Errant Intelligence

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A Media Theory for Machine Learning
A01=Clemens Apprich
AI
algorithmic bias
artificial intelligence
Author_Clemens Apprich
Category=JBCT
Category=NH
Clemens Apprich
computational epistemology
cybernetic systems
cybernetics
eq_bestseller
eq_history
eq_isMigrated=1
eq_isMigrated=2
eq_new_release
eq_nobargain
eq_non-fiction
eq_society-politics
Errant Intelligence
machine intelligence
machine learning
media archaeology
media theory
psychoanalytic theory technology
social dimensions of artificial intelligence
technological unconscious

Product details

  • ISBN 9789463723077
  • Weight: 470g
  • Dimensions: 156 x 234mm
  • Publication Date: 08 Jun 2026
  • Publisher: Amsterdam University Press
  • Publication City/Country: NL
  • Product Form: Hardback
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Artificial intelligence is often framed as a quest to replicate the human brain, promising frictionless cognition and a future of seamless automation. But what if this pervasive narrative obscures a deeper, more “errant” truth?

Errant Intelligence challenges the prevailing biological and individualistic interpretations of machine learning, arguing instead for a radical understanding of machine intelligence. The book embraces the deviations, inconsistencies, and “errant behaviour” as fundamental to the discovery of new knowledge, moving beyond the illusion of mere optimisation. Drawing on media theory, cybernetics, and a unique psychoanalytic lens, it explores the “technological unconscious” of machine learning. It traces the historical roots of AI, from early automatons and the Turing machine to natural language processing and contemporary machine learning systems. Challenging the idea of an autonomous, self-generating AI, the book exposes the hidden labour, assumed logics, and inherent biases that animate its operation. It re-evaluates computational thinking, insisting on its inherently social, collective, and symbolic character, and revealing how language and logical paradoxes are not obstacles but constitutive forces that shape intelligent machines.

Errant Intelligence offers a vital new framework for understanding the profound co-evolution of human and machine learning. It’s time to “unlearn” our assumptions and embrace the productive ambiguity and fallibility at the core of
machine intelligence.

Clemens Apprich is Professor of Media Theory and History at the University of Applied Arts Vienna, Austria. His current research focuses on computational cultures, particularly machine learning. He is the author of Technotopia: A Media Genealogy of Net Cultures (2017) and co-author of Pattern Discrimination (2019).

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