Metaphors surrounding artificial intelligence increasingly inform contemporary discussions on the topic, shaping both technical conceptualizations and cultural narratives. This paper explores literary metaphors by Jorge Luis Borges and Italo Calvino, arguing that their narratives offer critical conceptual frameworks for understanding deep learning’s representational structures. Drawing on five emblematic metaphors from Borges and Calvino—the Aleph (dimensional compression), the Library of Babel (combinatorial archive), the Chessboard (rule-based combination), the Atlas (emergent potential), and the Garden of Forking Paths (branching potentiality), this paper suggests that these literary metaphors reflect key phenomena within latent spaces and attention modules, such as the reduction of knowledge in one abstract point, dimensionality, and the combinatorial or potential logic. By aligning literary analysis with computational architectures, this paper highlights the significance of literary imagination in articulating deep learning’s capabilities and limitations, challenging simplified or anthropomorphic interpretations.
Schaerf et al. (Tue,) studied this question.