From manufacturing and mining to hospitality and healthcare, computerized maintenance management systems (CMMS) have become all but essential.

Physical AI—the embedding of digital intelligence into physical systems—is a promising but sometimes polarizing technology.

In this article, I want to explore an idea that often is framed in moral terms but is actually a cybernetic imperative: the necessity of diversity for viable systems.

From the internet and smartphones to 3D printing, recent decades have ushered in general-purpose technology that increases efficiency and collapses the cost of routine tasks.

The quality systems most medtech teams are stuck with aren’t built for how they work today. 21 CFR Part 820 was authorized by the Federal Food, Drug, and Cosmetic Act of 1978, long before the software industry even existed.

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?

The arrival of artificial intelligence (AI) in quality management has been met with a mixture of hype and skepticism. Is it just a faster anomaly detector, or is it truly transformative?

Your company works hard to bring quality products to market, but your current inefficient development process slows you down.

For decades, the one-dimensional (1D) barcode, the familiar pattern of black lines found on virtually every product, has been the universal language of global commerce.