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.
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
Doctoral Thesis Title: On Structure, Parallelism, and Approximation in Modern Neural Sequence Modeling Presenter: Morris Yau Presenter’s Affiliation (CSAIL, RLE, LIDS, MTL, etc.): CSAIL Thesis Supervisor(s): Jacob Andreas,…