(NaNoGenMo: Dada 2.0) 2.0

[tra38]

Deciding to take my suggestion in "Fake press coverage of NaNoGenMo" (#159) to its ultimate conclusion, I took this article (http://arcade.stanford.edu/blogs/nanogenmo-dada-20 ) and rearranged its paragraph order 50 times, thereby producing a 79300-word long epic. Many of the generated articles don't seem to work, but there are probably a few gems that are worth reading.

Source Code: https://gist.github.com/tra38/cf8226a55c5a49531f57
Full Text: https://gist.github.com/tra38/fb081ddcc1a662273d4a

My belief that it should be possible to generate a bunch of nonsense, look through the nonsense to find something meaningful, and then copy and paste. Turns out it was much harder than expected, and required me to look at more articles than I intended. But I did find a gem, luckily, and that probably encouraged me to post this issue.

This following 'article' takes a completely different tone from the original article (NaNoGenMo as being Dadism 2.0), simply treating NaNoGenMo as nothing more than a standard NLG research project that is only limited by technological constraints.

NLG algorithms are generally considered to be a form of “artificial” or “machine intelligence” because they do things—like write news articles about sports or the weather, or write real estate ads, as the prototype my Fast Forward Labs colleagues built—we believe humans alone can do. (I hope to explore the implications of the historical, relativist concept of artificial intelligence, espoused by people like Nancy Fulda, in a separate post.) As illustrated in the WSJ article, most people then evaluate NLG performance like André Bazin evaluates style in realism: as the art of realism lies in the seeming absence of artifice, so too does the art of algorithms lie in the seeming absence of automation. Commercialization only enhances this push towards verisimilitude, as investment banks and news agencies like Forbes won’t pay top dollar for software that generates strange prose. In turn, we come to judge machine intelligence by its humanness, orienting development offers towards writing prose that we would have written ourselves.

While open to anyone and, as in NaNoWriMo, governed by the single constraint that submissions contain at least 50,000 words, NaNoGenMo is gradually defining itself as a cohesive artistic movement that uses algorithms to experiment with literary form. The group’s identity is partly generated by ressentiment towards negative criticism that their “disjointed, robotic scripts” are “unlikely to trouble Booker judges.” Last year, one participant mocked how “futile it is to try to explain what we’re actually doing here, to the normals.” More positively, they are shaping identity through shared formal and critical resources. John Ohno (alias enkiv2) posted code to generate sestinas, haikus, and synonyms. Allison Parrish (alias aparrish) shared an interface to the Carnegie Mellon Pronouncing Dictionary that enables users to do things like scrape the dictionary for rhymes for a given word. Finally, Isaac Karth (alias ikarth) explained to members how the group’s tendency to assemble new poetry from prior texts has intellectual roots in Dadaism, Burrough’s cut-up techniques, and the constraint-oriented works of Oulipo. When I spoke with Kazemi about the project, he said that Ken Goldsmith’s Uncreative Writing had inspired his thinking on how NaNoGenMo can challenge customary notions of authorship and creativity.

What if machines generated text with different stylistic goals? Or rather, what if we evaluated machine intelligence not by its humanness but by its alienness, by its ability to generate something beyond what we could have created—or would have thought to create—without the assistance of an algorithm? What if automated prose could rupture our automatized perceptions, as Shklovsky described poetry in Art as Device, and offer a new vehicle for our own creativity?

The latest developments in machine learning are enabling machines to develop models of us in turn, ever updating what information they present and how they present it to match the input we provide. Kazemi is addressing this new give and take between man and machine head on in his 2015 NaNoGenMo submission, “co-authoring” a novel with an algorithm where for every ten sentences the algorithm drafts, he only commits the one he, as human, likes best. “Who wrote the book?” he asks. “[The algorithm] wrote literally every word, but [I] dictated nearly the entire form of the novel.” This is the same kind of dynamic new research tools built on IBM Watson are presenting to lawyers and doctors: ROSS, a legal tool built on the Watson API, presents answers to research questions, and all the lawyer has to do is to commit the answer she likes best. If NaNoGenMo helps us think more deeply about that dynamic, it can offer very important insights on the overall future of AI.

