Texts exist in discursive space
They contain other texts as quotations and references, self-similar scaled copies.
Major texts are attractors in this space.
Some are singularities that neither light nor heat can escape.
Others are strange attractors that draw endless asymptotic vacillation.
It’s the fractality of discourse.
(After the 1992 original.)
I’m learning the three.js JavaScript 3D Graphics library. One of the projects I’m going to apply this to is Blockchain Aesthetics. Here are some of the more aesthetic failures and successes so far at visualising Bitcoin transaction hashes.
(This essay wouldn’t gel and I abandoned it. “XXXX…” means “do more here in the next writing or edit pass.”
Do get “Speculative Aesthetics” and “Class Wargames”, they are both wonderful books.)
Urbanomic’s “Speculative Aesthetics” is a freshly mined block of well-contextualised ideas, including some insightful discussion of Accelerationism’s relationship to aesthetics. That relationship is one I’ve been thinking and writing about in ignorant parallel.
I do not understand (I mean that literally: I’ve tried to parse the arguments and failed) the idea of overdetermined adherence to a teleological ideology as “freedom”. Whether the eschaton is religious, political/economic or technological, I don’t regard submission to its inevitability as freedom so much as a kind of Dice-Mannish false blamelessness. The fruit fly that buzzes in an endless repeating pattern through a featureless space is neither free nor showing free will. But then nor is the liberal consumer or subject of history in any absolute sense, however right Popper was about totalitarianism.
Abstraction is a word that describes many different phenomena, XXXXXXXX
At the same time as reading (or “reading“) “Speculative Aesthetics” I’ve been reading Richard Barbrook’s excellent”Class Wargames” book. Barbrook’s history of Guy Debord’s “Game of War” casts the game convincingly as a pedagogical tool for inoculating the political Left against the temptations of vanguard politics. Debord’s game represents the field and forces of battle at the time of the Napoleonic wars at a high level of abstraction compared to the average SPI/GDW-style wargame , making them a general representation of armed conflict. In order to win, one must recapitulate the tactics of Napoleon or Trotsky, with their attendant sacrifice and bloodshed. Having played at being Napoleon, players can both defeat them and will understand why they wouldn’t want to become or follow such a leader in real life.
This indicates the value of Acceleration’s strategy of abstraction, from specifics to generalities and back. Failing cloning, there will not literally be another Napoleon. But there will be scenarios in which another clever general might need to be defeated, either on the battlefield or in politics. There’s nothing Accelerationist about a board game, but there is about its use to create a distributed clever general. And it’s in distribution that Accelerationism can avoid vanguardism.
The legacy of CCRU can easily be painted as sitting uncomfortably with Accelerationism’s emphasis on rationality. But the irrational, imaginative, mystical, fictional nature of much CCRU/Orphan Drift/Nick Land output is rational – it can be rational to use irrationality when there is no rational means of achieving a rational end. This is instrumental irrationality, and it’s a standard part of creativity. Creativity theory tells us why – if thinking sensibly is leaving you stuck in a rut then thinking irrationally may get you out of it. Whether surrealist games, Edward de Bono’s “lateral thinking” or chemically assisted imaginings, XXXXXXXXX.
This is a very different kind of abstraction from the Real Abstraction of Marxism, or the representational simplifications and distillations of Data Visualization. Accelerationist aesthetics must achieve a new kind of abstraction via either a CCRUian instrumental irrationality as a mythological attractor and search space exploder, a Debordian detournement and redemption (rather than gentrification) of data visualization , or a remix of both.
We can use NLTK’s support for WordNet to help generate and classify text.
from nltk.corpus import wordnet as wn
from nltk.corpus import sentiwordnet as swn
def make_synset(word, category='n', number='01'):
"""Conveniently make a synset"""
number = int(number)
return wn.synset('%s.%s.%02i' % (word, category, number))
>>> dog = make_synset('dog')
>>> dog.definition
'a member of the genus Canis (probably descended from the common wolf) that has been domesticated by man since prehistoric times; occurs in many breeds'
A synset is WordNet’s representation of a word/concept. Looking at the definition confirms that we have the synset for canis familiaris rather than persecution or undesirability.
>>> dog.hypernyms()
[Synset('domestic_animal.n.01'), Synset('canine.n.02')]
Hypernyms are more general concepts. ‘dog’ has two of them, which shows that WordNet is not arranged in a simple tree of concepts. This makes checking for common ancestors slightly more complex but represents concepts more realistically.
