Using machine learning to decode animal communication
成果类型：
Editorial Material
署名作者：
Rutz, Christian; Bronstein, Michael; Raskin, Aza; Vernes, Sonja C.; Zacarian, Katherine; Blasi, Damian E.
署名单位：
University of St Andrews; University of Oxford; Max Planck Society; Harvard University
刊物名称：
SCIENCE
ISSN/ISSBN：
0036-10261
DOI：
10.1126/science.adg7314
发表日期：
2023-07-14
页码：
152-155
关键词：
摘要：
The past few years have seen a surge of interest in using machine learning (ML) methods for studying the behavior of nonhuman animals (hereafter animals) (1). A topic that has attracted particular attention is the decoding of animal communication systems using deep learning and other approaches (2). Now is the time to tackle challenges concerning data availability, model validation, and research ethics, and to embrace opportunities for building collaborations across disciplines and initiatives.