Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.
“Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.
The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
The challenge involves than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
