Engineers often use vision-language models to produce new designs, such as for airplane or automobile components.

Quality inspection has always depended on people who know what they are looking at. That remains true as automated visual inspection becomes faster and more widely deployed. The strongest systems don’t try to remove expert judgment completely.

From our bank accounts to smartphones, all our sensitive data and devices are protected with a technology known as cryptography. Present-day cryptography essentially uses a very challenging set of math problems that are nearly impossible for current computer

3D printing has matured from a speculative sci-fi fantasy into a practical tool.

The vision system scanned the part and logged a pass. The SPC chart stayed inside its control limits. The line kept moving.

The premise of AI large language models (LLMs) is that any problem can be solved by vacuuming up as much information as possible, running it through probability models, and performing complex calculations to make predictions and come up with the optimal solution.

The deployment of automated software systems called AI agents has recently exploded.

Whether you’re a researcher stumped by a mystery compound or a manufacturer perplexed by an unknown substance, there’s a major resource you can rely on: a library of chemical fingerprints, known as mass spectra, that the National Institute of Standards and T

Manufacturers are exploring artificial intelligence, but most are still struggling to scale it across operations.

Every boardroom has heard it. Every procurement checklist includes it. Every investor deck now features it somewhere between the mission statement and the revenue model. “AI ethics” has become the term that signals seriousness without necessarily delivering it.