Inside the Mind of an AI: Materiality and the Crisis of Representation

• Published in: New literary history 2023-06, Vol.54 (1), p.635-666
• Main Author: Hayles, N. Katherine
• Format: Article
• Language: English
• Subjects: Authorship; Back propagation; Children; Language; Literary criticism; Narratives; Natural language; Natural language processing; Neural networks; Novels
• ISSN: 0028-6087; 1080-661X; 1080-661X
• DOI: 10.1353/nlh.2022.a898324

The advent of Transformer architectures for neural nets and the high language proficiency of programs like GPT-3 confront us with a fundamental question. How can, or should, literary criticism proceed when the text's creator is not a human but a machine? The query shakes to its core literary criticism, and indeed the entire enterprise of critical inquiry. This essay confronts the issue head-on, arguing that it does matter whether language is produced by humans or AIs. It provides context for the development of neural nets that process natural language, looking at competitors to GPT-3 and similar Transformer novels. It then goes into depth on GPT-3's Transformer architecture and how it processes word sequences. It compares how the AI learns language with how human children learn it, arguing that the differences result in a systemic fragility of reference for AI's language understanding. It interrogates the null strategy of assuming there is no difference between human- and machine-generated text and explores its implications, arguing for a middle position between the program understanding nothing about meaning or everything. Finally, it offers four strategies for how literary criticism can engage with machine-generated language, arguing that machine narratives can be of significant interest in themselves. The point is not to ignore the powerful capabilities of GPT-3 to generate compelling narratives and texts, but rather to devise and deploy a new kind of literary criticism that can adequately interpret its complexities.