Andreas Jacob, Beguš Gašper, Bronstein Michael M, Diamant Roee, Delaney Denley, Gero Shane, Goldwasser Shafi, Gruber David F, de Haas Sarah, Malkin Peter, Pavlov Nikolay, Payne Roger, Petri Giovanni, Rus Daniela, Sharma Pratyusha, Tchernov Dan, Tønnesen Pernille, Torralba Antonio, Vogt Daniel, Wood Robert J
MIT CSAIL, Cambridge, MA, USA.
Project CETI, New York, NY, USA.
iScience. 2022 May 13;25(6):104393. doi: 10.1016/j.isci.2022.104393. eCollection 2022 Jun 17.
Machine learning has been advancing dramatically over the past decade. Most strides are human-based applications due to the availability of large-scale datasets; however, opportunities are ripe to apply this technology to more deeply understand non-human communication. We detail a scientific roadmap for advancing the understanding of communication of whales that can be built further upon as a template to decipher other forms of animal and non-human communication. Sperm whales, with their highly developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent model for advanced tools that can be applied to other animals in the future. We outline the key elements required for the collection and processing of massive datasets, detecting basic communication units and language-like higher-level structures, and validating models through interactive playback experiments. The technological capabilities developed by such an undertaking hold potential for cross-applications in broader communities investigating non-human communication and behavioral research.
在过去十年中，机器学习取得了巨大进展。由于大规模数据集的可用性，大多数进展都基于人类应用；然而，将这项技术应用于更深入地理解非人类交流的时机已经成熟。我们详细阐述了一条科学路线图，以推进对鲸鱼交流的理解，该路线图可作为进一步构建的模板，用于破译其他形式的动物和非人类交流。抹香鲸具有高度发达的神经解剖特征、认知能力、社会结构以及基于离散咔哒声的编码方式，是未来可应用于其他动物的先进工具的优秀模型。我们概述了收集和处理海量数据集、检测基本交流单元和类似语言的高级结构以及通过交互式回放实验验证模型所需的关键要素。这样一项任务所开发的技术能力在更广泛的研究非人类交流和行为研究的群体中具有跨应用的潜力。
