Science

Generative artificial intelligence offers powerful new tools for teaching and learning science, but realizing its potential requires a thoughtful, coherent approach.

New research reveals how specific robot actions demonstrably shape human behavior during social interactions.

A novel artificial intelligence system combining neural networks and symbolic reasoning has achieved a significant advance in the field of combinatorial design.

New research explores how a robot’s physical materials and aesthetic design can proactively communicate its intentions and context to users, fostering more natural and effective collaboration.

A new multi-agent system, EvoScientist, is pushing the boundaries of automated research by evolving AI agents to independently design, execute, and report scientific experiments.

A new framework proposes shifting the focus of human-robot interaction from autonomous action to human-led collaboration, unlocking creative potential and responsible control.

A new wave of intelligent agents, powered by large language models, is poised to dramatically accelerate scientific workflows and reshape how we analyze complex data.

New research shows that carefully structuring robot learning datasets with analogous movement patterns dramatically improves a robot’s ability to transfer skills between different bodies.

A new open-source toolkit streamlines the process of extracting material properties from spectral data, making advanced analysis accessible to a wider range of researchers.

Researchers have developed a new framework to generate vast datasets for training humanoid robots to move effectively through cluttered spaces, leveraging the power of virtual reality and procedural generation.