ARTIFICIAL INTELLIGENCE AS A TRANSDUCTIVE OPERATOR

ARTIFICIAL INTELLIGENCE AS A TRANSDUCTIVE OPERATOR The Epistemic Crisis, Alignment, and the Irreversibility of Social Memory This study presents a relational interpretation of Generative Artificial Intelligence, proposing that Large Language Models and other generative systems can be understood as large-scale transductive operators rather than static repositories of knowledge or neutral mirrors of reality. Building upon the author's relational architecture and the Root Method (Método-Raiz), the study introduces transduction as the process through which latent informational configurations are transformed into observable manifestations under specific relational conditions. Within this framework, the model's latent representations constitute a condition of informational potential, the prompt establishes an initial relational condition, and the generated output represents the resulting manifestation of a computational transformation. The study develops this perspective through several interconnected propositions: AI as a Transductive Operator: Generative systems transform informational representations into textual, visual, and other manifestations through computational processes conditioned by prompts, model parameters, context, and system architecture. The Latent Space as Informational Latency: The internal representations of generative models are interpreted through the conceptual distinction between potentiality and manifestation. The Prompt as a Relational Condition: User input is examined as one of the conditions that participate in determining the trajectory of the generative process. Alignment as Relational Invariance: The alignment problem is reinterpreted through the proposed relationship between technical capacity and ethical governance, introducing the proportion of 1/2 as a conceptual reference for relational equilibrium. Transductive Irreversibility and Social Memory: The study examines how AI-mediated transformations can modify social practices, institutional arrangements, cognitive expectations, and collective epistemic conditions, even when the technologies responsible for those transformations are subsequently withdrawn. Epistemic Landscapes: A conceptual framework is proposed for governing the relational conditions under which AI-generated manifestations are produced, evaluated, validated, and incorporated into society. Epistemic Entropy: The study introduces this concept as a proposed interpretive framework for examining informational uncertainty and disorganization in AI-mediated environments. A central argument concerns the distinction between mathematical inversion and historical reversibility. Restoring a previous technological or institutional configuration does not necessarily restore the social relations, experiences, and accumulated memory that existed before a process of transformation. The study also establishes a conceptual dialogue between transduction and biological regulation, particularly through the analogy with epigenetic landscapes. This comparison is used to explore how regulatory conditions influence the transition from informational potential to observable manifestation, without asserting that biological and computational mechanisms are identical. The proposed framework is organized through the transductive sequence: RELATION → LATENCY → INTERACTION → TRANSDUCTION → STRUCTURE → MANIFESTATION → MEMORY → NEW RELATION This sequence provides the conceptual basis for examining how generative AI systems participate not only in the production of information but also in the transformation of the relational conditions that shape subsequent knowledge, decisions, and social interactions. The study does not present the proposed relational framework, the 1/2 equilibrium reference, or epistemic entropy as experimentally established universal laws. Rather, it develops them as conceptual and formal propositions within an interdisciplinary investigation of artificial intelligence, epistemology, social memory, and technological governance. Ultimately, the study proposes that the governance of Artificial Intelligence should extend beyond the control of isolated components and final outputs to encompass the relational architecture of the entire transductive process. Artificial Intelligence is examined as transduction in act: a process through which informational configurations become manifestations, manifestations modify social relations, and those modified relations establish the conditions for future transformations. The work contributes to the ongoing discussion on the philosophical foundations, epistemic implications, alignment challenges, and social governance of Generative Artificial Intelligence. Author: Cláudio Vicente da SilvaIndependent Researcher — Londrina, Paraná, BrazilDate: November 2026