How Robots Learned to Write So Well

LITERARY THEORY FOR ROBOTS: How Computers Learned to Write, by Dennis Yi Tenen

In “Literary Theory for Robots,” Dennis Yi Tenen’s playful new book on artificial intelligence and how computers learned to write, one of his most potent examples arrives in the form of a tiny mistake.

Tenen draws links between modern-day chatbots, pulp-fiction plot generators, old-fashioned dictionaries and medieval prophecy wheels. Both the utopians (the robots will save us!) and the doomsayers (the robots will destroy us!) have it wrong, he argues. There will always be an irreducibly human aspect to language and learning — a crucial core of meaning that emerges not just from syntax but from experience. Without it, you just get the chatter of parrots, who, “according to Descartes in his ‘Mediations,’ merely repeated without understanding,” Tenen writes.

But Descartes didn’t write “Mediations”; Tenen must have meant “Meditations” — the missing “t” will slip past any spell-checker program because both words are perfectly legitimate. (The book’s index lists the title correctly.) This minuscule typo doesn’t have any bearing on Tenen’s argument; if anything, it bolsters the case he wants to make. Machines are becoming stronger and smarter, but we still decide what is meaningful. A human wrote this book. And, despite the robots in the title, it is meant for other humans to read.

Tenen, now a professor of English and comparative literature at Columbia, used to be a software engineer at Microsoft. He puts his disparate skill sets to use in a book that is surprising, funny and resolutely unintimidating, even as he smuggles in big questions about art, intelligence, technology and the future of labor. I suspect that the book’s small size — it’s under 160 pages — is part of the point. People are not indefatigable machines, relentlessly ingesting enormous volumes on enormous subjects. Tenen has figured out how to present a web of complex ideas at human scale.

To that end, he tells stories, starting with the 14th-century Arab scholar Ibn Khaldun, who chronicled the use of the prophecy wheel, and ending with a chapter on the 20th-century Russian mathematician Andrey Markov, whose probability analysis of letter sequences in Pushkin’s “Eugene Onegin” constituted a fundamental building block of generative A.I. (Regular players of the game Wordle intuit such probabilities all the time.) Tenen writes knowledgeably about the technological roadblocks that stymied earlier models of computer learning, before “the brute force required to process most everything published in the English language” was so readily available. He urges us to be alert. He also urges us not to panic.