Science

Research reveals that artificial intelligence models are surprisingly adept at pinpointing and explaining errors in code written by beginner programmers.

A new analysis framework proactively identifies risks in human-AI collaboration by scrutinizing the interactions within these teams.

Researchers are developing proactive methods to identify and mitigate potential harms caused by biased artificial intelligence systems before they impact vulnerable populations.

A new deep learning approach effectively combines diverse financial opinions to improve sentiment analysis and potentially predict market trends.

A new framework leverages Bayesian neural networks to monitor the real-time condition of structures with unprecedented accuracy and reliability.

As AI systems gain the ability to act independently, ensuring their ethical and safe operation requires a new approach to risk management and control.

Researchers have developed a scalable method to build comprehensive firm-to-firm production networks from publicly available data, offering unprecedented insight into economic dependencies.

A new approach combines satellite imagery, road network details, and accident history to significantly improve the prediction of traffic accidents and identify key contributing factors.

Researchers have developed a novel framework to detect and mitigate a critical vulnerability – atomicity violations – within the code that powers decentralized applications.

A new approach to graph neural networks explicitly quantifies prediction uncertainty, enhancing robustness in challenging data scenarios.