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 ...

Interest in these AI networks, modeled after the human brain, is growing. Here’s what businesses need to know to power up tools and services. Jennifer Zaino is a New York-based freelance writer ...

Developers know a lot about the machine learning (ML) systems they create and manage, that’s a given. However, there is a need for non-developers to have a high level understanding of the types of ...

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 ...

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 ...

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 ...

The Navier–Stokes partial differential equation was developed in the early 19th century by Claude-Louis Navier and George ...