The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
Title: Probabilistic Machine Learning Methods for Spatiotemporal Data with Applications to Environmental Health Speaker: Renato Berlinghieri Date: Wednesday, April 29, 2026 Time: 12:00 pm Boston time Location: E14-633…
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
Causality plays a central role across science and engineering, and recent advances have come from deep collaborations between machine learning, statistics, and domain sciences. CLeaR highlights this cross-disciplinary…
Jointly led by the MIT Morningside Academy for Design, MIT Schwarzman College of Computing, and the Hasso Plattner Institute in Potsdam, the hub will foster a dynamic community where computing, creativity, and human-centered innovation meet.
Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.