Article:
How machines see the world: Understanding image annotation

dc.creatorTreccani, Carloalberto
dc.date.accessioned2019-01-08T13:24:56Z
dc.date.available2019-01-08T13:24:56Z
dc.date.issued2018
dc.description.abstractMichael Baxandall, in PAINTING AND EXPERIENCE IN 15TH CENTURY ITALY (1972), shows the existence of a series of rules that painters of the 15th century were advised to follow. These ‘guidelines’ explained, for example, how each different hand position painted, within that cultural context, represented a different concept. These rules were rather rich and detailed and helped the painter maintain relevance in that historical and cultural context. Today, companies such as Amazon or Facebook are trying to teach machines and algorithms to see and understand what they see (image recognition). However, this process of signification, simple for a human being, is still complex for machines and algorithms. Hundreds of thousands of workers, therefore, are hired in order to label what they see. The workers are paid in pennies per image labelled and labor in precarious working conditions. This often leads to insufficient, poor or confusing labelling. Yet these ‘low quality’ labels are determining the way machines and algorithms see and understand the world. What are the consequences of a learning process that is confused, inaccurate, and qualitatively poor, in this unprecedented historical moment where there are more machines than human beings analysing and trying to make sense of what they see?en
dc.identifier.doi10.25969/mediarep/3425
dc.identifier.urihttps://necsus-ejms.org/how-machines-see-the-world-understanding-image-annotation/
dc.identifier.urihttps://mediarep.org/handle/doc/4194
dc.languageeng
dc.publisherAmsterdam University Press
dc.publisher.placeAmsterdam
dc.relation.isPartOfissn:2213-0217
dc.relation.ispartofseriesNECSUS. European Journal of Media Studies
dc.rightsCreative Commons Attribution Non Commercial No Derivatives 4.0 Generic
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectSehende
dc.subjectmaschinelles Sehende
dc.subjectalgorithmic visionen
dc.subjecthuman visionen
dc.subjectimage annotationen
dc.subjectmachine visionen
dc.subjectresolutionen
dc.subject.ddcddc:791
dc.titleHow machines see the world: Understanding image annotationen
dc.typearticle
dc.type.statuspublishedVersion
dcterms.bibliographicCitationTreccani, Carloalberto (2018): How machines see the world: Understanding image annotation. In: NECSUS. European Journal of Media Studies 7 (1), 235–254. DOI: http://dx.doi.org/10.25969/mediarep/3425.
dspace.entity.typeArticleen
local.coverpage2020-11-22T11:04:09
local.identifier.firstpublishedhttps://necsus-ejms.org/how-machines-see-the-world-understanding-image-annotation/
local.source.epage254
local.source.issue1
local.source.spage235
local.source.volume7

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