As verbal interaction between humans and computers becomes more prominent in daily life — from Siri, Apple’s voice-activated assistant technology, to speech-based search engines to fully automated call centers — demand has grown for “social computers” that can communicate with humans in a natural way.

Teaching computers to grapple with humor is a key part of this equation, author Alex Stone writes in The New York Times Sunday Review.

To understand humor, computers need to contend with linguistic sleights like irony, sarcasm, metaphor, idiom and allegory — things that don’t readily translate into ones and zeros, and that are context-dependent.

To get around that cognitive complexity, computational humor researchers have by and large taken a more concrete approach: focusing on simple linguistic relationships, like double meanings, rather than on trying to model the high-level mental mechanics that underlie humor.

Standup (for System to Augment Non-Speakers’ Dialogue Using Puns), a program that generates punning riddles to help kids with language disabilities increase their verbal skills, The Standup program, engineered by a team of computer scientists in Scotland, is one of the more successful efforts.

Standup writes jokes by searching through a “lexical database.”

Another tack has been to apply machine-learning algorithms, which crunch mountains of data to identify statistical features that can be used to classify text as funny or unfunny.

Future error message? “Oops I just crashed! Call Geico. Ha ha ha.” Hope not. — Ed.

H/T: Peter Christiansen