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…
Electrical engineering and computer science (EECS)
Despite its impressive output, generative AI doesn’t have a coherent understanding of the world
Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.
Empowering systemic racism research at MIT and beyond
Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.
Nanoscale transistors could enable more efficient electronics
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
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.
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…
Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
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.