AI method radically speeds predictions of materials’ thermal properties
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
A new technique enables users to compare several large models and choose the one that works best for their task.
Members of the MIT community, supporters, and guests commemorate the opening of the new college headquarters.
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
The challenge asked teams to develop AI algorithms to track and predict satellites’ patterns of life in orbit using passively collected data
This new tool offers an easier way for people to analyze complex tabular data.
MosaicML, co-founded by an MIT alumnus and a professor, made deep-learning models faster and more efficient. Its acquisition by Databricks broadened that mission.
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.