A new paper in Engineering titled “Machine Memory Intelligence: Inspired by Human Memory Mechanisms” presents an approach to artificial intelligence. It aims to solve the problems of large models like ChatGPT, such as high data and power consumption, catastrophic forgetting, and weak logical reasoning. The researchers introduce “machine memory” and the machine memory intelligence (M²I) framework, exploring related key issues and progress to improve artificial intelligence (AI) capabilities.

Four University of Texas at Arlington faculty members have been named Senior Members of the National Academy of Inventors for their outstanding achievements in innovation. The UT Arlington honorees are Colin Cameron, professor of research in chemistry and biochemistry; Rasika Dias, distinguished professor of chemistry and biochemistry; Panos Shiakolas, associate professor of mechanical and aerospace engineering; and Baohong Yuan, distinguished professor of bioengineering.

Can public health experts make a forecast for the flu or another infectious disease as accurately as a computational model? 

Thomas McAndrew, assistant professor, Department of Biostatistics and Health Data Science in Lehigh University’s College of Health, has received an award from the National Science Foundation (NSF) to research and develop a novel approach to forecasting. His project, “IHBEM: Enhancing Influenza Forecasting Through an Integrated Platform for User-Generated Temporal Forecasts,” will help improve evidence-based public health decision making for infectious diseases.

The heart of the opioid epidemic that killed 665,341 people in the United States between 2005 and 2020 shifted geographically from the Northwest to the East, according to a new geographical analysis.