Machine learning framework maps global rooftop growth for sustainable energy and urban planning
A novel machine learning framework developed by IIASA researchers to estimate global rooftop area growth from 2020 to 2050 can aid in planning sustainable energy systems, urban development, and climate change mitigation, and has potential for significant benefits in emerging economies.
Researchers at Kyushu University, in collaboration with Osaka University and the Fine Ceramics Center, have developed a framework that uses machine learning to speed up the discovery of materials for green energy technology.
Researchers have developed a novel machine-learning framework that uses scene descriptions in movie scripts to automatically recognize different characters' actions. Applying the framework to hundreds of movie scripts showed that these actions tend to reflect widespread gender stereotypes, some of which are found to be consistent across time. Victor Martinez…
Recent AI advances enable modeling of weather forecasting 4-5 magnitudes faster than traditional computing methods. The brightest leaders, researchers and developers in climate science, high performance computing and AI will discuss such technology breakthroughs — and how they can help foster a greener Earth — at NVIDIA GTC. The virtual…