Science

Researchers have developed an artificial intelligence framework that can automatically generate and optimize magnetic resonance imaging pulse sequences, promising faster innovation and broader access to advanced imaging techniques.

A new study delves into the common errors of artificial intelligence systems designed to understand how people interact with objects, revealing critical weaknesses in current approaches.

A new architectural approach, inspired by biological systems, prioritizes adaptive sensing and closed-loop control for more robust and efficient physical AI.

Researchers have developed an embodied agent capable of continuous improvement in robotic tasks through self-reflection and optimization, minimizing the need for constant human intervention.

A new framework leverages machine learning and automated experimentation to accelerate the discovery of functional inorganic materials.

Researchers are pushing the boundaries of computer vision by enabling systems to recognize and reason about a limitless range of human interactions with objects.

New research suggests that AI-assisted writing isn’t simply accelerating scientific progress, but fundamentally altering how new discoveries are made.

A new framework empowers researchers to build and deploy autonomous AI systems for scientific tasks without compromising safety or reproducibility.

A new analysis explores the evolving landscape of artificial intelligence and asks whether current systems are capable of genuine creative thought.

A new framework leverages artificial intelligence to autonomously design and validate scientific hypotheses, dramatically speeding up the search for novel materials.