Moniker, a design studio based in Amsterdam, wrote a simple query that scans Twitter for sentences in the form “it’s + hour + : + minute + am/PM + and +” to compose a realtime global diary of daily activities. The “it’s hour and I am” tends to elicit predictable confessions or complaints, showing how expressions automate our thoughts: “It’s 12:20 and I need a drink;” “It’s 1:00 pm and I have not moved from my bed;” “It’s 11:00 pm and I’ve finally got a decent cup of coffee.” Twide and Twejudice replaces most of the dialogue in Austen’s original with a word used in a similar context on Twitter, resulting in frivolous dialogue: (Mr Bennet asking Mrs Bennet about Mr Bingley:) "Is he/she overrun 0r single?” (Mrs Bennet exclaiming about Mr Bingley's arrival:) "What _a fineee thingi 4my rageaholics girls!'' While these lack the sophistication of The Seeker, by polluting Austen with Twitter diction, they illustrate how contemporary media have modified communication norms.

Allison Parrish’s I Waded in Clear Water uses sentiment analysis algorithms, which rank sentences based upon features that indicate emotional texture, to transform Gustavus Hindman Miller’s Ten Thousand Dreams, Interpreted. Parrish mobilizes the formulaic “action” and “denotation” structure of Miller’s text (action = “To see an oak full of acorns”; denotation = “denotes increase and promotion”). She first transforms the actions into first-person, simple past sentences (“I saw an oak full of acorns”) and then reorders the sentences from the worst to the best thing that can happen in dreams, according to a score given by a sentiment analysis algorithm run on the denotation. The sentiment scores create short chapters: “I drove into muddy water. I saw others weeding.”; and longer chapters with paratactic strings of disjointed actions: “…I descended a ladder. I saw any one lame. I saw my lover taking laudanum through disappointment. I heard mocking laughter. I kept a ledge. I had lice on my body. I saw. I lost it. I felt melancholy over any event. I saw others melancholy. I sent a message….” According to the sentiment algorithm, wading in clear water is our best dream.

Evaluating these works by their capacity to read like human prose is a stale exercise because what qualifies as “natural” language is relative, not absolute. Our own linguistic habits are developed through interaction with others, be they members of a given social class, colleagues at work or school, or spambots littering our Twitter feeds. In a recent Medium post, Katie Rose Pipkin eloquently described how machines have already modified what we think of as natural language, whether we're cognizant of it or not. We speak differently to search tools and virtual assistants because we have come to develop a tacit understanding of how they work and can modify our requests to communicate effectively.

At least two 2014 submissions use dreams as a locus to explore the odd beauty of machine intelligence. Thricedotted’s The Seeker relates the autobiography of a machine trying to “learn about human behavior by reading WikiHow.” The work is visually beautiful, with each iteration of the algorithm’s operations punctuated by pages that raindrop abstractions and house aphorisms like “imagine not one thing could be undirected.” Like the hopscotch overtones in Cortázar’s Rayuela, the aphorisms encourage the reader to perceive meaningful patterns in what might otherwise be random data (Thricedotted’s internet identity often mentions apophenia). Time and again, the algorithm repeats a “work, scan, imagine” loop, scraping WikiHow, searching plain text memories for a concept encountered during “work,” and building a dream sequence—or “univision”—from concepts it doesn’t recognize. These univisions contain the most surprising poetry in the work, where beauty arises from the reader’s ineluctable tendency to feel meaning in fragments.

