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.
A new framework uses collaborative agents to automatically refine code generated from scientific papers, bypassing the need for painstaking prompt engineering.
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.
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.
New research demonstrates how artificial neural networks can learn effective distance metrics from sequence data, potentially accelerating and improving the accuracy of evolutionary relationship inference.
This review explores how large language models are transforming speech-based interactions, paving the way for more natural and engaging conversational AI.