770 Fotografie, Video, Computerkunst
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AI video generation models such as Open AI’s Sora make it possible to create ultra-realistic animations from scratch and seamlessly merge disparate visual content into new synthetic media. For its developers, however, Sora seems more than just a tool; they rather see it as a “world simulator.” But if Sora, trained on online video content scraped from YouTube or generated by game engines such as Unreal, is supposed to simulate a world, what world is it? As the short essay argues, it is a world of constant modulation and endless flows of patterns—a flat world, the world of platform capitalism.
The contribution by Pamela C. Scorzin discusses the intersection of artificial intelligence (AI) and the arts, focusing on whether AI can be creative or produce art. While AI lacks consciousness and common sense and cannot yet autonomously produce creative art, it can be used as a tool and medium by creatives to design, create, and realize projects. The author also considers the current convergence of robotics and AI, which is giving rise to humanoid robots that can behave like humans and imitate human expressions, interactions, and movements. Human-like robots are now being equipped with AI and incorporating GenAI or ChatGPT. This allows them to understand and respond to conversational cues and generate original outputs in their unique style. Some robot artists showcase their ‘creativity’ live on stage, sparking discussions about the essence of creativity and authorship.
This study explores the potential of generative AI, specifically Long Short-Term Memory (LSTM) networks, to advance collaborative choreographic composition within the framework of the Body Logic (BL) Method—a choreographic approach grounded in cognitive science designed to challenge inherited habits and practices in contemporary dance. Through five cognitive tasks that emphasize different movement types and their qualities, we investigate how LSTM networks recognize established movement patterns and innovate by combining them in novel ways, mirroring the processes of human creativity. Furthermore, we examine how LSTM-generated sequences, derived from learned data, convey expressive qualities through a variety of movements. The AI-generated movements closely follow the original movement trajectory but exhibit minor deviations attributable to the LSTM model’s inherent prediction uncertainty. These variations illustrate the model’s capability to introduce fresh elements while maintaining learned patterns, akin to human creativity. This research contributes novel perspectives on how technology can enrich artistic expression and challenge habitual decision-making in dance.
In light of the growing criticism and resistance mounted against digital technology and its proponents, the applied sciences need to adopt critical positions that draw from existing knowledge accumulated by current and historical critics and movements. Here, the contemporary abolitionist movement provides a particularly powerful framework to grasp the broader implications of technological development in the context of global racialized capitalism. This article proposes to adopt an abolitionist perspective in the applied sciences and to develop alternative modes of access to engineering and design based on a fundamental questioning and rejection of the established design paradigms developed under a neoliberal and capitalist status quo.
Text-to-image generative models offer an innovative method for creating visual content, exploiting the limitless potential of text-based inputs. However, the reliance on text prompts can lead to a labor-intensive process of experimentation for users aiming to achieve high-quality results. This has led to the development of a specialized prompt language with specific, descriptive keywords that users can exploit so as to achieve the best possible visual outcomes. Insight into prompting strategies can be obtained by analyzing the media generated and shared on text-to-image online platforms. Utilizing natural language processing (NLP) and visualization techniques, a detailed analysis of the prompts that led to the creation of images with exceptional popularity (by like count) was performed. The present study is focused on identifying the predominant topics and language patterns that contribute to the creation of images that receive high community ratings. Our analysis reveals a strong focus on surface aesthetics in prompts, which often emphasize conventional beauty and popular visual themes and a gravitation towards erotic and pornographic content. Positive prompts typically involve descriptions of female bodily features, blending elements of fantasy and realism. Negative prompts consistently counteract what seems to be perceived as visual imperfections, often describing body horror, marked by distorted human features as well as technical imperfections related to digital imagery in particular.
Psychedelic dreams, oddly-fingered hands, plausible yet disturbing alternative realities—generative AI systems have finally unleashed the weird. But in the process, haven’t they also normalized it? As a result, what we once considered weird is no longer so; instead, it has become a specific expression of classic kitsch. Let’s call it normie weird. If that’s the case, what happened to the real thing, what we could call weirdo weird, then? When one of the world’s leading tech companies releases a tool that produces crazy images reminiscent of a Max Ernst painting, something feels really strange. It’s not the images themselves, which we keep calling “surreal” to bring them back to something known and therefore reassuring. Instead, what’s truly weird might be cloaked in derivative, pictorial, ultimately visual aesthetics. It is behind a superficial layer of cute illustrations that lurks the non-human core of a sprawling statistical entity. This perspective confronts us with a haunting realization: that humanity itself is a kind of deep kitsch, and art, its greatest achievement, nothing but a shiny souvenir.
The question “What’s hidden in the hidden layers?” has been asked in artificial intelligence research since at least 1989. Likewise, today's answers to the challenges of deep learning remain the same as they were 35 years ago – from backpropagation to feature visualization and synthetic training data. Even the applications remain the same: nonlinear classification and self-driving cars. Once again, this reveals the surprising continuity of methods, applications, and rhetoric in the field of machine learning, that claims perpetual novelty, even though its roots stretch all the way back to cybernetics and its origins in control engineering. But the secrets of the hidden layers, it has become clear, are not only what is revealed by looking at their weights or their activation in response to particular inputs. This collection of texts explores the key themes and discussions of the second edition of the Hidden Layers conference, held at Köln International School of Design from June 12 to 15, 2024. Offering various perspectives on ‘hidden layers’, the conference was organized in two thematic tracks: “AI, Code & Material” and “AI, Society & (Visual) Culture.”