Earlier this year, the Wall Street Journal (WSJ) published an article with an interactive sidebar featuring excerpts from financial investment research reports. Readers were prompted to identify whether the excerpts were written by robots or humans. Admittedly, Wall Street’s preference for terse prose over poetic flourish makes a challenge like this make sense. “Q2 cash balance expectation of $830m implies ~$80m of cash burn in Q2 after a $140m reduction in cash balance in Q1,” a sampled sentence, is effectively just three data points fused together with syntax. And that’s no coincidence. White collar workers like journalists need not fear their job security (at least not yet…) because new natural language generation (NLG) algorithms are very good at representing structured data sets in prose, but not yet very good at much else. That capability in itself is very powerful, as our ability to draw insights from data often depends on how they are presented (e.g. a chart reveals insights one would have missed in rows and columns). But it is a far cry from the creative courage required to build a world on a blank page.

Technical constraints explain why NaNoGenMo has come to align itself with poetics of recontextualization and reassembly. Indeed, genuine NLG algorithms, that is, those that can build words and syntax from the building blocks of letters and get smarter over time, are still very nascent. Most of the 2014 submissions instead use rules to transform former texts in creative ways, which also leads to topical similarities.

It is this search to use automation as a vehicle for defamiliarization that makes NaNoGenMo so exciting. Darius Kazemi, an internet artist who runs an annual Bot Summit, created NaNoGenMo “on a whim” in November, 2013. Thoughtful about literary form, Kazemi was amused by the fact that National Novel Writing Month (NaNoWriMo) set only two criteria for participants: submissions must be written in 30 days (the month of November) and must comprise at least 50,000 words. The absence of form invited experimentation: why write a novel when you can write an algorithm that writes an novel? He tweeted his idea, and a new GitHub (a web-based software development collaboration tool) community was formed.

[ikarth]

And thus we come full circle.

[kathrynhume]

Well this is delightful! Couldn't help but think of Pierre Menard, Author of the Quixote. May write a follow up piece about the results of 2015, including the recursion.

[enkiv2]

Hahaha. I like your version better than mine :P

On Sun, Nov 29, 2015 at 10:30 PM Tariq Ali [email protected] wrote:

Deciding to take my suggestion in "Fake press coverage of NaNoGenMo" (#9
#9) to its ultimate
conclusion, I took this article (
http://arcade.stanford.edu/blogs/nanogenmo-dada-20 ) and rearranged its
paragraph order 50 times, thereby producing a 79300-word long epic. Many of
the generated articles don't seem to work, but there are probably a few
gems that are worth reading.

Source Code: https://gist.github.com/tra38/cf8226a55c5a49531f57
Full Text: https://gist.github.com/tra38/fb081ddcc1a662273d4a

This following 'article' takes a completely different tone from the
original article (NaNoGenMo being Dadism 2.0), simply treating NaNoGenMo as
nothing more than a standard NLG research project that is only limited by
technological constraints.

NLG algorithms are generally considered to be a form of “artificial” or
“machine intelligence” because they do things—like write news articles
about sports or the weather, or write real estate ads, as the prototype my
Fast Forward Labs colleagues built—we believe humans alone can do. (I hope
to explore the implications of the historical, relativist concept of
artificial intelligence, espoused by people like Nancy Fulda, in a separate
post.) As illustrated in the WSJ article, most people then evaluate NLG
performance like André Bazin evaluates style in realism: as the art of
realism lies in the seeming absence of artifice, so too does the art of
algorithms lie in the seeming absence of automation. Commercialization only
enhances this push towards verisimilitude, as investment banks and news
agencies like Forbes won’t pay top dollar for software that generates
strange prose. In turn, we come to judge machine intelligence by its
humanness, orienting development offers towards writing prose that we would
have written ourselves.

