TotalEnergies' deployment of machine learning at its Port Arthur, Texas, refinery demonstrates how predictive AI can ...

The virtual study demonstrated that the SABC approach to backside power minimizes EPE and over-etch variations in the TSV ...

Discover how homomorphic encryption (HE) enhances privacy-preserving model context sharing in AI, ensuring secure data handling and compliance for MCP deployments.

Integral transforms play a foundational role in applied mathematics, statistics, and theoretical physics, serving as powerful ...

The Two-Factor Theory was proposed based on engineers and accountants engaged in mental work, so it is feasible to apply the Two-Factor Theory to the research of university administrators who also ...

Through a novel combination of machine learning and atomic force microscopy, researchers in China have unveiled the molecular ...

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational ...

No matter how much data they learn, why do artificial intelligence (AI) models often miss the mark on human intent?

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

Foundation models are AI systems trained on vast amounts of data — often trillions of individual data points — and they are capable of learning new ways of modeling information and performing a range ...

In this episode of the Wine and Gold Talk podcast, Ethan Sands and Jimmy Watkins are joined by The Athletic’s Joel Lorenzi ...

Explore how AI-driven threat detection can secure Model Context Protocol (MCP) deployments from data manipulation attempts, with a focus on post-quantum security.