Expanding the Boundaries of Meaning: Culler, Uexküll, and AI in Semiotic Perspective

Introduction

Language does not merely label reality; it actively constructs it. Jonathan Culler, drawing on Saussure, argues that no single way of dividing or interpreting experience is inherently superior to another, as long as it enables individuals to navigate and flourish within their environment. Similarly, Uexküll’s Umwelt theory suggests that meaning is not exclusive to humans but is shaped by the perceptual and semiotic structures of different species. In the digital sphere, AI systems process data in ways that could be seen as a form of meaning-making, though fundamentally distinct from human and animal cognition. This article explores how these perspectives challenge traditional assumptions about interpretation and considers whether AI systems, in their structured responses to input, participate in semiosis.

Language as an Active Shaper of Meaning

Culler’s discussion of linguistic systems highlights that they do not simply reflect reality but impose their own structures upon it. His example of French fleuve and rivière versus English river and stream illustrates how different languages carve up conceptual space in distinct ways. Whereas English differentiates between river and stream primarily based on size, French distinguishes fleuve and rivière based on whether the body of water flows into the sea:

 “It is obvious that the sound sequences of fleuve and riviére are signifiers of French but not of English, whereas river and stream are English but not French. Less obviously but more significantly, the organization of the conceptual plane is also different in English and French. The signified 'river' is opposed to 'stream' solely in terms of size, whereas a 'fleuve' differs from a rivière not because it is necessarily larger but because it flows into the sea, while a 'rivière' does not. In short 'fleuve' and 'rivière' are not signifieds or concepts of English. They represent a different articulation of the conceptual plane” (Culler).

This suggests that the categories through which we understand the world are not universal but contingent upon linguistic frameworks. Rather than viewing these differences as arbitrary, Culler argues they are functional within their respective systems. Meaning, in this view, is not an objective property of the world but a product of structured distinctions that facilitate communication and thought.

Uexküll’s Umwelt and the Plurality of Meaning-Making

Jakob von Uexküll extends this notion beyond human language to biological cognition, proposing that each organism inhabits a unique Umwelt—a subjective perceptual world shaped by its sensory and semiotic capacities. A tick, for instance, perceives its environment not as a human does but through its own selective cues, such as temperature and chemical signals. This perspective implies that meaning is not an intrinsic feature of objects but emerges from the interactions between an organism and its surroundings. Just as linguistic categories frame human experience, an animal’s perceptual and cognitive structures determine how it constructs meaning from environmental signs. Uexküll’s insights challenge anthropocentric models of cognition, suggesting that meaning-making is not exclusive to humans but a widespread phenomenon across life forms, each adapted to its own ecological niche.

AI and the Cybernetics of Inscription

Artificial intelligence presents a distinct but related case of structured interpretation. Early cyberneticists such as Norbert Wiener and Warren McCulloch conceptualized cognition as a process of feedback and information processing, ideas that remain influential in contemporary AI research. Machine learning systems, particularly deep learning models, identify patterns and make predictions based on statistical correlations rather than human-like understanding. While AI does not exhibit intentionality or subjective experience, its ability to infer meaning from data can be likened to indexical semiotics, where a sign refers to something else based on correlation rather than symbolic convention. This computational inscription of meaning resonates with Derrida’s notion of arche-writing, in which meaning is generated through traces rather than intrinsic representation. However, whether AI genuinely participates in semiosis or merely mimics interpretive processes remains an open question.

Does Interpretation Require Subjectivity?

Peirce’s semiotic framework offers a broad definition of interpretation, one that does not necessarily require consciousness. In his model, signs acquire meaning through their role within a structured system of relations. If meaning arises from differential structures rather than subjective experience, AI’s ability to process signs in a structured manner might be considered a form of semiosis, albeit one distinct from human cognition. Nonetheless, key differences remain. Unlike biological organisms, AI does not engage with the world through embodied perception, nor does it possess the evolutionary history that shapes meaning-making in animals. While AI can detect and respond to patterns, the absence of intentionality and self-generated goals complicates claims that it interprets in the same way that humans or animals do. The extent to which AI meaning-making parallels or diverges from biological systems thus remains an open field of inquiry.

Conclusion

The perspectives of Culler and Uexküll challenge the idea that meaning is exclusive to human language, demonstrating that both linguistic and biological systems construct reality in ways suited to their needs. AI introduces a new dimension to this discussion, raising questions about whether structured data processing constitutes a form of interpretation. While AI lacks subjective experience, it nonetheless interacts with signs in a way that can be analyzed semiotically. This shift in perspective suggests that meaning is not a single, monolithic phenomenon but a spectrum of interpretive processes that vary across languages, species, and computational systems. Whether AI participates in semiosis in a meaningful sense remains a matter of debate, but its capacity to generate structured responses to input invites a reconsideration of the boundaries of meaning itself.

Bibliography

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