Science

Accurate animal identification is crucial for conservation, but achieving it requires more than just powerful machine learning – it demands a clear understanding of ecological goals and the nature of identification errors.

As AI systems become increasingly complex and autonomous, traditional software engineering approaches are proving inadequate to predict-or even understand-their emergent behaviors.

As generative AI tools proliferate, assessing genuine human creativity requires a shift in focus from absolute merit to demonstrable distinction from machine-generated content.

Researchers have developed a new approach to generating realistic human-human co-manipulation movements, enabling more natural and intuitive collaboration.

A new perspective suggests that truly effective Explainable AI isn’t just about making models understandable, but about designing explanations that actively support human learning and skill development.

New research suggests that improving AI’s ability to understand cause and effect doesn’t require simply making models bigger, but rather building them with a more flexible internal structure.

Researchers are exploring how artificial intelligence can move beyond simple information retrieval to actively generate genuinely novel and high-quality research ideas.

A new foundation model, JoyAI-RA, is pushing the boundaries of robotic autonomy by unifying how robots perceive the world, interpret instructions, and execute complex tasks.

A new approach leverages artificial intelligence to autonomously create and refine algorithms for wireless communication, challenging traditional methods.

A new generation of AI agents is capable of autonomously formulating and validating scientific theories, moving beyond data analysis to genuine knowledge creation.