The"Lost Trace":The Generative AI Image and Knowledge Framework Reconstruction of Image Hermeneutics
The emergence of generative AI images poses significant challenges to the field of image hermeneutics.To reconstruct the knowledge framework of image hermeneutics in the era of ar-tificial intelligence,it is imperative that we engage in critical reflection on three fundamental propo-sitions within its knowledge context:"representation","symbolicity",and"intertextuality.As a semiotic mode,"representation"highlights the world's reliance on signs and exposes the linguistic foundations of image interpretation.Traditional image representation is characterized by transitivity,mimicry,and mirror-like qualities.However,generative AI images reject this representational basis of image existence and the language of image production.They are inherently intransitive,anti-rep-resentational,and generative in nature.The significance of generative AI,such as Sora as a world simulator,extends beyond its mere visual comprehension of the world.Its deeper implication lies in the construction of a"general image"of the world,thereby enabling the commensurability between the verbal image and the visual image.While language texts have traditionally provided a rich inter-textual context grounded in intertextuality,offering indispensable interpretive rules and anchoring systems for image hermeneutics,the advent of generative AI images signals a paradigm turn.It an-nounces the disappearance of co-texts and the collapse of the intertextual world,ultimately confining images to a desolate realm devoid of symbolic"traces".
generative AI imageimage hermeneuticsintertextualitygeneral imageSora
NETL
NSTL
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