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WINNER OF THE ELIZABETH AGEE PRIZE IN AMERICAN LITERARY STUDIES

A philosophical and formally inventive inquiry into the experimental terrains of American speculative fiction, where meaning emerges through ambiguity, rupture, and critical refusal.

Dear Incomprehension: On American Speculative Fiction is a thought-provoking exploration of the fascinating field of contemporary speculative fiction, a genre that transcends boundaries and encompasses science fiction, fabulist tales, modern fairy tales, and experimental narratives that challenge the very essence of storytelling and reading.

Written as a philosophical essay that mirrors the unorthodox nature of the texts it analyzes, Dear Incomprehension examines American works that defy traditional narrative devices such as plot, character, logic, and even legibility. Drawing on a number of philosophical theories, among them object-oriented ontology and speculative realism, Vanderhaeghe argues that these unconventional works resist the standard tools of criticism, demanding instead a novel perspective and approach.

An in-depth resource for scholars and students of contemporary literature, philosophy, and digital literary studies, Dear Incomprehension offers a fresh framework for analyzing experimental fiction and alternative modes of meaning-making. It is a must-read for those seeking to grasp the complexities and nuances of American experimental and speculative fiction.

Computation has now been reconfigured by machine learning: those technical processes and operations that yoke together statistics and computer science to create artificial intelligence (AI) by furnishing vast datasets to learn tasks and predict outcomes. In DeepAesthetics, Anna Munster examines the range of more-than-human experiences this transformation has engendered and considers how those experiences can be qualitative as well as quantitative. Drawing on process philosophy, Munster approaches computational experience through its relations and operations. She combines deep learning—the subfield of machine learning that uses neural network architectures—and aesthetics to offer a way to understand the insensible and frequently imperceptible forms of nonlinear and continuously modulating statistical function. Attending to the domains and operations of image production, statistical racialization, AI conversational agents, and critical AI art, Munster analyzes how machine learning is operationally entangled with racialized, neurotypical, and cognitivist modes of producing knowledge and experience. She approaches machine learning as events through which a different sensibility registers, one in which AI is populated by oddness, disjunctions, and surprises, and where artful engagement with machine learning fosters indeterminate futures.