{"title":"How Technológos \"Responds\" to What Used to Be Called \"Images\"","authors":"W. Ernst","doi":"10.7146/nja.v30i61-62.127863","DOIUrl":null,"url":null,"abstract":"LIBERATING THE IMAGE FROM ITS ANTHROPOCENTRIC DEFINITION “Traditionally we think of images as [...] delimited phenomena that in one way or the other appear to the human mind and apparatus of perception” (Questionnaire). The choice of words in the Questionnaire is indicative already. When optical physiology and cognitive image sensation—from the “analogue” camera obscura-like eye to the almost “digital” signal-computing brain— is observed closely,1 image processing within the human turns out as, indeed, a function of an “apparatus.” Sigmund Freud’s nonmetaphorical concept of the psychic “Apparat” in chapter VII of his Interpretation of Dreams2 explicitly compares the preliminary stages of imaging to the microscope, or to photography.3 The mechanistic approach reemerged in protocybernetic research into the electrical circuit simulation of neural image perception.4 The human “mind and apparatus of perception” (Questionnaire) literally became a nonhuman machinery in Rosenblatt’s computational Perceptron, liberating the “image” from its physiological anthropocentrism.5 Machine vision, so far, stayed profoundly different from human image cognition. But technical images as outputs from Artificial Neuronal Nets start to challenge, and to emulate, the human imaginative potential, once they are not only trained by human tagging, but (in a more complex way) by rivalling machines among themselves which are fed with big data derived from “social media.” Just like Gottfried Ephraim Lessing, in his 1766 treatise Laokoon, had almost identified the aesthetic properties of the visual arts as parallel perception (aisthesis, in the Aristotelean sense) of coexistent units in space, today, it is no coincidence that “deep” machine learning takes place in parallel graphics processing units (GPUs) that were originally developed for image processing in computers. Artificial Intelligence does not simply mimick human image perception (even if Van Gogh-like paintings HOW TECHNOLÓGOS “RESPONDS” TO WHAT USED TO BE CALLED “IMAGES.” A MEDIA-ARCHAEOLOGICAL RESPONSE TO THE “QUESTIONNAIRE ON THE CHANGING ONTOLOGY OF THE IMAGE”","PeriodicalId":38858,"journal":{"name":"Nordic Journal of Aesthetics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nordic Journal of Aesthetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7146/nja.v30i61-62.127863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 0
Abstract
LIBERATING THE IMAGE FROM ITS ANTHROPOCENTRIC DEFINITION “Traditionally we think of images as [...] delimited phenomena that in one way or the other appear to the human mind and apparatus of perception” (Questionnaire). The choice of words in the Questionnaire is indicative already. When optical physiology and cognitive image sensation—from the “analogue” camera obscura-like eye to the almost “digital” signal-computing brain— is observed closely,1 image processing within the human turns out as, indeed, a function of an “apparatus.” Sigmund Freud’s nonmetaphorical concept of the psychic “Apparat” in chapter VII of his Interpretation of Dreams2 explicitly compares the preliminary stages of imaging to the microscope, or to photography.3 The mechanistic approach reemerged in protocybernetic research into the electrical circuit simulation of neural image perception.4 The human “mind and apparatus of perception” (Questionnaire) literally became a nonhuman machinery in Rosenblatt’s computational Perceptron, liberating the “image” from its physiological anthropocentrism.5 Machine vision, so far, stayed profoundly different from human image cognition. But technical images as outputs from Artificial Neuronal Nets start to challenge, and to emulate, the human imaginative potential, once they are not only trained by human tagging, but (in a more complex way) by rivalling machines among themselves which are fed with big data derived from “social media.” Just like Gottfried Ephraim Lessing, in his 1766 treatise Laokoon, had almost identified the aesthetic properties of the visual arts as parallel perception (aisthesis, in the Aristotelean sense) of coexistent units in space, today, it is no coincidence that “deep” machine learning takes place in parallel graphics processing units (GPUs) that were originally developed for image processing in computers. Artificial Intelligence does not simply mimick human image perception (even if Van Gogh-like paintings HOW TECHNOLÓGOS “RESPONDS” TO WHAT USED TO BE CALLED “IMAGES.” A MEDIA-ARCHAEOLOGICAL RESPONSE TO THE “QUESTIONNAIRE ON THE CHANGING ONTOLOGY OF THE IMAGE”