>>> dog.hyponyms()
[Synset('puppy.n.01'), Synset('great_pyrenees.n.01'), Synset('basenji.n.01'), Synset('newfoundland.n.01'), Synset('lapdog.n.01'), Synset('poodle.n.01'), Synset('leonberg.n.01'), Synset('toy_dog.n.01'), Synset('spitz.n.01'), Synset('pooch.n.01'), Synset('cur.n.01'), Synset('mexican_hairless.n.01'), Synset('hunting_dog.n.01'), Synset('working_dog.n.01'), Synset('dalmatian.n.02'), Synset('pug.n.01'), Synset('corgi.n.01'), Synset('griffon.n.02')]
Hyponyms are more specific concepts. ‘dog’ has several. These may have hypernyms other than ‘dog’, and may have several hyponyms themselves.
def _recurse_all_hypernyms(synset, all_hypernyms):
synset_hypernyms = synset.hypernyms()
if synset_hypernyms:
all_hypernyms += synset_hypernyms
for hypernym in synset_hypernyms:
_recurse_all_hypernyms(hypernym, all_hypernyms)
def all_hypernyms(synset):
"""Get the set of hypernyms of the hypernym of the synset etc.
Nouns can have multiple hypernyms, so we can't just create a depth-sorted
list."""
hypernyms = []
_recurse_all_hypernyms(synset, hypernyms)
return set(hypernyms)
>>> all_hypernyms(dog)
>>> set([Synset('chordate.n.01'), Synset('living_thing.n.01'), Synset('physical_entity.n.01'), Synset('animal.n.01'), Synset('mammal.n.01'), Synset('object.n.01'), Synset('vertebrate.n.01'), Synset('entity.n.01'), Synset('carnivore.n.01'), Synset('domestic_animal.n.01'), Synset('canine.n.02'), Synset('placental.n.01'), Synset('organism.n.01'), Synset('whole.n.02')])
We can recursively fetch the hypernyms of a synset. since ‘dog’ has two hypernyms this isn’t a single list of hypernyms.
We can use this to find how similar different words are by searching for common ancestors.
The Python WordNet library can find common hypernyms for us though.
>>> cat = make_synset('cat')
>>> cat.common_hypernyms(dog)
[Synset('chordate.n.01'), Synset('living_thing.n.01'), Synset('physical_entity.n.01'), Synset('animal.n.01'), Synset('mammal.n.01'), Synset('vertebrate.n.01'), Synset('entity.n.01'), Synset('carnivore.n.01'), Synset('object.n.01'), Synset('placental.n.01'), Synset('organism.n.01'), Synset('whole.n.02')]
>>> steel = make_synset('steel')
>>> steel.common_hypernyms(dog)
[Synset('physical_entity.n.01'), Synset('entity.n.01')]
>>> sunset = make_synset('sunset')
>>> sunset.common_hypernyms(dog)
[Synset('entity.n.01')]
As might be expected, cats and dogs are more similar than steel or sunsets.
We can recursively fetch the hyponyms of a synset. This gives us the set of objects or concepts with a kind-of relationship to the word.
def _recurse_all_hyponyms(synset, all_hyponyms):
synset_hyponyms = synset.hyponyms()
if synset_hyponyms:
all_hyponyms += synset_hyponyms
for hyponym in synset_hyponyms:
_recurse_all_hyponyms(hyponym, all_hyponyms)
def all_hyponyms(synset):
"""Get the set of the tree of hyponyms under the synset"""
hyponyms = []
_recurse_all_hyponyms(synset, hyponyms)
return set(hyponyms)
>>> all_hyponyms(dog)
set([Synset('harrier.n.02'), Synset('water_spaniel.n.01'), Synset('standard_poodle.n.01'), Synset('dandie_dinmont.n.01'), Synset('wirehair.n.01'), Synset('toy_manchester.n.01'), Synset('puppy.n.01'), Synset('briard.n.01'), Synset('beagle.n.01'), Synset('siberian_husky.n.01'), Synset('manchester_terrier.n.01'), Synset('bloodhound.n.01'), ...
