Existing computer models used to guide decisions about energy infrastructure and contamination often treat microbes as fixed and unchanging, even though they constantly adapt to their surroundings. University of Maine researchers are aiming to close the gap by analyzing microbial genetic information using artificial intelligence to identify the biological processes that most strongly influence underground chemical reactions. Those insights will then be integrated into existing subsurface models, allowing scientists to better simulate how microbes, minerals and groundwater interact in changing environments.

Researchers from UCL, the University of Southampton and the University of Nottingham have developed a reinforcement learning-based control scheme that helps wave energy converters harvest more power from the ocean without relying on a precise mathematical model of the device. By combining a proximal policy optimization (PPO) algorithm with short-term wave forecasts, the controller continuously adapts itself and generated up to 13.9% more energy than a conventional controller when tested on real sea-wave data collected off the coast of Cornwall, UK.

Shanghai, July 18--At the "AI for the Real World" forum during the 2026 World Artificial Intelligence Conference (WAIC), YUAN Yang, Associate Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences, delivered a keynote speech--Calculus of Intelligence: A Theoretical Framework for Agents and unveiled iFuture, a new academic journal focusing on artificial intelligence(AI), alongside its supporting platform, Oscholar, demonstrating a fresh approach to academic publishing and research paradigms in the age of AI.