A new method to enhance effectiveness of cartilage repair therapy
SMART researchers find mesenchymal stromal cells are an attractive alternative to current costly, time-intensive treatments.
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SMART researchers find mesenchymal stromal cells are an attractive alternative to current costly, time-intensive treatments.
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
PrismsVR, founded by Anurupa Ganguly ’07, MNG ’09, takes students to virtual worlds to learn through experiences and movement.
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.
By using a 3D printer like an iron, researchers can precisely control the color, shade, and texture of fabricated objects, using only one material.
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
Advisors commended for providing exceptional individualized mentoring for postdocs.
MIT’s innovation and entrepreneurship system helps launch water, food, and ag startups with social and economic benefits.
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
MIT and Lincoln Laboratory are among awardees of $38 million in project awards to the Northeast Microelectronics Coalition to boost U.S. chip technology innovation.
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.
The program will invite students to investigate new vistas at the intersection of music, computing, and technology.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.