How Technológos "Responds" to What Used to Be Called "Images"

Q3 Arts and Humanities Nordic Journal of Aesthetics Pub Date : 2021-07-02 DOI:10.7146/nja.v30i61-62.127863
W. Ernst
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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”
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Technológos如何“回应”过去所谓的“图像”
将图像从其ANTHROPOCENTRIC定义中解放出来“传统上,我们认为图像是[…]界定的现象,以某种方式出现在人类的思维和感知装置中”(问卷)。问卷中的措辞已经具有指示性。当仔细观察光学生理学和认知图像感觉时,从“模拟”的模糊眼睛到几乎“数字”的信号计算大脑,1人类内部的图像处理实际上是一种“装置”的功能。西格蒙德·弗洛伊德在其《梦的解释》第七章中对精神“幻影”的非隐喻概念2明确地将成像的初步阶段与显微镜进行了比较,或摄影。3机械方法在神经图像感知电路模拟的原始基础研究中重新出现。4在Rosenblatt的计算感知器中,人类的“感知的思想和装置”(问卷)实际上成为了一种非人类机器,将“图像”从其生理人类中心主义中解放出来。5到目前为止,机器视觉,与人类的形象认知有着深刻的不同。但是,作为人工神经元网络输出的技术图像开始挑战和模仿人类的想象力潜力,一旦它们不仅通过人类标记来训练,而且(以更复杂的方式)通过与机器竞争来训练,这些机器被提供了来自“社交媒体”的大数据,几乎将视觉艺术的美学特性确定为空间中共存单元的平行感知(亚里士多德意义上的抽象),如今,“深度”机器学习发生在最初用于计算机图像处理的并行图形处理单元(GPU)中,这绝非巧合。人工智能并不是简单地模仿人类的图像感知(即使梵高喜欢绘画TECHNOLóGOS如何对过去被称为“图像”的东西做出“反应”。医学心理学对“关于图像形态变化的问卷”的回应
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来源期刊
Nordic Journal of Aesthetics
Nordic Journal of Aesthetics Arts and Humanities-Visual Arts and Performing Arts
CiteScore
0.20
自引率
0.00%
发文量
21
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