Can robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Four from MIT named 2025 Rhodes Scholars
Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.
A portable light system that can digitize everyday objects
A new design tool uses UV and RGB lights to change the color and textures of everyday objects. The system could enable surfaces to display dynamic patterns, such as health data and fashion designs.
MIT Schwarzman College of Computing launches postdoctoral program to advance AI across disciplines
The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.
A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Interactive mouthpiece advances opportunities for health data, assistive technology, and hands-free interactions
“MouthIO” is an in-mouth device that users can digitally design and 3D print with integrated sensors and actuators to capture health data and interact with a computer or phone.
Making it easier to verify an AI model’s responses
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
Combining next-token prediction and video diffusion in computer vision and robotics
A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.
New 3D printing technique creates unique objects quickly and with less waste
By using a 3D printer like an iron, researchers can precisely control the color, shade, and texture of fabricated objects, using only one material.
How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
AI pareidolia: Can machines spot faces in inanimate objects?
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
Enhancing LLM collaboration for smarter, more efficient solutions
“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.
A fast and flexible approach to help doctors annotate medical scans
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
Sam Madden named faculty head of computer science in EECS
Computer scientist who specializes in database management systems joins the leadership of the Department of Electrical Engineering and Computer Science.
A framework for solving parabolic partial differential equations
A new algorithm solves complicated partial differential equations by breaking them down into simpler problems, potentially guiding computer graphics and geometry processing.