Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

The experimental model won't compete with the biggest and best, but it could tell us why they behave in weird ways—and how ...

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner ...

The Navier–Stokes partial differential equation was developed in the early 19th century by Claude-Louis Navier and George Stokes. It has proved its worth for modelling incompressible fluids in ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...

eSpeaks host Corey Noles sits down with Qualcomm's Craig Tellalian to explore a workplace computing transformation: the rise of AI-ready PCs. Matt Hillary, VP of Security and CISO at Drata, details ...

An international collaboration has resulted in a paper in Scientific Reports. Associate Professor Timur Madzhidov, one of the co-authors of the publication, explains, "First, we fed existing chemical ...