Science

New research identifies effective deep learning models for detecting distributed denial-of-service attacks targeting Internet of Things devices, prioritizing both performance and interpretability.

A new analysis reveals that today’s concerns about online misinformation have deep historical roots in earlier psychological research on memory distortion and suggest that understanding this lineage is key to addressing the current crisis.

New research demonstrates the potential of data-driven models to anticipate complications during continuous renal replacement therapy, paving the way for more proactive patient care.

A new framework enhances the ability of distributed machine learning systems to provide trustworthy predictions, even when data and models vary significantly across different sources.

New research reveals that even sophisticated AI agents can exhibit surprisingly human-like, and counterproductive, behavior when competing for limited resources.

Researchers are now using artificial intelligence to automatically detect subtle, silent bugs in the core libraries that power modern machine learning applications.

New research reveals that the structure of work and employee perceptions of change are critical factors in determining how readily and deeply artificial intelligence is integrated into the workplace.

A new approach combines malware analysis with large language models to dramatically speed up the creation of legally compliant data breach reports.

Researchers have developed a novel, training-free method to enhance the safety of large language models across multiple languages.

A new method analyzes the initial dynamics of transformer layers to predict and prevent the training instabilities that plague these powerful models.