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Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge

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The Cultural Life of Machine Learning

Abstract

Since their introduction by Warren McCulloch and Walter Pitts in the 1940s, neural networks have evolved from an experimentally inclined model of the mind to a powerful machine learning architecture. While this transformation is generally narrated as a story of linear progress, this piece argues that what traverses neural networks’ historical and current forms is a shared adversarial epistemology whose emergence coincides with neural networks’ initial adoption by cyberneticists and whose legacy informs current research in artificial intelligence. Bridging McCulloch and Norbert Wiener’s concerns with the evils of knowledge to the growing literature on inputs called “adversarial examples,” this chapter revisits the history of neural networks from the perspective of a larger reformulation of knowledge into the product of efforts to overcome an assumed adversary.

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Authors and Affiliations

  1. Brown University, Providence, RI, USA

    Théo Lepage-Richer

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  1. Théo Lepage-Richer
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Correspondence to Théo Lepage-Richer .

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  1. Centre Urbanisation Culture Société, Institut national de la recherche scientifique, Quebec City, QC, Canada

    Jonathan Roberge

  2. Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, UK

    Michael Castelle

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Lepage-Richer, T. (2021). Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge. In: Roberge, J., Castelle, M. (eds) The Cultural Life of Machine Learning. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-56286-1_7

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