Machine Learning and technoecological conditions of sensing

Abstract

In what way can machine learning be understood as a computational mode of sensing? How does the practice of making sense take place in the context of developing machine learning applications? What assumptions and conflicts are constitutive for that very process of sensing? Bringing case studies from machine learning into conversation with theoretical work primarily by Erich Hörl, Luciana Parisi, Wendy Hui Kyong Chun and Karen Barad, this article reflects on the re-configuration of sense in the course of the expansion of media-technology. It questions how computational expressions become relatable as well as the mechanisms for encapsulating the capacity of sensing for determining purposes.