题型:阅读理解 题类:常考题 难易度:普通
北京市房山区2019-2020学年高二上学期英语期末考试试卷
Scientists at the University of Oxford have developed new artificial intelligence software to recognize the faces of chimpanzees in the wild. The new software will allow researchers to significantly cut back on time and resources spent analyzing video footage.
"For species like chimpanzees, which have complex social lives and live for many years, recording their behavior from short-term field research can only tell us so much." says Dan Schofield, researcher and DPhil student at Oxford University's Primate Models Lab. "By using the power of machine learning to unlock large video footage, it makes it feasible to measure behavior over the long term. Observing how the social lives of a group change over several generations become possible as well."
The computer model was trained using over 10 million images from Kyoto University's Primate Research Institute (PRI) video footage of wild chimpanzees in West Africa. The new software is the first to recognize individuals in a wide range of poses, performing with high accuracy in difficult conditions such as low lighting, poor image quality and movement blur (模糊).
"Access to this large video footage has allowed us to use cutting edge deep neural networks to train models at a scale that was previously not possible." says Arsha Nagrani, co-author of the study and DPhil student in University of Oxford. "Additionally, our method differs from previous primate face recognition software in that it can be applied to raw video footage with limited manual intervention (人工干预) or pre-processing, saving hours of time and resources."
The technology has potential for many uses, such as monitoring species for protection. Although the current application focused on chimpanzees, the software provided could be applied to other species, and help drive the adoption of artificial intelligence systems to solve a range of problems in the wildlife sciences.
"All our software is available open-source for the research community." says Nagrani. "We hope that this will help researchers across other parts of the world apply the same cutting-edge techniques to their unique animal data sets. As a computer vision researcher, it is extremely satisfying to see these methods applied to solve real, challenging biodiversity (生物多样性) problems."
"With an increasing biodiversity crisis and many of the world's ecosystems under threat, the ability to closely monitor different species and populations using systems will be important for protection efforts, as well as animal behavior research." adds Schofield. "Interdisciplinary cooperation like this have huge potential to make an impact, by finding solutions for old problems, and asking biological questions which were previously not available on a large scale."
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