Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.

FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.

By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.

The team used machine learning to analyze satellite and roadside images of areas where small farms predominate and agricultural data are sparse.

The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.

"Artificial intelligence can dramatically improve colonoscopy procedures... But AI models rely on high-quality data."

PhD students interning with the MIT-IBM Watson AI Lab look to improve natural language usage.

A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.

This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.

“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.