AI Art: The Semiotic Error as the Manifestation of Artificial Intelligence

This paper contends that the semiotic failure in generative art is not merely a technical anomaly to be optimized away, but a critical aesthetic event that exposes the internal mathematical constraints of non-human computational models. Bridging Donald Campbell’s blind-variation model with Viktor Shklovsky’s ostranenie and the Heideggerian phenomenology of tool-being, the study analyzes algorithmic error as a disruption that shifts generative systems from a state of transparent functionality into a mode of confrontational presence. Utilizing a methodology of technical–aesthetic reverse-engineering, a spectrum of contemporary artistic practices is examined to demonstrate how structural boundaries become legible when human linguistic codes fracture against the stochastic reality of deep neural networks. The research establishes a four-tier operational typology that conceptualizes the boundaries between normal stochastic variation, machine hallucination, technical glitches, and artistically induced errors. The analytical framework demonstrates that the authentic disclosure of the non-human parameters of artificial intelligence occurs not within the regimes of normative prompt execution, but through the tactical subversion and deactivation of the built-in optimization boundaries and platform filters engineered to constrain the algorithm. Within this critical threshold, visual anomalies offer a plausible structural interpretation of latent clipping failures, feature entanglement, and motion-vector degradation occurring when hyper-amplified Classifier-Free Guidance scales collide with non-congruent data matrices. Ultimately, the embrace of computational failure reconfigures the AI error from a technical deficit into an interpretive threshold, serving as a phenomenological index of operational friction that exposes the fragile, non-semantic structure of deep neural networks.