Science

Moving beyond explaining AI decisions, this review explores how to create inherently interpretable systems.

A new model reveals a surprisingly simple principle driving collective behavior across diverse systems, offering insights for building more resilient and adaptable robotic swarms.

A new functional architecture aims to prevent errors and ensure reliable results in AI systems designed to automate scientific discovery.

Researchers are using artificial intelligence to navigate the vast landscape of possible crystal structures and identify promising new materials.

A new approach combines learned behaviors with reinforcement learning to create more resilient and versatile humanoid robots.

A new framework aims to bring clarity and consistency to how we measure progress in applying machine learning to scientific discovery.

This review proposes a framework for building social platforms that prioritize user wellbeing, safety, and agency over pure engagement.

New research details a platform that automatically converts raw data into formats ready for artificial intelligence, minimizing the need for human intervention.

Researchers demonstrate a data-driven control system that enables a bio-inspired robotic arm to achieve robust and accurate movement.

A new digital environment allows artificial intelligence agents to independently investigate scientific questions and uncover unexpected insights.