Algorithmic Semiotics: The Textual Turn in AI Language Models


Introduction

The emergence of artificial intelligence in natural language processing has reignited philosophical debates about the nature of language and signification. Among the most relevant theoretical frameworks for understanding these developments is Jacques Derrida’s concept of arche-writing, which challenges the assumption that speech precedes and grounds writing. Traditional linguistic thought often privileges spoken language as the foundation of communication, viewing writing as a secondary system for storing and transmitting speech. However, Derrida argues that writing, as a broader system of inscription, fundamentally shapes all linguistic expression. The development of large language models (LLMs) provides compelling evidence for this claim, as these systems acquire linguistic competence exclusively through written text rather than through speech or social interaction. This article explores the intersection of Derrida’s deconstructive philosophy and the technological mechanisms underlying AI-driven language generation, revealing how these models exemplify the structural primacy of writing over speech.

The Priority of Writing in Language Acquisition

One of Derrida’s most radical interventions in linguistic philosophy was his critique of the assumed precedence of speech over writing. He argued that attempts to establish the primacy of spoken language inevitably rely on the very structures of writing they seek to subordinate. The notion that speech is more immediate or authentic than inscription presupposes a system of differentiation and reference—both of which are properties of writing itself. In this sense, Derrida deconstructs the binary opposition between speech and text, showing that all language operates within a network of signifiers that are always already inscribed.

The development of LLMs provides a striking technological validation of this perspective. Unlike human learners, who acquire language through interactive experiences involving speech and auditory input, these models develop linguistic competence solely through exposure to vast corpora of written text. Despite the absence of sensory and social context, LLMs demonstrate remarkable fluency in generating coherent discourse. This reliance on text suggests that language acquisition is not inherently tied to vocalization but can emerge from structured inscriptions and relational patterns. AI-driven language generation thus reinforces Derrida’s claim that writing is not a derivative system but the foundational infrastructure of linguistic meaning.

Algorithmic Semiotics and the Displacement of the Subject

Derrida’s insights extend beyond the question of speech and writing to a broader critique of meaning as dependent on human intentionality. Ferdinand de Saussure had already suggested that language is a system of differences rather than an inventory of fixed meanings. He dismissed the quest for linguistic origins, emphasizing that signification emerges relationally rather than from individual agency. Derrida radicalized this view by arguing that meaning is not produced by a conscious subject but unfolds within a shifting chain of signifiers.

The mechanisms underlying AI-driven semiotics illustrate this displacement of the human subject in meaning production. Machine learning models process language by identifying statistical relationships between textual elements rather than by referencing extralinguistic concepts. The significance of any given word or phrase is determined by its position within a corpus rather than by a speaker’s intent. This computational approach mirrors Derrida’s concept of différance—the endless deferral of meaning through a play of differences—since AI-generated text lacks an anchoring presence, instead existing within an ever-evolving system of relational probabilities.

The Practical and Philosophical Implications of Text-Based AI

The dominance of written text in AI training also has significant practical and epistemological implications. Unlike human cognition, which integrates multimodal sensory input, artificial intelligence is largely constrained to textual datasets. Training LLMs on spoken language presents formidable technical challenges, including the variability of pronunciation, contextual ambiguity, and the storage demands of large-scale audio processing. Consequently, AI research prioritizes text-based learning, reinforcing the notion that written language, with its stability and analyzability, serves as the most effective medium for linguistic modeling.

From a post-structuralist perspective, this reliance on text aligns with Derrida’s broader argument that writing structures human communication at its core. The apparent success of LLMs in generating sophisticated discourse without any exposure to spoken dialogue suggests that speech is not the fundamental mode of linguistic processing. Rather, inscription—the structured, iterative process of encoding and decoding symbols—constitutes the essence of signification. In this way, AI development inadvertently affirms Derrida’s assertion that writing, rather than being subordinate to speech, operates as the primary medium through which language functions.

Conclusion

The intersection of Derrida’s deconstruction and AI-driven language modeling provides a compelling framework for rethinking the nature of linguistic meaning. By demonstrating that language can be acquired and generated without recourse to speech or conscious interpretation, LLMs exemplify the primacy of writing as an autonomous system of signification. This challenges the long-standing assumption that human cognition is the necessary locus of meaning, suggesting instead that signification emerges through differential structures independent of subjective agency. In a world increasingly shaped by AI-generated discourse, Derrida’s insights remain as relevant as ever, urging us to reconsider the very foundations of linguistic philosophy in light of new technological realities.

Bibliography

Saussure, Ferdinand de. Course in General Linguistics. Edited by Charles Bally and Albert Sechehaye. Translated by Wade Baskin. New York:

Derrida, Jacques. Of Grammatology. Translated by Gayatri Chakravorty Spivak. Baltimore: Johns Hopkins University Press, 1976.

Derrida, Jacques. Writing and Difference. Translated by Alan Bass. Chicago: University of Chicago Press, 1978.

Coyne, Richard. Derrida on AI. Published June 1, 2024. https://richardcoyne.com/2024/06/01/derrida-on-ai-2/

Jurafsky, Daniel, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2023.

Bender, Emily M., and Alexander Koller. Linguistic Structure in AI: From Formal Syntax to Large Language Models. Cambridge: MIT Press, 2024.

Kommentare

Kommentar veröffentlichen

Beliebte Posts aus diesem Blog

The End of the Book and the Writing of the Future: Rereading Derrida’s 'Program'

Bild
Introduction For centuries, the book has been the emblem of knowledge, a repository of meaning, a space where thought is preserved. Yet, Jacques Derrida’s Of Grammatology opens with a striking provocation: "the end of the book." This declaration does not announce the disappearance of writing but rather signals a deeper shift—the challenging of a philosophical tradition that has privileged speech as the primary site of meaning. In "The Program," the first section of Of Grammatology , he lays the groundwork for his critique of logocentrism, exposing how writing has always been at the heart of signification, even when it was cast as a secondary system. At the time of his writing in the 1960s, cybernetics and information theory were emerging as transformative forces. Their implications, though only beginning to be understood, pointed toward the primacy of inscription over the metaphysics of presence —a development that has become even more evident in today's...
Mehr anzeigen

AI and the Priority of Writing: The Raise of Large Language Models (LLMs)

Bild
AI and the Priority of Writing   Introduction The rise of artificial intelligence, particularly large language models (LLMs), challenges long-standing assumptions about language, meaning, and human cognition. Unlike humans, who acquire language through embodied interaction, AI systems develop linguistic competence purely through textual data. This phenomenon invites a reexamination of the role of writing in meaning-making, aligning with Derrida’s concept of arche-writing —the idea that inscription precedes and exceeds speech. The efficiency of AI’s text-based training suggests that writing, rather than spoken language, is the foundation of signification. In this article, we explore how AI’s reliance on written language provides evidence for the primacy of inscription in the structure of meaning. AI and the Written Word Large language models like ChatGPT are trained exclusively on vast corpora of written text. They do not acquire language through sensory perception, auditory...
Mehr anzeigen