Researchers develop deep learning technique that can automatically identify animals V3

Researchers have developed a deep learning algorithm that can automatically identify, count and describe animals in their natural habitats. A new paper, published in Proceedings of the National Academy of Sciences (PNAS), decribes how the cutting-edge artificial intelligence technique can automatically describe photographs that have been collected by motion-sensor cameras on deep neural networks. The result is a system that can automate animal identification for up to 99.3 per cent of images while still performing at the same 96.6 per cent accuracy rate of crowd-sourced teams of human volunteers. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behaviour into'big data' sciences," explained Jeff Clune, the senior author of the paper and Harris Associate Professor at the University of Wyoming. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems."