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

Abstract

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.