A faster way to estimate AI power consumption
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
Teaching AI models to say “I’m not sure”
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
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.
Preview tool helps makers visualize 3D-printed objects
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
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.
Mixing generative AI with physics to create personal items that work in the real world
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.
Personalization features can make LLMs more agreeable
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.