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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.
New method simplifies the construction process for complex materials
With a new, user-friendly interface, researchers can quickly design many cellular metamaterial structures that have unique mechanical properties.
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.
The complicated interplay: AI and government
In an episode of the Data-Smart Pod, MIT Schwarzman College of Computing Dean Dan Huttenlocher discusses AI, use cases for generative AI in government, and balancing the human with the digital in a bureaucracy.
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.
A new way to look at data privacy
Researchers create a privacy technique that protects sensitive data while maintaining a machine-learning model’s performance.
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.
Generative AI imagines new protein structures
MIT researchers develop "FrameDiff," a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
