AI copilot enhances human precision for safer aviation
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.
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.
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
PIGINet leverages machine learning to streamline and enhance household robots’ task and motion planning, by assessing and filtering feasible solutions in complex environments.
PIGINet leverages machine learning to streamline and enhance household robots’ task and motion planning, by assessing and filtering feasible solutions in complex environments.
MIT researchers develop “FrameDiff,” a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.
The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.
The CSAIL scientist describes natural language processing research through state-of-the-art machine-learning models and investigation of how language can enhance other types of artificial intelligence.