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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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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