Each week, the New Yorker magazine runs a cartoon contest where readers are invited to submit a caption for a cartoon. And each week, Bob Mankoff, cartoon editor of the magazine, and his staff sort through thousands of submissions to find the funniest entry. To speed this process up and further engage readers, Mankoff and the New Yorker enlisted the help of NEXT and its active learning algorithms to help choose the winner.

The adaptive data-collection algorithms in NEXT decide which New Yorker cartoon captions to ask participants to judge based on the observation that even after a small number of judgments, there are some captions that are clearly not funny. Consequently, our algorithms automatically stop requesting judgments for the unpromising entries and focus on trying out the ones that might get a laugh. With active learning algorithms like this, the winner can be determined from far fewer total judgments and with greater certainty than using standard crowdsourcing methods that collect an equal number of judgments for every caption (regardless of how good or bad). The New Yorker caption contest illustrates how NEXT puts advanced active machine learning tools into real-world application.

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  • A New Yorker video describing the results:
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