A new University at Buffalo-led study outlines how artificial intelligence-powered handwriting analysis may serve as an early detection tool for dyslexia and dysgraphia among young children.

A multidisciplinary team of researchers has developed an artificial intelligence (AI) model that can predict acute child malnutrition in Kenya up to six months in advance. The tool offers governments and humanitarian organizations critical lead time to deliver life-saving food, health care, and supplies to at-risk areas.

The machine learning model outperforms traditional approaches by integrating clinical data from more than 17,000 Kenyan health facilities with satellite data on crop health and productivity. It achieves 89% accuracy when forecasting one month out and maintains 86% accuracy over six months — a significant improvement over simpler baseline models that rely only on recent historical child malnutrition prevalence trends.

A new study suggests that populations of artificial intelligence (AI) agents, similar to ChatGPT, can spontaneously develop shared social conventions through interaction alone. The research from City St George’s, University of London and the IT University of Copenhagen suggests that when these large language model (LLM) artificial intelligence (AI) agents communicate in groups, they do not just follow scripts or repeat patterns, but self-organise, reaching consensus on linguistic norms much like human communities. The study has been published today in the journal, Science Advances.