Using language to give robots a better grasp of an open-ended world
By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
AI models that prioritize similarity falter when asked to design something completely new.
Amid the race to make AI bigger and better, Lincoln Laboratory is developing ways to reduce power, train efficiently, and make energy use transparent.
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.
The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.
The system could improve image quality in video streaming or help autonomous vehicles identify road hazards in real-time.
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
The dataset, being collected as part of a US Coast Guard science mission, will be released open source to help advance naval mission planning and climate change studies.
Training artificial neural networks with data from real brains can make computer vision more robust.
MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.
MIT Environmental Solutions Initiative Research Program Director Marcela Angel MCP ’18 has built an international program in natural climate solutions.
This machine-learning method could assist with robotic scene understanding, image editing, or online recommendation systems.