You Wouldn’t Know Humor If It Bit You in the Algorithm!

Professional comedians can’t tell you exactly how they produce the “ha-ha effect.” Academics have tried to dissect the essence of humor, but to nobody’s great satisfaction. No one can say what exactly it is about a joke that causes people to roll in the aisles, as opposed to simply roll their eyes. 

Efforts to distill this craft down to a science have been, at best, laughable. That’s why I couldn’t help snickering when I came across this recent Dataversity article mentioning some branch of artificial intelligence (AI) that I’d never heard of: so-called “computational humor.” To assure myself that this was not simply The Onion messing with my mind, I checked out this Wikpedia page on the subject. 

Sadly, it would appear that automated pun generation and knock-knock joke detection are as far as this field has come in the 20-odd years since its founding. But more fundamentally, there’s a conceptual problem at the heart of computational efforts to reduce humor to deterministic algorithms. The issue with such algorithms is that they are simply rules. By contrast, humor is at its most effective when it breaks rules, such as when a joke ends with a clever punchline that was not at all what you were expecting, or a transgressive jolt that puts the listener off-balance. Algorithmic approaches to joke generation result in formulaic humor, which, pretty much by definition, isn’t funny.  And algorithmic approaches for detecting what might make people chuckle suffer from the same problem. 

If there can be no specific pattern for detecting the delicious twist that separates a brilliant quip from a lame crack, you simply can’t leave humor to the robots. Unless you yourself are a robot, and in which case, present company excluded. 

So let’s be honest with ourselves. It’s absurd to imagine that algorithms can automate, not just the process of creating things that have all the patterned formats characteristic of jokes, but the outcome of ensuring that those jokes are in fact funny. After all, nobody imagines that Pixar’s movies, which almost everybody agrees are consistently funny, are actually written, composed, directed, and voiced by algorithms. The “computer-generated imagery” upon which they’re all constructed is a compositional palette equivalent to a painter’s brushes and hues. 

Likewise, computers are integral to modern artistry in such fields such as music and graphics. But it would be crazy to attribute the quality of these works to the invisible algorithms inside the creators’ software tools, when it’s in fact the artists’ skills that make all the difference. 

Nevertheless, algorithms come in two varieties—deterministic (the variety I just shot down a moment ago) and statistical—and there is potential for the latter to facilitate automated detection, and possibly creation, of humorous language, images, and so forth. Though we may not be able to create hard-and-fast algorithmic rules for what’s funny, we may be able to predict what’s most likely to be regarded as funny by particular people in particular circumstances. 

In that regard, the aforementioned Dataversity article discusses a Virginia Tech research projects that uses statistical algorithms—specifically image recognition and machine learning—to distinguish humorous clip-art from that with no funny intent. But that doesn’t mean this process is entirely automated. Tellingly, the algorithms were trained from a database of 6,400 images that were rated for funniness by a legion of crowdsourced humans. 

One might apply this same crowdsourcing approach to the detection of humor in verbal statements. Statistical approaches to computational linguistics have proven themselves to some degree in the closely aligned field of automated sarcasm analysis, as I discussed in this InfoWorld column from July 2014.  The presence of sarcasm in somebody’s linguistic output often (but not always) flags the presence of a humorous intention. Or the sarcasm could be intended as a dagger of pure nastiness. But even those can be funny in particular circumstances, depending on whose mouth they’re spewing from. 

But why should we even try to automate any of this? That’s notably missing from much of the research, but it’s not hard to identify a few killer apps, at least on the humor-detection side of the equation. 

Clearly, a streaming service might be able to use algorithmic detection, ranking, and recommendations to put subscribers in touch with comedians, recordings, films, TV shows, e-books, and other works they might find especially amusing. 

An online humor marketplace might be able to match sellers of humorous materials with customers who get into whatever style, subject, or twisted outlook on life resonates most with them. Social graph analytics could probably play a useful role in matching the myriad fine-grained niches of taste in the vast universe of comedy geeks. 

And an online community might use statistical algorithms to automate detection of the most humorous member posts. This detection could be based both on some computational linguistic model of their intrinsic merit plus social ratings of how they’re regarded by other members. One use for such a capability might be to boost the visibility of the funnier members and thereby encourage them to keep up the good work. This would sort of be like having a perpetual “open mike night” in your online community. Giving these people some recognition might do wonders for boosting community morale, considering that funny people are a magnet in many social circumstances. 

But I’m still not sure that there’s any killer app for a joke-writing algorithm. Except maybe to laugh at for writing such crappy jokes. 

But, hey, that’s an app. So who am I to judge?