Markus Maier

Whether Generative AI (GenAI) is creative is a question that has generated considerable philosophical debate yet remains unresolved. We argue that this impasse is not accidental but symptomatic of a wrong concep-tualization: The dominant question – is GenAI a creative agent? – presupposes that creativity is a property residing in a system. Instead, we propose to ask the question: what role does GenAI play as a medium of creative practice? This means that GenAI is neither a passive tool, nor an autonomous system, but a tech-nology that actively structures the possibility space of creative expression. We propose five “dimensions of creativity” – modal, expressive, disruptive, reflexive, and integrated – understood as a process in which each dimension generates a tension that the next takes up and transforms. Applying this framework to GenAI, we show that the creative process itself changes under the new technical conditions GenAI introduces, and that the central risk is a drift toward pure reproduction – the collapse of creative practice into the recombination of what the medium has already mapped. We argue that resisting this drift requires bringing the medium to its own limits: making the technological conditions of creative practice visible and transparent, thereby transforming reproductivity back into creativity.

Despite our full access to their internal structure, deep neural networks are epistemically opaque to us; we cannot extract meaningful explanations from existing micro-level descriptions. This paper examines whether complexity science – the systematic use of analogical mappings between models of complex systems – can address this opacity. We reconstruct the methodology of complexity science through the lens of structure-preserving analogies that transfer abstract relational features between source and target domains. Recent Physics of AI approaches that apply effective field theory and renormalization to DNNs exemplify this practice. By analyzing the relationship between analogical model construction and scientific explanation, we argue that complexity science can explain DNNs when analogies guide the development of higher-level models. Thus, our work provides a philosophical basis for evaluating how physical concepts might illuminate artificial intelligence systems. However, due to the heuristic value of such analogies, the resulting models must be validated theoretically and empirically on a case-by-case basis.

Each technology advances at its own pace, often indifferent to theoretical and philosophical-scientific conceptualizations. In the case of technologies like artificial intelligence (AI), and especially generative AI (GenAI), developments are so rapid that conceptual and epistemological reflections struggle to keep up, even at the level of basic definitions. Yet these definitions carry significant non-theoretical implications, including social, legal, and policy-related consequences. In this paper, I offer some reflections on the definition of GenAI proposed by Ronge et al. (2025), using it as an opportunity to highlight the most relevant aspects of the ongoing debate sparked by these new AI systems. In doing so, we will seek to explore the implications of generative AI systems in the context of human interaction, particularly in light of their role as active supports rather than passive tools. This shift, coupled with the increasingly anthropomorphic perception of their capabilities, sets GenAI systems apart from any previous technologies, not only in terms of technical features but also in how they are perceived by everyday users.

Erstmals 1969 veröffentlicht, wurde das Buch Perceptrons von Marvin Minsky und Seymour Papert auf die eine oder andere Weise über die Jahrzehnte immer wieder aufgegriffen, zitiert und zusammen mit dessen konnektionistischer Gegenposition als eine der großen Kontroversen der KI-Geschichte stilisiert. Es ist allerdings längst nicht eindeutig, inwieweit Inhalt und Auswirkung des Buches übereinstimmen. Dies lässt sich mithilfe der Carnap’schen Aussagenanalyse genauer beleuchten. Carnap beschreibt nach welchen Mechanismen Aussagen Inhalt und subjektive Vorstellungen vermischen und was es bedeutet, wenn eine Aussage nur scheinbar ein Problem behandelt. Wendet man diese Analyse auf Perceptrons an, lassen sich nicht nur die vergangene Kontroverse besser verstehen, sondern auch Rückschlüsse auf heutige KI-Debatten ziehen. Unser Beitrag eröffnet damit die Möglichkeit, gegenwärtige Grenzdiskussionen Künstlicher Intelligenz sachlich zu betrachten.

Die aktuellen Entwicklungen Künstlicher Intelligenz vermitteln den Eindruck grenzenloser Möglichkeiten. Sie strahlen dabei in alle gesellschaftlichen Bereiche aus und bestimmen unsere lebensweltlichen Praxen maßgeblich mit. Wie bei allen großen technologischen Transformationen stellt sich dabei die Frage nach deren zukünftigen Potentialen, umgekehrt aber auch nach ihren prinzipiellen Grenzen. In diesem Spannungsverhältnis kann philosophische Reflexion durch begriffliche Klarheit für Orientierung sorgen und einen (auf-)klärenden Beitrag leisten, welcher dabei hilft ernstzunehmende technische Möglichkeiten von unvernünftigen Spekulationen sinnvoll unterscheiden zu können. Der vorliegende Band trägt damit zu einem übergreifenden Diskurs über die Beurteilung von Möglichkeiten und Grenzen Künstlicher Intelligenz bei.

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT and the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis.

Künstliche Intelligenz ist eine der Schlüsseltechnologien des 21. Jahrhunderts. Sie gewinnt einen immer größeren Einfluss in allen gesellschaftlich relevanten Bereichen und transformiert diese Felder maßgeblich. Dementsprechend ist die Frage naheliegend, ob Künstliche Intelligenz auch eine zentrale Rolle für ein gelingendes Anthropozän spielen kann - und soll. Im folgenden Beitrag versuchen wir dieser Fragestellung in Bezug auf die damit (inhärent) erhobenen Geltungsansprüche nachzugehen. Dazu analysieren wir die beiden Begriffe Künstliche Intelligenz und Anthropozän und arbeiten deren Verhältnis zur technologischen Rationalität heraus. Abschließend versuchen wir anhand unserer Rekonstruktion zu zeigen, wie ein gelingendes Anthropozän aussehen sollte, wenn es seinem eigenen Anspruch gerecht werden will.

In their anthology Everything Flows: Towards a Processual Philosophy of Biology, Daniel J. Nicholson and John Dupré argue that modern theories of biology imply that the fundamental structure of reality is processual at its core. In the present work, I first examine the implicit and explicit metaphysical presuppositions the editors make in order to allow for such an inference from scientific theory to ontology. After showing the difficulties of a naïve transfer of theoretical entities to fundamental ontology, I argue that the editors can nevertheless extend their claims beyond the mere articulation of different domain ontologies. This leads to the idea of a scientifically informed induction base for an ontology of processes.