Researchers at Mass General Brigham have developed a series of artificial intelligence (AI) tools that uses machine learning ...

More engineers are turning to reinforcement learning to incorporate adaptive and self-tuning control into industrial systems. It aims to strike a balance between traditional ...

Entrepreneur Frank Giaoui has spent decades at the intersection of law, technology and economics, and discusses the benefits ...

A machine learning model for prediction of preeclampsia risk using routinely collected data was feasible among pregnancies in ...

Learn how to implement cryptographic agility in Model Context Protocol (MCP) to protect AI infrastructure against quantum threats with PQC and modular security.

Advances in instrumentation, modeling and control are more fully understood and utilized when assisted by first-principle, ...

What if AI doesn’t come to control us through force, but through convenience—predicting our thoughts, smoothing decisions, ...

A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine learning models rather than relying on a single algorithm. The researchers ...

Choosing the right method for multimodal AI—systems that combine text, images, and more—has long been trial and error. Emory ...

“Industrial AI must start with trusted data,” said Steve Mason, COO of Canary Labs. “Our historian has long been the foundation for operational visibility. By natively integrating with SORBA.ai’ SORBA ...

Most executives understand that bad training data can compromise the AI output, but many don't understand how to identify or address this issue.

Fragmented automation platforms cost more than you think in training, inventory and downtime. Here's how consolidation changes the math.