Research published in the International Journal of Information and Communication Technology discusses the development of an artificial intelligence system that combines text, images and reviewer behavior to detect and trace fake e-commerce reviews. The system could address the growing challenge faced by online marketplaces as deceptive feedback becomes increasingly sophisticated.

Large language models (LLMs), which are the artificial intelligence (AI) systems behind modern chatbots, translation tools, and virtual assistants, have become revolutionary tools worldwide. Companies, governments, schools, and developers now rely on them to serve users across dozens of languages. Unfortunately, as these systems grow more capable and incorporate support for more and more languages, they also become more computationally demanding. Generating responses from large multilingual models not only costs more but also takes significantly more time.

A fire alarm jolts you from your office desk, and you head for the nearest exit. But what if the closest exit has already been blocked by the fire? Researchers at the National Institute of Standards and Technology (NIST) and their colleagues have developed an AI model called Safe Step that can redirect occupants to the safest evacuation route in a fire. Described in the Journal of Building Engineering, the model can be used with electronic displays to show whether an exit is safe to use.

A perspective in Frontiers in Artificial Intelligence titled "Evidence-based AI: from trailblazer to trustblazer?" introduces a formal discipline called Evidence-based AI that applies the rigorous standards of medicine and toxicology to agentic software systems. The paper was led by Insilica founder and CEO Dr. Thomas Luechtefeld in collaboration with Dr. Thomas Hartung of the Johns Hopkins Bloomberg School of Public Health.

Researchers at Ames National Laboratory are advancing the discovery of materials for rare-earth-free permanent magnets by combining fundamental physics with artificial intelligence. The work contributes to the U.S. Department of Energy's (DOE) Genesis Mission, which unites DOE National Labs, industry, and academia to harness AI for breakthroughs in energy, discovery science, and national security, including a focus on securing America's supply of rare earths and other critical minerals.