Science

New research reveals how increasingly sophisticated artificial intelligence is being weaponized to create convincing romance scams and exploit vulnerable individuals.

Researchers have developed a physics-informed neural network that significantly accelerates the modeling of landslides and other gravity-driven flows, opening doors to real-time hazard assessment.

A new analysis reveals that current large language models are fundamentally unsuited for critical security roles, raising serious concerns for risk management and regulatory compliance.

New research reveals that complex decision-making systems built from multiple AI agents can unexpectedly worsen unfair outcomes, even if each individual agent appears unbiased.

A new framework proposes applying epidemiological principles to monitor and understand the outputs of AI systems, ensuring responsible and explainable deployment.

As artificial general intelligence nears reality, a growing body of research suggests it may emerge not as a monolithic entity, but as a complex web of interacting agents.

A new approach to financial modeling leverages Bayesian analytics to enhance accuracy and interpretability across forecasting, fraud detection, and regulatory compliance.

As data breaches and fraudulent activities surge, organizations are turning to Explainable AI to not only detect threats but also understand why those threats were flagged.

New research introduces a robust method for quantifying the risk of data reconstruction attacks in federated learning, paving the way for stronger privacy guarantees.

A new study demonstrates how graph neural networks can effectively model concrete composition and predict compressive strength, rivaling established machine learning techniques.