Kategorie: IMAGE 37/2023

By Fabian Offert | What is the concept of history inherent in contemporary models of visual culture like CLIP and DALL·E 2? This essay argues that, counter to the corporate interests behind such models, any understanding of history facilitated by them must be heavily politicized. This, the essay contends, is a result of a significant technical dependency on traditional forms of (re-)mediation. Polemically, for CLIP and CLIP-dependent generative models, the recent past is literally black and white, and the distant past is actually made of marble. Moreover, proprietary models like DALL·E 2 are intentionally cut off from the historical record in multiple ways as they are supposed to remain politically neutral and culturally agnostic. One of the many consequences is a (visual) world in which, for instance, fascism can never return because it is, paradoxically at the same time, censored (we cannot talk about it), remediated (it is safely confined to a black-and-white media prison), and erased (from the historical record).

By Erwin Feyersinger, Lukas Kohmann; and Michael Pelzer | This explorative paper focuses on fuzziness of meaning and visual representation in connection with text prompts, image results, and the mapping between them by discussing the question: How does the fuzziness inherent in artificial intelligence-based text-to-image generators such as DALL·E 2, Midjourney, or Stable Diffusion influence creative processes of image production – and how can we grasp its mechanics from a theoretical perspective? In addressing these questions, we explore three connected interdisciplinary approaches: (1) Text-to-image generators give new relevance to Hegel’s notion of language as ‘the imagination which creates signs’. They reinforce how language itself inevitably acts as a meaning-transforming system and extend the formative dimension of language with a technology-driven facet. (2) From the perspective of speech act theory, we discuss this explorative interaction with an algorithm as performative utterances. (3) In further examining the pragmatic dimension of this interaction, we discuss the creative potential arising from the visual feedback loops it includes. Following this thought, we show that the fuzzy variety of images which DALL·E 2 presents in response to one and the same text prompt contributes to a highly accelerated form of externalized visual thinking.

By Nicolle Lamerichs | Generative AI, exemplified by tools like DALL·E, Midjourney, and Stable Diffusion, is gaining popularity and impacting various industries. This essay explores the rise of generative AI from a fan and media studies perspective, focusing on its reception within fandom. Fan cultures, driven by data and new media platforms, embrace generative art as a means to create transformative works based on beloved characters and stories. Platforms like Reddit foster communities where users share generative art and exchange tips. However, ethical concerns arise in fandom, including issues of copyright, monetization, and unauthorized use of fan art as training data. The essay analyzes how artists and stakeholders discuss and regulate generative AI within their communities, such as implementing bans on AI-generated art at fan conventions. While AI enables playful interactions and inspiring outcomes, users are critical of turning generative images into a business model. The essay highlights the potential of AI in empowering artistic practice but acknowledges concerns regarding its misuse. Fandom serves as a case study to explore user engagement with the innovative potential and challenges of generative AI.

By Konstantinos Michos | Recent advances in AI technology have enabled an unprecedented level of control over the processing of digital images. This breakthrough has sparked discussions about many potential issues, such as fake news, propaganda, the intellectual property of images, the protection of personal data, and possible threats to human creativity. Susan Sontag (2005 [1977]) recognized the strong causal relationship involved in the creation of photographs, upon which scientific images, rely to carry data (cf. Cromey 2012). First, this essay is going to present a brief overview of the AI image generative techniques and their status within the rest of computational methodologies employed in scientific imaging. Then it will outline their implementation in two specific examples: The Black Hole image (cf. Event Horizon Telescope Collaboration 2019a-f) and medical imagery (cf., e.g., Oren et al. 2020). Finally, conclusions will be drawn regarding the epistemic validity of AI images. Considering the exponential growth of available experimental data, scientists are expected to resort to AI methods to process it quickly. An overreliance on AI lacking proper ethics will not only result in academic fraud (cf. Gu et al. 2022; Wang et al. 2022) but will also expose an uninitiated public to images where a lack of sufficient explanation can shape distorted opinions about science.

By Pamela C. Scorzin | This paper examines the impact of AI on modern visual culture, focusing specifically on the design of AI avatars for social media, mixed reality, and the Metaverse. The term “AI imagery” encompasses a variety of AI-generated representations, including prompt engineering. Images produced by advanced AI generators such as Midjourney, DALL-E 2, and Stable Diffusion raise questions about their nature, reality, and connection to new body concepts and ideologies. As AI-generated images become more (photo-)realistic, their connection to reality and truth becomes less clear. Nevertheless, these synthetic images created from vast amounts of internet metadata are not considered fictional or unreal. Instead, they offer a unique perspective by revealing previously hidden information and sharing it through digital platforms. Consequently, generative images act as meta-images, representing a distinct form of reality in a simulated photo-realistic style (known as “promptography”) that effectively communicates with globally connected communities. Additionally, generative images also serve as operative images, creating a technology-based visual language within a vast platform network. As networked and meta-images, they are capable of constructing and narrating the ‘meta-human’.

By Jay David Bolter | As the essays in this collection demonstrate, AI generative imagery raises compelling theoretical and historical questions for media studies. One fruitful approach is to regard these AI systems as a medium rooted in the principle of remediation, because the AI models depend on vast numbers of samples of other media (painting, drawing, photography, and textual captions) scraped from the web. This algorithmic remediation is related to, but distinct from earlier forms of remix, such as hip-hop. To generate new images from the AI models, the user types in a textual prompt. The resulting text-image pairs constitute a kind of metapicture, as defined by William J.T. Mitchell in Picture Theory (1994).