Science

Researchers are equipping robots with the ability to recall and reuse past experiences, paving the way for more adaptable and robust long-term task execution.

Researchers have shown that even smaller AI models can be effectively trained to break down scientific sentences into their core components, creating a structured digital map of meaning.

A new approach combines vision and active learning to allow robotic harvesters to intelligently assess which apples are within reach, improving efficiency and reducing labeling requirements.

This review explores how incorporating causal reasoning into clinical decision support systems can move beyond simple predictions to enable more transparent, collaborative, and ultimately, trustworthy healthcare AI.

New research explores how large language models can automatically craft incentive structures that foster better collaboration in multi-agent reinforcement learning systems.

A new AI framework dramatically improves our ability to extract crucial information about viral mutations from complex scientific papers.

A new artificial intelligence framework draws inspiration from the way biological systems identify family members, fusing visual, acoustic, and contextual data to improve individual re-identification.

A new framework combines the power of artificial intelligence with symbolic reasoning to overcome challenges in molecular design and optimization, paving the way for more reliable property prediction.

New research suggests that robots undergoing continual learning develop an internal representation of their own bodies, hinting at the emergence of a rudimentary ‘self’.

As artificial intelligence increasingly shapes our choices, this review examines how our interaction with predictive algorithms echoes ancient practices of seeking guidance, ultimately demonstrating the enduring power of human decision-making.