The NVIDIA corporate blog has long been a go-to source for information on the latest developments in AI and accelerated computing.
The blog’s series of “explainers” are among our most-read posts, offering a quick way to catch up on the newest technologies.
In this post, we’ve rounded up 11 of the most popular explainers from the blog, providing a beginner’s guide to understanding the concepts and applications of these cutting-edge technologies.
From AI models to quantum computing, these explainers are a must-read for anyone looking to stay informed on the latest tech developments in 2022.
“What Is a Pretrained AI Model?” – This post covers the basics of pretrained AI models, including how they work and why they’re useful.
“What Is Denoising?” – This piece explains denoising and its use in image and signal processing.
“What Are Graph Neural Networks?” – This article introduces graph neural networks, including how they work and are used in various applications.
“What Is Green Computing?” – This post explains the basics of green computing, including why it’s important and how it can be achieved.
“What is Direct and Indirect Lighting?” – This piece covers the differences between direct and indirect lighting in computer graphics, and how they’re used in different applications.
“What Is a QPU?” – This blog introduces the quantum processing unit, including what it is and how they’re used in quantum computing.
“What Is an Exaflop?” – This article explains what an exaflop is and why it’s an important measure of computational power.
“What Is Zero Trust?” – This post covers the basics of zero trust, including what it is and how it can improve network security.
“What Is Extended Reality?” – This piece provides an overview of extended reality — the umbrella term for virtual, augmented and mixed reality — including what it is and how it’s used in different applications.
“What Is a Transformer Model?” – This blog explains what transformer models are and how they’re used in AI.
“What Is Path Tracing?” – This article covers the basics of path tracing, including how it works and why it’s important for creating realistic computer graphics. It provides examples of its applications in different fields.
Let us know in the comments section below which AI and accelerated computing concepts you’d like explained next on our blog. We’re always looking for suggestions and feedback.