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Help a Computer Win the New Yorker Cartoon Caption Contest

This is a weekly experiment to see if an artificial intelligence program can produce real humor.

New here? Learn about the experiment. Each week, we showcase three AI-generated captions to readers (like you!). Based on whether the captions are deemed funny, we’ll tinker with the inputs (or change approaches completely) in hopes of generating better results.

🚧 Work In Progress 🚧

We’re working on next week’s approach, check back on Monday, June 21!

Get notified when this week’s approach is ready.

Each week we try a new approach!

Why are we doing this?

TLDR: We want to determine whether non-funny humans (The Pudding team), when aided by a computer, can produce better-than-average jokes.

And if you want, here’s the long version.

We don’t expect computers to replace our comedians anytime soon, and this project isn’t about proving that a computer, in isolation, can be funny.

Joke writing is difficult for the best comedians too: they refine their material over countless attempts in front of audiences of people. Comedians lament the effort that goes into producing one hour of stand-up, metaphorically called “The Gym” and literally “working out” material. Jokes that seemed brilliant on paper can fall flat in front of an audience. There is so much detail to consider and refine, like the perfect timing of a punchline. The work is a months-long feedback loop between a person and an audience—the laughs (and their duration)—are their inputs. They’re perfected in small gyms (tiny comedy clubs) before they’re ready for the big stage (Netflix).

To make people laugh, it isn’t as simple as telling a computer the rules of humor. This project keeps humans in the loop, to teach and nudge computers in the right direction—to ask the right questions to a machine that has infinite answers.

This project explores how humans can assist in the AI joke-writing process: can we turn a mediocre joke writers (The Pudding team) into a New Yorker cartoon-captioning champion, if aided by a computer that’s trained on all of humanity’s humor, knowledge, and references?

Created by Matt Daniels and Russell Goldenberg using GPT-3. Contributions from Pamela Mishkin.

Want to see another experiment with GPT-3? Check out Nothing Breaks Like A.I. Heart.