As artificial intelligence becomes capable of producing sophisticated art in seconds, a new paper challenges us to rethink what makes human creativity valuable. The paper argues that while AI can bypass years of artistic training, it cannot replace the transformative experiences that give creativity its deeper meaning. The research suggests that the future of art may lie less in producing impressive works and more in using technology to inspire exploration, meaningful experiences, and new ways of seeing the world.

Waste that would otherwise end up in landfill could help low-carbon cement production by reducing the industry’s reliance on fossil fuels, according to Australian researchers.

Researchers in China developed AgriMamba, a text-guided multimodal framework for plant disease severity grading in complex field environments. The framework combines disease images with pathological descriptions of lesion color, shape, edge characteristics, and spatial distribution. It follows a coarse-to-fine process that localizes the target leaf, segments lesion regions, and quantifies disease severity using lesion-to-leaf area ratios. Experimental and visualization results showed that AgriMamba achieved competitive performance compared with several representative deep learning methods. The model also progressively focused on target leaves and lesions despite background interference, leaf occlusion, and small or scattered disease regions. These findings provide a new technical approach for automated plant disease assessment in complex field environments.