Beyond Quantity

Research with Subsymbolic AI

An exploration of the implications and effects of AI on scientific and academic practice.

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately?

Contributor: Andreas Sudmann (Edited by), Anna Echterholter (Edited by), Markus Ramsauer (Edited by), Fabian Retkowski (Edited by), Jens Schroter (Edited by), Alexander Waibel (Edited by)

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