Unsupervised joke generation from big data [PDF], a paper by University of Edinburgh researchers Sasa Petrovic and David Matthews, describes an ingenious and successful method for teaching a computer to make up jokes like "I like my relationships like I like my source, open;" "I like my coffee like I like my war, cold;" and "I like my boys like I like my sectors, bad." The researchers wrote code that called on Google's n-gram database to find noun-attribute pairs, zero in on nouns with ambiguous meaning, and automatically generate jokes.

The problem in implementing such a model is in getting the necessary data. The word frequencies needed were gathered from Google's n-gram database, which was augmented by tagging words with their part of speech using Wordnet. This was then used to work out how often each noun occurred with the same attribute and the other statistics needed to apply the rules given above.

Next some human jokes, harvested from Twitter, were mixed in and a people were asked to rate the set as funny or not funny. Of the human jokes 33% were judged to be funny compared to the computer generated jokes of which 16% were funny. You could say that currently AI is half as funny as a human.

The joking doesn't stop there as the authors also couldn't resist naming their computation of the log likelihood of a joke as the LOcal Log-likelihood or LOL and when ranked according to LOL we get Rank OF Likelihood or ROLF. Hmmm.