Science

A new deep learning architecture enhances the accuracy of identifying structural damage in infrastructure using image analysis.

New research reveals that machine learning models protecting Internet of Things devices are surprisingly susceptible to data poisoning, raising critical questions about their reliability in real-world security applications.

New research proposes a framework to address the critical gap between convincingly articulated answers and actual truth in AI systems, emphasizing the need for robust human-AI collaboration.

As autonomous AI systems multiply, organizations are grappling with how to understand, govern, and mitigate the risks of increasingly complex agent networks.

New research highlights the critical need to validate explainable AI techniques across different model architectures before deploying them in high-stakes financial environments.

A new AI framework uses predictive modeling to shield borrowers from liquidation in decentralized lending platforms.

A new approach using graph neural networks and temporal analysis offers regulators a powerful tool for monitoring systemic risk and proactively identifying vulnerable institutions.

Researchers are leveraging the power of large language models to identify misleading information in the financial world, achieving top results in a recent challenge.

New research reveals that acoustic features in corporate earnings calls are surprisingly unreliable for gauging financial health, and can even hinder predictive accuracy.

A new deep learning framework improves the accuracy of building load forecasting by intelligently fusing historical data and adapting to fluctuating energy usage.