The New Yorker:

At the frontiers of knowledge, researchers are discovering that A.I. doesn’t just take prompts—it gives them, too, sparking new forms of creativity and collaboration.

By Dan Rockmore

Contrary to what many of my friends believe, good academics are always working—at least in the sense that when we’re stuck on a problem, which is most of the time, it’s impossible to leave it behind. A worthwhile problem is a brainworm: it stays with you until it’s resolved or replaced by another one. My Dartmouth colleague Luke Chang, a neuroscientist who studies what happens in people’s heads when we communicate, is no stranger to this affliction. One day, on a long drive back to Hanover, he found himself preoccupied with such a worm. The drive up I-89 is usually uneventful—a straight shot north, ideal for letting your mind off the leash. But Luke’s mind snagged on a technical challenge: how to turn a decent model of facial expression into something truly convincing. The aim was to encode the various nuanced ways human faces transmit states of mind, and then to visualize them; smiles and frowns are the barest beginning. The spectrum of human emotions and intentions is embodied in a range of expressions which serve as a basic alphabet for communication. He’d been trying to integrate facial “action unit” measurements into his software. But visualization was proving tricky.Instead of lifelike faces, his code kept spitting out cartoonish sketches. Every recent attempt had ended in disaster, and it was driving him crazy.

Years ago, Luke might have gnawed at the problem alone for the length of the drive. This time, he decided to hash it out with his newest collaborator: ChatGPT. For an hour, they talked. Luke broke down his model and described where things were going wrong. He floated questions, speculated about solutions. ChatGPT, as ever, was upbeat, inexhaustible, and, crucially, unfazed by failure. It made suggestions. It asked its own questions. Some avenues were promising; others were dead ends. We sometimes forget that the machine is less oracle than broad interlocutor. The exchange wasn’t quite spitballing; it was something more organized—human and machine feeling their way through the fog together. Eventually, ChatGPT suggested Luke look into a technique called “disentanglement,” a way of simplifying mathematical models that have grown unwieldy. The term triggered something in Luke. “And then it starts explaining it to me,” he recalled. “I’m, like, ‘Oh, that’s really interesting.’ Then I’m, like, ‘O.K., tell me more—conceptually, and, actually, how would I implement this disentanglement thing? Can you just write some code?’ ”

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