Science

As AI systems gain more autonomy, ensuring their safety and security is paramount, and this article presents a comprehensive framework for proactive risk management.

A new framework standardizes the identification and quantification of risks facing artificial intelligence systems, connecting technical vulnerabilities to business impact.

Researchers have developed a novel agentic AI framework to dramatically improve the speed and accuracy of cloudburst prediction and enable coordinated responses to mitigate potential disasters.

A new framework proposes proactively simulating catastrophic AI scenarios to improve risk evaluation and prepare for previously unimaginable threats.

Researchers have developed a deep learning model that more accurately gauges how easy a document is to understand, going beyond simple word counts and sentence length.

As multi-agent systems powered by large language models become more complex, understanding how quickly they recover from reasoning failures is crucial for dependable operation.

A new approach leverages statistical guarantees to provide pilots with real-time risk assessments and preemptive warnings during flight testing.

A new deep learning pipeline leverages advanced image analysis to improve the detection of 16 different types of oral lesions from clinical images.

A new modeling approach identifies and addresses critical performance bottlenecks in neuromorphic accelerators, paving the way for more efficient and scalable brain-inspired hardware.

A new framework leverages the power of advanced image segmentation and knowledge graphs to automatically generate detailed descriptions of changes detected in remote sensing data.