This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.

The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.

A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.

Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.

A new approach could streamline virtual training processes or aid clinicians in reviewing diagnostic videos.

By synchronizing media streams transmitted from the cloud to two devices, researchers could improve cloud gaming and AR/VR applications.

Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.

A machine-learning model can identify the action in a video clip and label it, without the help of humans.

“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.

Study uncovers overlooked environmental impacts of internet use by estimating associated carbon, land, and water footprints.