A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Machine Learning and technoecological conditions of sensing
2019
A Peer-Reviewed Journal About
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
doi:10.7146/aprja.v8i1.115412
fatcat:7dhxrw5mgzchja7prtnl2f5vcy