Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning
| dc.contributor.author | Mühlhoff, Rainer | |
| dc.date.accessioned | 2021-01-21T10:27:42Z | |
| dc.date.available | 2021-01-21T10:27:42Z | |
| dc.date.issued | 2019-11-06 | |
| dc.date.updated | 2020-09-30T19:39:24Z | |
| dc.description | Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich. | de |
| dc.description | This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively. | en |
| dc.description.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. | en |
| dc.identifier.eissn | 1461-7315 | |
| dc.identifier.issn | 1461-4448 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/12510 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-11329 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.subject.ddc | 600 Technik, Technologie | de |
| dc.subject.other | artificial intelligence | en |
| dc.subject.other | audience labor | en |
| dc.subject.other | commercial content moderation | en |
| dc.subject.other | cybernetics | en |
| dc.subject.other | deep learning | en |
| dc.subject.other | human computation | en |
| dc.subject.other | human-computer interaction | en |
| dc.subject.other | social media | en |
| dc.subject.other | tracking | en |
| dc.subject.other | training data | en |
| dc.subject.other | user experience design | en |
| dc.title | Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1177/1461444819885334 | en |
| dcterms.bibliographicCitation.issue | 10 | en |
| dcterms.bibliographicCitation.journaltitle | New Media & Society | en |
| dcterms.bibliographicCitation.originalpublishername | SAGE | en |
| dcterms.bibliographicCitation.originalpublisherplace | London | en |
| dcterms.bibliographicCitation.pageend | 1884 | en |
| dcterms.bibliographicCitation.pagestart | 1868 | en |
| dcterms.bibliographicCitation.volume | 22 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 5 Verkehrs- und Maschinensysteme::Inst. Werkzeugmaschinen und Fabrikbetrieb::FG Wissensdynamik und Nachhaltigkeit in den Technikwissenschaften | de |
| tub.affiliation.faculty | Fak. 5 Verkehrs- und Maschinensysteme | de |
| tub.affiliation.group | FG Wissensdynamik und Nachhaltigkeit in den Technikwissenschaften | de |
| tub.affiliation.institute | Inst. Werkzeugmaschinen und Fabrikbetrieb | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |