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.
The MIT Human Insight Collaborative will elevate the human-centered disciplines and unite the Institute’s top scholars to help solve the world’s biggest challenges.
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.
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
By emulating a magnetic field on a superconducting quantum computer, researchers can probe complex properties of materials.
The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
“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.
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
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.
By fabricating semiconductor-free logic gates, which can be used to perform computation, researchers hope to streamline the manufacture of electronics.
Along with James Robinson, the professors are honored for work on the relationship between economic growth and political institutions.
By using a 3D printer like an iron, researchers can precisely control the color, shade, and texture of fabricated objects, using only one material.
Models show that an unexpected reduction in human-driven emissions led to a 10 percent decline in atmospheric mercury concentrations.
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.