Objective
In 2022, the world appeared transformed by a new consumer artificial intelligence (AI), which has dazzled observers with the ability to generate text, images or music. This transformation has been brought about not by general AI but by deep learning, a specific subset of machine learning that has come to dominate AI scientifically and commercially. This has given rise to an emergent ‘deep culture’, which is produced with universal data extraction and on an industrial scale. While these developments have led to much public anxiety, we lack an understanding of the fundamental shift in cultural relations through deep learning, and what this means for cultural production and analysis.
DEEP CULTURE is the first project to develop ‘deep culture’ as an object of study as well as a method and capacity to attend to this historical moment. It claims that the relations between culture and deep learning can be reconfigured if humanistic ideas are included, which address the complexities of culture and values of difference. To this end, the project advances a radical interdisciplinary framework at the intersection of digital humanities, cultural studies and computer science. DEEP CULTURE has three main objectives: i) to develop a critical inquiry about deep culture through epistemic translations of keywords and practices in deep learning; ii) to advance methodologies for a critical inquiry with deep culture by reconfiguring current deep learning techniques and practices through humanistic ideas and values; iii) to promote productive, critical relationships with and beyond deep culture by collectively contesting deep learning’s cultural enactments and co-producing new ones with diverse publics. Empirically, the project is situated at three archival sites: historical, real-time and incidental archives.
Based in Amsterdam, DEEP CULTURE is led by the first Distinguished University Professor in AI and Humanities and an expert on interdisciplinary research collaborations.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
1012WX Amsterdam
Netherlands