Machine Feeling

14 January 2019, 09:30 - 16 January 2019, 17:30

Room S1 in the Alison Richard Building, Sidgwick Site tbc

These are closed workshops but there are  two related public talks, The Undetection of the New: An Overview of Machine Learning Limitations and their Social Impact and  A Feeling for Worlds: Encountering Animals as Sensors.  The talks are free to attend but places are limited and must be booked. Please register online on the relevant event pages.

Transmediale 2019 is a critical inquiry into new technologies of feeling, recognizing that digital culture has become instrumental for capturing and managing what Raymond Williams’ would once have called “structures of feeling”—referring to lived experiences and cultural expressions, distinct from supposedly fixed social products and institutions. Inspired by the festival theme, this research workshop focuses specifically on the domain of machine learning and on the ability of technologies to capture and structure feelings and experiences that are active, in flux, and in the present.

The term “structures of feeling” points to a material analysis of aesthetics and culture, including its technical and social forms, and in the way that this concept was originally employed as an acknowledgment of the importance of the hard to capture dimensions of everyday life. Styles, expressions, and sentiments are always in flux, yet Williams, and others after him, have with this term argued that they are grounded in cultural history and specific everyday situations. In developing a critical and analytic understanding we should therefore turn our attention to changes in language, style, aesthetics, and those social forms which are active in the present, but not yet fully formed or captured by a conceptual or scientific knowledge framework.

In this workshop we would like to further explore this line of thinking within the field of machine learning. For example, in the ways that automated experiences of seeing, hearing, and reading begin to produce knowledge through the capture of everyday styles, expressions, preferences, sentiments, and so forth—the very means that Williams alludes to.

If, in general, machine learning appears to lack an affective dimension, then in what ways are we to understand its resolute and concerted pursuit of this? What old registers of processing culture and organizing time, space and power does it build on? What potential new sensibilities and structures of feeling may arise in such normalized registers of our habits? What new cultural and social forms and practices emerge in the coming together of machine learning and structures of feeling?

For further information about this event please click here.