Meow Meow Meeeeeeoooooow: NaNoGenMo 2017

Every November since 1999, writers from around the world have observed National Novel Writing Month (NaNoWriMo). The challenge? Spend the month writing a novel of at least 50,000. This year alone, NaNoWriMo anticipates more than 400,000 participants. Past NaNoWriMo successes include Sara Gruen’s Water for Elephants, a 2006 bestseller that later became a movie featuring Robert Pattinson and Reese Witherspoon, and Erin Morgenstein’s 2011 The Night Circus, which won an Alex Award from the American Library Association in 2012.

The month of November, however, is not only celebrated by writers worldwide. During the same month, NaNoWriMo’s lesser-known and slightly deranged younger sibling also asserts itself. Proposed on a whim in 2013 by Internet artist Darius Kazemi, National Novel Generation Month (NaNoGenMo) has increasingly gained traction in the programming world. The challenge? Write a code that generates a novel of at least 50,000 words. ‘The “novel” is defined however you want’, Kazemi explains. ‘It could be 50,000 repetitions of the word “meow”. It could literally grab a random novel from Project Gutenberg. It doesn’t matter, as long as it’s 50k+ words’.

And before you ask – yes, someone has actually generated a series of novels comprising 50,000 repetitions of the word ‘meow’.

Computer generation of stories didn’t begin with NaNoGenMo. Indeed, new research reveals that the first known computer story generator was actually developed as early as the 1960s. Even earlier than that, around the 1840s, computer science pioneer Ada Lovelace was considering computational creativity, albeit in slightly different terms. In one popular quotation, for example Lovelace warns about ‘exaggerated ideas that might arise as to the powers of the Analytical Engine [general-purpose computer]’ imagined by her mentor Charles Babbage:

The Analytical Engine has no pretensions whatever to originate any thing. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with [italics original].

Stories produced for NaNoGenMo are the result of computer codes developed with their programmers’ intentions. These codes are usually highly specific: the generated text needs to adhere to at least some literary conventions so that it’s understandable and – if you’re lucky – readable.

As one commentator writes:

I think you’ll find that writing code that writes a book that is not-boring to read for the first few hundred words is not too difficult.

After those first few hundred words, though… well, all I can suggest is you download one of the completed novels (from this year or from any earlier year) and try to read the whole thing. The word “boring” does not quite do justice to the experience.

David Stark’s 2014 Moebius Octopus, for example, sounds promising enough: Stark describes the code as ‘mutating Moby Dick to be about sexy space amazons fighting octopodes through a word mapping.’ The generated output, though, quickly loses its novelty when once actually sits down and starts reading the convoluted text.

Some NaNoGenMo texts, though, are intended to be skimmed. One of the most well-known NaNoGenMo submissions, Nick Montfort’s 2013 World Clock recounts fictional events from around the world for each minute of a day. One section reads: ‘It is now almost 22:38 in Catamarca. In some homey dwelling a youth named Ephrem, who is rather large, reads a embossed certificate. He chews a fingernail.’ The text continues in this way. And, if you read it from beginning to end, it gets very boring, very quickly. Flip to any of World Clock’s pages and read a snippet, however, and it can actually be pretty thought-provoking.

Indeed, NaNoGenMo submissions are often better appreciated for the ideas driving generation, rather than the generated output itself. One could consider NaNoGenMo as an opportunity for programmers to explore conceptual literature by making their own. For example, a 2015 submission by Duncan Regan, The Cover of The Sun Also Rises, converts a photo Regan took of his copy of Hemingway’s The Sun Also Rises into a novel and audio book. The text begins: ‘Quartz. Davy’s grey. Purple taupe. Gray. Pale silver. Almond. Pastel gray. Pale silver. Ash grey. Ash grey. Manatee. Taupe gray. AuroMetalSaurus. Dark electric blue. Teal blue. Teal blue. Teal blue. Teal blue.’ 2014 saw the release of Greg Borenstein’s Generated Detective, a noir comic generator that pulls sentences from Project Gutenberg’s detective novel corpus and pairs them with a public domain image that is run through an application that transforms the photo into a comic book style. In 2016, NaNoGenMo included a ‘newspaper blackout poetry’ submission, the output of a program that scribbles out most of scanned pages save for select words that create new sentences. 2016 also saw NaNoGenMo as a university module assignment. All of this output restructures old text into new forms that allow us to consider what already exists with a fresh perspective. Channelling Ada Lovelace, one could argue that these NaNoGenMo codes have ‘no pretensions whatever to originate any thing… [their] province is to assist us in making available what we are already acquainted with.’

This small selection of NaNoGenMo submissions described over shows the diversity of NaNoGenMO submissions, but only represents a fraction of the ways in which computer storytelling systems can be applied. Some early posts about this year’s NaNoGenMo indicate that we’re in for an exciting month. We can expect a soap opera simulation, a Choose-Your-Own-Adventure novel, and a ‘Chatty Chess Engine’ that narrates in-depth analyses of the chessboard and potential moves, to list just a few.

Get excited, y’all. It looks like we’re for a weird ride come November. Join me in following the development of 2017 NaNoGenMo submissions here.

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Sunday Book-Thought 55

To make this essential point clear it helps to take an example used by Minsky and look at what is involved in understanding a piece of everyday equipment as simple as a chair. No piece of equipment makes sense by itself. The physical object which is a chair can be defined in isolation as a collection of atoms, or of wood or metal components, but such a description will not enable us to pick out chairs. What makes an object a chair is its function, and what makes possible its role as equipment for sitting it its place in a total practical context. This presupposes certain facts about human beings (fatigue, the ways the body bends), and a network of other culturally determined equipment (tables, floors, lamps), and skills (eating, writing, going to conferences, giving lectures, etc.). Chairs would not be equipment for sitting if our knees bent backwards like those of flamingos, or if we had no tables as in traditional Japan or the Australian bush. Anyone in our culture understands such things as how to sit on kitchen chairs, swivel chairs, folding chairs; and in arm chairs, rocking chairs, deck chairs, barber’s chairs, sedan chairs, dentist’s chairs, basket chairs, reclining chairs, wheel chairs, sling chairs, and beanbag chairs – as well as how to get out of them again. This ability presupposes a repertoire of bodily skills which may well be indefinitely large, since there seems to be an indefinitely large variety of chairs and of successful (graceful, comfortable, secure, posed, etc.) ways to sit in them. Moreover, understanding chairs also includes social skills such as being able to sit appropriately (sedately, demurely, naturally, casually, sloppily, provocatively, etc.) at dinners, interviews, desk jobs, lectures, auditions, concerts (intimate enough for there to be chairs rather than seats), and in waiting rooms, living rooms, bedrooms, courts, libraries, and bars (of the sort sporting chairs, not stools).
– Hubert L. Dreyfus, What Computers Still Can’t Do: A Critique of Artificial Reason (Cambridge, MA: The MIT Press, 1992; rep. 1994 [Fourth Printing]), pp. 36-37.