in transportation systems
Assistant Professor Cathy Wu is addressing traffic control problems by leveraging deep reinforcement learning.
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 Professor of Computing
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.