Fellows

Natural Language Processing and Artificial Neural Networks: Cultures of Machine Translation

PROJECT DESCRIPTION

The aim of the project is to explore the media history of machine translation systems that are based on technologies of artificial neural networks (ANNs). The project traces three specific pathways. First, and most generally, it investigates machine translation systems with regard to their epistemological-technical conceptualization of the medium of language. Second, the project explores and critically analyzes the accessibility and interpretability of such systems, taking into account the fact that current AI discourses often treat ANNs as a fundamentally opaque technology. Finally, the project goes beyond understanding how specific AI systems enable the translation of languages to explore the way ANNs themselves can be perceived as technologies and infrastructures of translation.

CV

After having worked in Göttingen, Regensburg, Vienna, and Berlin, Andreas Sudmann currently teaches and researches as an adjunct professor (Privatdozent) of media studies at the Ruhr University Bochum, where he also completed his habilitation in 2016. Most recently, he held a guest professorship in media studies at the Philipps University in Marburg before taking up the research fellowship at the IFK in Vienna. Current research foci include media-theoretical and historical problems of artificial intelligence (AI), specifically, machine-learning methods, aesthetics and politics of popular and technical/digital visual media, forms and processes of seriality and documentary, media theory, and media critique.

Publications

gem. mit Christoph Engemann, Machine Learning. Medien, Infrastrukturen und Technologien der Künstlichen Intelligenz, Bielefeld 2018; „On the Media-political Dimension of Artificial Intelligence: Deep Learning as a Black Box and OpenAI“, in: Ramón Reichert, Mathias Fuchs (Hg.), Rethinking AI. Neural Networks, Biopolitics and the New Artificial Intelligence, Digital Culture & Society 4/1, 2018, S.181–200; „Deep Learning als dokumentarische Praxis“, in: Sprache und Literatur 48/2, 2017, S. 155–170.