Danmaku provide public textual responses aligned to a video’s playback timeline. We examine danmaku on the eighteen prize-winning works of Bilibili’s first AI Video Creation Contest. We use within-work comment density to locate candidate moments for phenomenologically informed close reading, rather than to measure psychological absorption; five-second windows at or above each work’s 80th percentile define the primary sample, and a corpus inventory, sensitivity checks and a bilingual evidence log make the selection inspectable. In several selected intervals, appreciation of an image and explicit questions or remarks about its synthetic origin appear together: in a faux natural history documentary, viewers praise its familiar style and ask whether it is AI within the same five seconds. We call this juxtaposition doubled spectatorship and trace it through readings of astonishment at impossible images, uncanny likenesses, documentary texture and expressions of bodily response. The intervals also include technical fault-finding; the design neither shows simultaneous states in one viewer nor distinguishes AI video from CGI, animation or live-action video. The study contributes a documented sampling procedure and proposes, as a hypothesis for comparative and audience research, that origin-questioning in AI video may concern whether anything depicted was filmed at all.
Persistent online worlds occupy a marginal place in the history of the internet, yet they raise pressing questions about memory, obsolescence, and the conservation of born-digital works. This article takes as its case study Ouvroir, the island-museum built by Chris Marker and Max Moswitzer in Second Life in 2008, an object at once archive and ruin whose persistence depends on the maintenance fees paid to a proprietary platform. It asks how such a work can be documented when it cannot be conserved. Proceeding through an archaeological reading of its superimposed strata (governance, museum, archaeological imaginary, and temporality) and through a practice-based inventory conducted in situ with the tools of an ordinary avatar, the study documents the site by photographic capture, metadata extraction, dating, localisation, and interviews. It proposes two operative concepts, the “allegorithmic ruin” and the “living anarchive,” each defined by observable criteria. The inventory shows that the platform’s constraints determine the status of every artefact, that the historical core of Ouvroir can be read as a sealed technical layer, and that the category of “object” is an effect of assembly. It concludes that documentation, rather than conservation, offers a viable, iterative proto-methodology for the archaeology of virtual worlds.
This article examines the comparison between Alfred Hitchcock’s Vertigo and DeepDream through visual, narrative, and historical perspectives. It first considers formal affinities between DeepDream’s amplification of learned visual features and the perceptual construction of “Madeleine” in Vertigo, while stressing the functional and ontological differences between human perception and artificial neural networks. The analysis then turns to the film’s opening credits and nightmare sequence, asking how their treatment of the eye, fixation, light, and visual instability may be reconsidered in relation to contemporary research on vision. Rather than proposing any direct influence or shared genealogy, the article uses the work of William Grey Walter, David Hubel and Torsten Wiesel as a form of historical mediation. Some of this research was contemporary with Vertigo, while some would also, through substantial conceptual and technical transformations, contribute to the history of artificial visual-recognition systems. The comparison thus provides a way to historicize certain affinities between Vertigo and DeepDream while also clarifying the limits of their correspondence.
This Special Issue invited contributions that critically examine how exhibition practices are navigating the shifting space of materiality in an age defined by digital technology, algorithmic culture, and increasing socio-political instability [...]
In the contemporary realm of digital art, generative AI (GAI) systems, including text-to-image models, produce high-quality artworks within seconds, thereby disrupting the existing digital art ecosystem. This raises a pivotal question: can generative AI be regarded as an aesthetic subject? This inquiry represents a frontier issue situated at the crossroads of analytical aesthetics and neuroscience. An aesthetic subject is defined by the universal and non-utilitarian character of the emotions, pleasure, and judgments experienced during the aesthetic process. To determine whether generative AI qualifies as an aesthetic subject, reliance on mere intuition or technological observations is insufficient. Rather, it is necessary to revisit the notion of the “aesthetic subject” rooted in analytical philosophy and integrate it with recent advancements in neuroaesthetic evidence. This study examines the problem through seven lenses—aesthetic judgment; emotional internality; artistic imaginativity; meaning; artistic creativity; consciousness; and intentionality—while also comparing aesthetic functions with fundamental aesthetic distinctions. It provides a prudent evaluation of this matter. Specifically, the analysis proceeds from the simplest to the most complex components to determine their verifiability or falsifiability; subsequently, by integrating these findings, a comprehensive conclusion is drawn regarding the status of generative AI as an aesthetic subject. Adopting a stance of weak epistemology and utilizing the aforementioned theoretical framework, this paper concludes that, at present, generative artificial intelligence cannot be considered an aesthetic subject.
