Changing the conversation in health care
The Language/AI Incubator, an MIT Human Insight Collaborative project, is investigating how AI can improve communications among patients and practitioners.
The Language/AI Incubator, an MIT Human Insight Collaborative project, is investigating how AI can improve communications among patients and practitioners.
An AI pipeline developed by CSAIL researchers enables unique hydrodynamic designs for bodyboard-sized vehicles that glide underwater and could help scientists gather marine data.
Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.
In an analysis of over 160,000 transplant candidates, researchers found that race is linked to how likely an organ offer is to be accepted on behalf of a patient.
PhD candidate Sabrina Corsetti builds photonic devices that manipulate light to enable previously unimaginable applications, like pocket-sized 3D printers.
By leveraging reflections from wireless signals like Wi-Fi, the system could allow robots to find and manipulate items that are blocked from view.
At a fireside chat, L. Rafael Reif and Anantha P. Chandrakasan discussed the importance of developing engineering leadership skills to solve the world’s most challenging problems.
MIT CSAIL researchers combined GenAI and a physics simulation engine to refine robot designs. The result: a machine that out-jumped a robot designed by humans.
Eleven faculty members have been granted tenure in six units across MIT’s School of Engineering.
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.
Ranking at the top for the 14th year in a row, the Institute also places first in 11 subject areas.
The low-cost, scalable technology can seamlessly integrate high-speed gallium nitride transistors onto a standard silicon chip.
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
Composed of “computing bilinguals,” the Undergraduate Advisory Group provides vital input to help advance the mission of the MIT Schwarzman College of Computing.