Against the background of the growing use of machine learning (ML) based technologies by the military, our article calls for an analytical perspective on ML platforms to understand how ML proliferates across the military and to what effects. Adopting a material–technical perspective on platforms as developed within new media studies, and bringing this literature to critical security studies, we suggest that a focus on platforms and the technical work they do is needed to understand how digital technologies are emerging and shaping security practices. Through a detailed study of Google's open-source ML platform TensorFlow and a discussion of the US Department of Defense Algorithmic Warfare Cross-Functional Team, or Project Maven, we make two broader contributions. First, we identify a broader “platformization” of the military, with which we refer to the growing involvement and permeation of the (technomaterial) ML platform as the infrastructure that enables new practices of decentralized and experimental algorithm development across the military. Second, we draw out how this platformization is accompanied by new entanglements between the military and actors in the corporate domain, especially Big Tech, which play a key role in this context, as well as the open-source community that is organized around these platforms.

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机器学习与军队平台化：谷歌机器学习平台 TensorFlow 研究

在军队越来越多地使用基于机器学习 (ML) 的技术的背景下，我们的文章呼吁对 ML 平台进行分析，以了解 ML 如何在军队中扩散以及产生何种影响。采用在新媒体研究中开发的平台的材料技术视角，并将这些文献带到关键的安全研究中，我们建议需要关注平台及其所做的技术工作，以了解数字技术如何新兴和塑造安全实践. 通过对 Google 的开源 ML 平台 TensorFlow 的详细研究以及对美国国防部算法战跨职能团队（Project Maven）的讨论，我们做出了两个更广泛的贡献。首先，我们确定了更广泛的军队“平台化”，我们将（技术材料）机器学习平台的日益参与和渗透称为基础设施，使整个军队能够进行分散和实验性算法开发的新实践。其次，我们描绘了这种平台化如何伴随着军队和企业领域参与者之间的新纠葛，尤其是在这方面发挥关键作用的大型科技公司，以及围绕这些平台组织的开源社区.