Post-Automation Poetics; or, How Cold-War Computers Discovered Poetry
Bibliographic Data
| ID | 7579835 |
|---|---|
| Authors | Avery Slater (0000-0002-8700-7234, corresponding author) |
| Year | 2023 |
| Volume | 95 |
| Issue | 2 |
| Pages | 205-227 |
| Publication date | 2023-06-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | American Literature (JOURNAL) |
| Journal identifiers | ISSN: 0002-9831 • E-ISSN: 1527-2117 |
| Publisher | Duke University Press (PUBLISHER • US) |
| DOI | 10.1215/00029831-10575035 |
| OpenAlex | W4327746494 |
| Language | EN |
| Citations received | 3 |
| References cited | 12 |
This article examines early Cold War attempts to generate poetry using computers. Set between the end of World War II and the rise of personal computing, computer-generated poetry from this period was shaped not only by artists but also the university lab, the defense-contactor, and the corporation. Computer-generated poetry from this era often participated in the larger project of fostering public conception of the power and prestige of computers. This ethos of “post-automation poetics” was also informed by computer science experiments with computation’s linguistic-processing powers—from machine translation to early AI. This article contextualizes the computer poetry of Alison Knowles, Nanni Balestrini, and others within the scientific concerns of mathematicians like Theo Lutz and linguists like Margaret Masterman. Framed by governmental power, university funding, and corporate ambition, “post-automation poetics” engages with computation’s relevance to literary production: from Cold War mainframes to contemporary large language models (LLMs) like GPT-3
Art · Cold war · Ethos · Literature · Media studies · Poetics · Poetry · Political science · Politics · Power (physics) · Sociology · Art, Technology, and Culture · Artistic and Creative Research · Computer Science · Cybernetics and Technology in Society · History · Law
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |