New research reveals that artificial intelligence models trained on diverse scientific data-from molecules to materials-are beginning to ‘understand’ matter in a remarkably similar way.
A new framework proposes that reciprocal effort – the give and take of focus – is central to building successful interactions between humans and robots.
New research demonstrates how reinforcement learning can create AI systems that optimize their strategies in real-time based on user feedback, without the need for extensive labeled datasets.
A new framework empowers robots to understand and replicate actions observed in videos, bridging the gap between visual perception and physical manipulation.
A new framework breaks down intricate AI systems into modular components, improving both understanding and control.
New research reveals a counterintuitive tendency for people to dismiss accusations of dishonesty made by artificial intelligence, even when the AI expresses high confidence, highlighting the importance of understanding trust dynamics in human-AI collaboration.
A new framework uses collaborative agents to automatically refine code generated from scientific papers, bypassing the need for painstaking prompt engineering.
Researchers have experimentally characterized a new 3-DoF robotic finger demonstrating high-precision trajectory tracking capabilities for advanced manipulation tasks.
Researchers have developed a unified framework that simultaneously discovers the hidden variables and governing equations of hysteretic systems, paving the way for more accurate predictions and interpretable models.
Researchers have unveiled SimWorld, a powerful platform designed to push the boundaries of artificial intelligence by immersing agents in dynamic and believable environments.