While open to anyone and, as in NaNoWriMo, governed by the single
constraint that submissions contain at least 50,000 words, NaNoGenMo is
gradually defining itself as a cohesive artistic movement that uses
algorithms to experiment with literary form. The group’s identity is partly
generated by ressentiment towards negative criticism that their
“disjointed, robotic scripts” are “unlikely to trouble Booker judges.” Last
year, one participant mocked how “futile it is to try to explain what we’re
actually doing here, to the normals.” More positively, they are shaping
identity through shared formal and critical resources. John Ohno (alias
enkiv2) posted code to generate sestinas, haikus, and synonyms. Allison
Parrish (alias aparrish) shared an interface to the Carnegie Mellon
Pronouncing Dictionary that enables users to do things like scrape the
dictionary for rhymes for a given word. Finally, Isaac Karth (alias ikarth)
explained to members how the group’s tendency to assemble new poetry from
prior texts has intellectual roots in Dadaism, Burrough’s cut-up
techniques, and the constraint-oriented works of Oulipo. When I spoke with
Kazemi about the project, he said that Ken Goldsmith’s Uncreative Writing
had inspired his thinking on how NaNoGenMo can challenge customary notions
of authorship and creativity.

What if machines generated text with different stylistic goals? Or rather,
what if we evaluated machine intelligence not by its humanness but by its
alienness, by its ability to generate something beyond what we could have
created—or would have thought to create—without the assistance of an
algorithm? What if automated prose could rupture our automatized
perceptions, as Shklovsky described poetry in Art as Device, and offer a
new vehicle for our own creativity?

The latest developments in machine learning are enabling machines to
develop models of us in turn, ever updating what information they present
and how they present it to match the input we provide. Kazemi is addressing
this new give and take between man and machine head on in his 2015
NaNoGenMo submission, “co-authoring” a novel with an algorithm where for
every ten sentences the algorithm drafts, he only commits the one he, as
human, likes best. “Who wrote the book?” he asks. “[The algorithm] wrote
literally every word, but [I] dictated nearly the entire form of the
novel.” This is the same kind of dynamic new research tools built on IBM
Watson are presenting to lawyers and doctors: ROSS, a legal tool built on
the Watson API, presents answers to research questions, and all the lawyer
has to do is to commit the answer she likes best. If NaNoGenMo helps us
think more deeply about that dynamic, it can offer very important insights
on the overall future of AI.

Moniker, a design studio based in Amsterdam, wrote a simple query that
scans Twitter for sentences in the form “it’s + hour + : + minute + am/PM +
and +” to compose a realtime global diary of daily activities. The “it’s
hour and I am” tends to elicit predictable confessions or complaints,
showing how expressions automate our thoughts: “It’s 12:20 and I need a
drink;” “It’s 1:00 pm and I have not moved from my bed;” “It’s 11:00 pm and
I’ve finally got a decent cup of coffee.” Twide and Twejudice replaces most
of the dialogue in Austen’s original with a word used in a similar context
on Twitter, resulting in frivolous dialogue: (Mr Bennet asking Mrs Bennet
about Mr Bingley:) "Is he/she overrun 0r single?” (Mrs Bennet exclaiming
about Mr Bingley's arrival:) "What _a fineee thingi 4my rageaholics
girls!'' While these lack the sophistication of The Seeker, by polluting
Austen with Twitter diction, they illustrate how contemporary media have
modified communication norms.

Allison Parrish’s I Waded in Clear Water uses sentiment analysis
algorithms, which rank sentences based upon features that indicate
emotional texture, to transform Gustavus Hindman Miller’s Ten Thousand
Dreams, Interpreted. Parrish mobilizes the formulaic “action” and
“denotation” structure of Miller’s text (action = “To see an oak full of
acorns”; denotation = “denotes increase and promotion”). She first
transforms the actions into first-person, simple past sentences (“I saw an
oak full of acorns”) and then reorders the sentences from the worst to the
best thing that can happen in dreams, according to a score given by a
sentiment analysis algorithm run on the denotation. The sentiment scores
create short chapters: “I drove into muddy water. I saw others weeding.”;
and longer chapters with paratactic strings of disjointed actions: “…I
descended a ladder. I saw any one lame. I saw my lover taking laudanum
through disappointment. I heard mocking laughter. I kept a ledge. I had
lice on my body. I saw. I lost it. I felt melancholy over any event. I saw
others melancholy. I sent a message….” According to the sentiment
algorithm, wading in clear water is our best dream.

