Science

A new analysis reveals that demographic factors and information fatigue, not the source of news, were the biggest obstacles to accurate voter perceptions during a turbulent election cycle.

A new formal verification framework demonstrates that strategically placed early exits can dramatically improve the efficiency of neural network analysis without compromising their robustness.

A new framework harnesses the capabilities of large language models to accurately forecast the dynamic behavior of power systems, enhancing grid reliability and resilience.

Researchers are leveraging the power of neural networks and symbolic regression to not only predict communication delays but also to distill those predictions into human-readable formulas.

New research demonstrates how artificially generated patient data can significantly improve the accuracy of models predicting cardiac events.

Despite remarkable advances in language processing, artificial intelligence consistently struggles to accurately interpret user intent, opening the door to manipulation and unexpected behavior.

Researchers are leveraging the power of differential equations to more accurately and efficiently forecast the movements of multiple celestial bodies.

New research reveals a universal indicator-critical slowing down-that can predict impending failure in a wide range of systems, from engineered infrastructure to natural ecosystems.

This research introduces an architecture that uses blockchain technology to enhance the security, transparency, and accountability of artificial intelligence systems operating in real-world environments.

A novel system aims to establish a clear record of AI application activity, fostering accountability and mitigating risks in increasingly complex AI ecosystems.