Science

A new framework proposes governing AI not by assessing potential harms, but by directly regulating the decisions these systems make.

A new review challenges alarmist predictions of existential risk from artificial intelligence, arguing that immediate societal harms deserve greater attention.

This review outlines a practical framework to help small and medium-sized enterprises navigate the complexities of implementing artificial intelligence for improved financial decision-making.

A novel approach combines deep learning with robust statistical methods to deliver more accurate and trustworthy predictions for dynamic, real-world time series data.

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.