A landmark study of 1.4 million real workplace interactions with artificial intelligence reveals teachable differences between routine and sophisticated AI use that offer organizations a concrete road map for identifying and scaling high-impact AI capability.

The joint study by KPMG LLP, the U.S. audit, tax, and advisory firm, and the McCombs School of Business at The University of Texas at Austin identifies distinct, observable patterns in how high‑impact users frame problems, guide AI reasoning, and apply AI across complex tasks that KPMG is applying internally and in its work for clients. The study is published today in Harvard Business Review.

A new technique for assessing the reliability of a large language model’s predictions is better at identifying when the model is confident, but wrong. This more accurate method for uncertainty quantification could help users know whether to trust the model’s outputs.

Dr. Christine Rutter, a clinical associate professor at the Texas A&M University College of Veterinary Medicine and Biomedical Sciences, discusses the health risks associated with different metals and how owners can keep their pets safe.