Science

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.

This research details the development of a bio-inspired, underactuated robot designed for efficient and simplified underwater navigation.

A collaborative AI framework unlocks novel modeling strategies in scientific machine learning by mimicking the power of distributed expertise.