Description
In the industrial age, automation came for the shoemaker and the seamstress. Today, it has come for the writer, physician, programmer, and attorney.
Literary Theory for Robots reveals the hidden history of modern machine intelligence, taking readers on a spellbinding journey from medieval Arabic philosophy to visions of a universal language, past Hollywood fiction factories and missile defense systems trained on Russian folktales. In this provocative reflection on the shared pasts of literature and computer science, former Microsoft engineer and professor of comparative literature Dennis Yi Tenen provides crucial context for recent developments in AI, which holds important lessons for the future of humans living with smart technology.
Intelligence expressed through technology should not be mistaken for a magical genie, capable of self-directed thought or action. Rather, in highly original and effervescent prose with a generous dose of wit, Yi Tenen asks us to read past the artifice―to better perceive the mechanics of collaborative work. Something as simple as a spell-checker or a grammar-correction tool, embedded in every word-processor, represents the culmination of a shared human effort, spanning centuries.
Smart tools, like dictionaries and grammar books, have always accompanied the act of writing, thinking, and communicating. That these paper machines are now automated does not bring them to life. Nor can we cede agency over the creative process. With its masterful blend of history, technology, and philosophy, Yi Tenen’s work ultimately urges us to view AI as a matter of labor history, celebrating the long-standing cooperation between authors and engineers.
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Publisher : W. W. Norton & Company
Publication date : February 6, 2024
Language : English
Print length : 176 pages
ISBN-10 : 0393882187
ISBN-13 : 978-0393882186
Item Weight : 2.31 pounds
Dimensions : 5.8 x 0.7 x 8.6 inches
Best Sellers Rank: #1,098,512 in Books (See Top 100 in Books) #652 in History of Technology #1,067 in Linguistics Reference #1,662 in Artificial Intelligence & Semantics
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