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A new framework aims to detect AI-authored peer reviews, raising concerns about the potential suppression of creative thought in scientific evaluation.

New research demonstrates how artificial intelligence is expanding the reach and precision of ab initio nuclear theory calculations, overcoming long-standing limitations in computational power.

As artificial intelligence becomes increasingly pervasive, minimizing its energy footprint is critical for sustainable and scalable deployment in resource-limited environments.

New research reveals that the surprisingly effective generalization of massively overparameterized neural networks isn’t due to a single mechanism, but a complex interplay of factors.

A new benchmark reveals existing AI detection tools struggle to identify images generated by artificial intelligence when applied to scientific figures.

Researchers have developed a new AI framework that can realistically simulate industrial anomalies, paving the way for more robust quality control and improved defect detection systems.

This review explores how SHAP values can illuminate the inner workings of diverse machine learning models, offering a powerful approach to understanding and interpreting their predictions.

A new approach allows for accurate prediction of model outcomes even after training data is removed, offering insights into model stability and potential privacy implications.

A new framework combines the power of large language models with graph-based reasoning to improve the accuracy and, crucially, the explainability of fake news detection.

Researchers are leveraging the power of artificial intelligence to autonomously navigate and learn from complex graph-structured data, unlocking improved performance in reasoning tasks.