• DOI:10.1177/1461444819885334
• Corpus ID: 209363848

Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning

@article{Mhlhoff2020HumanaidedAI,
  title={Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning},
  author={Rainer M{\"u}hlhoff},
  journal={New Media \& Society},
  year={2020},
  volume={22},
  pages={1868 - 1884}
}

• Rainer Mühlhoff
• Published 6 November 2019
• Computer Science
• New Media & Society

Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI.” This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.