Evaluating these works by their capacity to read like human prose is a
stale exercise because what qualifies as “natural” language is relative,
not absolute. Our own linguistic habits are developed through interaction
with others, be they members of a given social class, colleagues at work or
school, or spambots littering our Twitter feeds. In a recent Medium post,
Katie Rose Pipkin eloquently described how machines have already modified
what we think of as natural language, whether we're cognizant of it or not.
We speak differently to search tools and virtual assistants because we have
come to develop a tacit understanding of how they work and can modify our
requests to communicate effectively.

At least two 2014 submissions use dreams as a locus to explore the odd
beauty of machine intelligence. Thricedotted’s The Seeker relates the
autobiography of a machine trying to “learn about human behavior by reading
WikiHow.” The work is visually beautiful, with each iteration of the
algorithm’s operations punctuated by pages that raindrop abstractions and
house aphorisms like “imagine not one thing could be undirected.” Like the
hopscotch overtones in Cortázar’s Rayuela, the aphorisms encourage the
reader to perceive meaningful patterns in what might otherwise be random
data (Thricedotted’s internet identity often mentions apophenia). Time and
again, the algorithm repeats a “work, scan, imagine” loop, scraping
WikiHow, searching plain text memories for a concept encountered during
“work,” and building a dream sequence—or “univision”—from concepts it
doesn’t recognize. These univisions contain the most surprising poetry in
the work, where beauty arises from the reader’s ineluctable tendency to
feel meaning in fragments.

Earlier this year, the Wall Street Journal (WSJ) published an article with
an interactive sidebar featuring excerpts from financial investment
research reports. Readers were prompted to identify whether the excerpts
were written by robots or humans. Admittedly, Wall Street’s preference for
terse prose over poetic flourish makes a challenge like this make sense.
“Q2 cash balance expectation of $830m implies ~$80m of cash burn in Q2
after a $140m reduction in cash balance in Q1,” a sampled sentence, is
effectively just three data points fused together with syntax. And that’s
no coincidence. White collar workers like journalists need not fear their
job security (at least not yet…) because new natural language generation
(NLG) algorithms are very good at representing structured data sets in
prose, but not yet very good at much else. That capability in itself is
very powerful, as our ability to draw insights from data often depends on
how they are presented (e.g. a chart reveals insights one would have missed
in rows and columns). But it is a far cry from the creative courage
required to build a world on a blank page.

Technical constraints explain why NaNoGenMo has come to align itself with
poetics of recontextualization and reassembly. Indeed, genuine NLG
algorithms, that is, those that can build words and syntax from the
building blocks of letters and get smarter over time, are still very
nascent. Most of the 2014 submissions instead use rules to transform former
texts in creative ways, which also leads to topical similarities.

It is this search to use automation as a vehicle for defamiliarization
that makes NaNoGenMo so exciting. Darius Kazemi, an internet artist who
runs an annual Bot Summit, created NaNoGenMo “on a whim” in November, 2013.
Thoughtful about literary form, Kazemi was amused by the fact that National
Novel Writing Month (NaNoWriMo) set only two criteria for participants:
submissions must be written in 30 days (the month of November) and must
comprise at least 50,000 words. The absence of form invited
experimentation: why write a novel when you can write an algorithm that
writes an novel? He tweeted his idea, and a new GitHub (a web-based
software development collaboration tool) community was formed.

—
Reply to this email directly or view it on GitHub
#180.

[tra38]

Thanks for the praise.

An interesting note: I just realized that the preview I posted (as well as several other articles that were generated) misleadingly implies that the Wall Street Journal also entered into NaNoGenMo. (The only reason I did not catch this earlier though is because the preview article still flows properly, and the inaccuracy can only be detected by people who were familiar with the original article.)

[kathrynhume]

I think having WSJ as an entry may be the funniest part.