Science

As artificial intelligence rapidly advances, simply avoiding negative outcomes isn’t enough – proactive coordination is essential for managing potential system failures.

Researchers are exploring how deep neural networks can learn to perform statistical inference directly from simulated data, bypassing the need for complex likelihood calculations.

A new approach combines deep learning with statistical modeling to understand how defects influence the dynamic and static properties of magnetic materials.

A new framework explores how to assess and manage risks from artificial intelligence without stifling development and compliance.

Researchers have developed a machine learning model that accurately forecasts how structures will vibrate, even with limited data for training.

As large language models become integral to financial services, a robust system for identifying and quantifying potential harms is critical.

A new approach leverages graph neural networks and attention mechanisms to not only identify threats in industrial control systems, but also to explain why they’re happening.

A new machine learning model significantly improves the accuracy of static analysis by predicting and filtering out false positives in vulnerability reports.

A new framework leverages the power of artificial intelligence to automate and improve the accuracy and adaptability of environmental, social, and governance reporting.

A new approach harnesses the underlying dynamics of chaotic systems to significantly improve long-term predictions of extreme events.