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The AI Detection Arms Race Is On

And college students are developing the weapons, quickly building to

EDWARD TIAN DIDN’T think of himself as a writer. As a computer science major at Princeton, he’d taken a couple of journalism classes, where he learned the basics of reporting, and his sunny affect and tinkerer’s curiosity endeared him to his teachers and classmates. But he describes his writing style at the time as “pretty bad”—formulaic and clunky. One of his journalism professors said that Tian was good at “pattern recognition,” which was helpful when producing news copy. So Tian was surprised when, sophomore year, he managed to secure a spot in John McPhee’s exclusive non-fiction writing seminar.

Every week, 16 students gathered to hear the legendary New Yorker writer dissect his craft. McPhee assigned exercises that forced them to think rigorously about words: Describe a piece of modern art on campus, or prune the Gettysburg Address for length. Using a projector and slides, McPhee shared hand-drawn diagrams that illustrated different ways he structured his own essays: a straight line, a triangle, a spiral. Tian remembers McPhee saying he couldn’t tell his students how to write, but he could at least help them find their own unique voice.

If McPhee stoked a romantic view of language in Tian, computer science offered a different perspective: language as statistics. During the pandemic, he’d taken a year off to work at the BBC and intern at Bellingcat, an open source journalism project, where he’d written code to detect Twitter bots. As a junior, he’d taken classes on machine learning and natural language processing. And in the fall of 2022, he began to work on his senior thesis about detecting the differences between AI-generated and human-written text.


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