Errant Intelligence
A Media Theory of Machine Learning
Versandkostenfrei!
Erscheint vorauss. 5. Juni 2026
162,99 €
inkl. MwSt.
Weitere Ausgaben:
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...
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.
