MIT News Artificial Intelligence

The LOBSTgER research initiative at MIT Sea Grant explores how generative AI can expand scientific storytelling by building on field-based photographic data.

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.

Caitlin Morris, a PhD student and 2024 MAD Fellow affiliated with the MIT Media Lab, designs digital learning platforms that make room for the “social magic” that influences curiosity and motivation.

Composed of “computing bilinguals,” the Undergraduate Advisory Group provides vital input to help advance the mission of the MIT Schwarzman College of Computing.

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.

MIT Advanced Vehicle Technology Consortium marks a decade of developing data that improve understanding of how drivers use and respond to increasingly sophisticated automotive features.

The MIT Ethics of Computing Research Symposium showcases projects at the intersection of technology, ethics, and social responsibility.

By performing deep learning at the speed of light, this chip could give edge devices new capabilities for real-time data analysis.

A new method can physically restore original paintings using digitally constructed films, which can be removed if desired.

A new framework from the MIT-IBM Watson AI Lab supercharges language models, so they can reason over, interactively develop, and verify valid, complex travel agendas.

A new book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.

Forget optimists vs. Luddites. Most people evaluate AI based on its perceived capability and their need for personalization.

The system automatically learns to adapt to unknown disturbances such as gusting winds.

The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.

Coactive, founded by two MIT alumni, has built an AI-powered platform to unlock new insights from content of all types.

A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.

With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.

SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.

Courses on developing AI models for health care need to focus more on identifying and addressing bias, says Leo Anthony Celi.