While exploring a digitally represented object through artificially created sense of touch, brain-computer interface users described the warm fur of a purring cat, the smooth rigid surface of a door key and cool roundness of an apple.

Many products in the modern world are in some way fabricated using computer numerical control (CNC) machines, which use computers to automate machine operations in manufacturing. While simple in concept, the ways to instruct these machines is in reality often complex. A team of researchers has devised a system to demonstrate how to mitigate some of this complexity.

Humans are better than current AI models at interpreting social interactions and understanding social dynamics in moving scenes. Researchers believe this is because AI neural networks were inspired by the infrastructure of the part of the brain that processes static images, which is different from the area of the brain that processes dynamic social scenes.

Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains error free.

Inspired by the movements of a tiny parasitic worm, engineers have created a 5-inch soft robot that can jump as high as a basketball hoop. Their device, a silicone rod with a carbon-fiber spine, can leap 10 feet high even though it doesn't have legs. The researchers made it after watching high-speed video of nematodes pinching themselves into odd shapes to fling themselves forward and backward.

Researchers have developed a new robotic framework powered by artificial intelligence — called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) — that allows robots to learn tasks by watching a single how-to video.

Researchers have developed a new artificial intelligence (AI) technique that brings machine vision closer to how the human brain processes images. Called Lp-Convolution, this method improves the accuracy and efficiency of image recognition systems while reducing the computational burden of existing AI models.

A powerful clinical artificial intelligence tool developed by biomedical informatics researchers has demonstrated remarkable accuracy on all three parts of the United States Medical Licensing Exam (Step exams), according to a new article.

A team has developed an explainable AI model for automatic collision avoidance between ships.

A groundbreaking open-source computer program uses artificial intelligence to analyze videos of patients with Parkinson's disease and other movement disorders. The tool, called VisionMD, helps doctors more accurately monitor subtle motor changes, improving patient care and advancing clinical research.