WordNet has some support for synonyms and antonyms via lemmas.
def synset_synonyms(synset):
"""Get the synonyms for the synset"""
return set([lemma.synset for lemma in synset.lemmas])
def synset_antonyms(synset):
"""Get the antonyms for [the first lemma of] the synset"""
return set([lemma.synset for lemma in synset.lemmas[0].antonyms()])
>>> synset_synonyms(sunset)
set([Synset('sunset.n.01')])
>>> synset_antonyms(sunset)
set([Synset('dawn.n.01')])
And we can find related concepts by getting all the hyponyms of a word’s hypernynms.
def all_peers(synset):
"""Get the set of all peers of the synset (including the synset).
If the synset has multiple hypernyms then the peers will be hyponyms of
multiple synsets."""
hypernyms = synset.hypernyms()
peers = []
for hypernym in hypernyms:
peers += hypernym.hyponyms()
return set(peers)
>>> all_peers(sunset)
set([Synset('zero_hour.n.01'), Synset('rush_hour.n.01'), Synset('early-morning_hour.n.01'), Synset('none.n.01'), Synset('midnight.n.01'), Synset('happy_hour.n.01'), Synset('dawn.n.01'), Synset('bedtime.n.01'), Synset('late-night_hour.n.01'), Synset('small_hours.n.01'), Synset('noon.n.01'), Synset('sunset.n.01'), Synset('twilight.n.01'), Synset('mealtime.n.01'), Synset('canonical_hour.n.01'), Synset('closing_time.n.01')])
We use sets here so that common ancestors and children appear only once, and to allow for boolean set operations on concepts.
It’s trivial to get the the word (or words) for a synset.
def synsets_words(synsets):
"""Get the set of strings for the words represented by the synsets"""
return set([synset_word(synset) for synset in synsets])
>>> synsets_words(all_hyponyms(dog))
set(['rottweiler', 'bull mastiff', 'belgian sheepdog', 'courser', 'brabancon griffon', 'toy terrier', 'fox terrier', 'sennenhunde', 'standard poodle', 'saluki', 'pointer', 'toy spaniel', 'setter', 'giant schnauzer', 'housedog', 'papillon', 'american foxhound', 'weimaraner', 'cocker spaniel', 'basenji', 'beagle', ...
WordNet has part/whole, group and substance relationships.
>>> body = make_synset('body')
>>> body.part_meronyms()
[Synset('arm.n.01'), Synset('articulatory_system.n.01'), Synset('body_substance.n.01'), Synset('cavity.n.04'), Synset('circulatory_system.n.01'), Synset('crotch.n.02'), Synset('digestive_system.n.01'), Synset('endocrine_system.n.01'), Synset('head.n.01'), Synset('leg.n.01'), Synset('lymphatic_system.n.01'), Synset('musculoskeletal_system.n.01'), Synset('neck.n.01'), Synset('nervous_system.n.01'), Synset('pressure_point.n.01'), Synset('respiratory_system.n.01'), Synset('sensory_system.n.02'), Synset('torso.n.01'), Synset('vascular_system.n.01')]
>>> dog.member_holonyms()
[Synset('canis.n.01'), Synset('pack.n.06')]
>>> wood = make_synset('wood')
>>> wood.substance_holonyms()
[Synset('beam.n.02'), Synset('chopping_block.n.01'), Synset('lumber.n.01'), Synset('spindle.n.02')]
>>> wood.substance_meronyms()
[Synset('lignin.n.01')]
We can use hypernyms to classify words into domains using WordNet, but there’s an existing domain classification system in the form of WordNet Domains. It can be downloaded here. Code for using this can be found on Stack Overflow. But it doesn’t seem to work with nltk 3.0 (the synset numbers don’t match).
And there’s a sentiment score system for WordNet in the form of SentiWordNet. There’s an interface for it in WordNet 3.0.
def make_senti_synset(word, category='n', number='01'):
"""Conveniently make a senti_synset"""
number = int(number)
return swn.senti_synset('%s.%s.%02i' % (word, category, number))
def synsets_sentiments(synsets):
"""Return the objs, pos, neg and pos - neg score sums for the synsets"""
pos = 0.0
obj = 0.0
neg = 0.0
for synset in synsets:
try:
pos += synset.pos_score()
obj += synset.obj_score()
neg += synset.neg_score()
except AttributeError, e:
pass
return obj, pos, neg, pos - neg
>>> happy = make_senti_synset('happy', 'a')
>>> happy.pos_score()
0.875
>>> happy.neg_score()
0.0
>>> happy.obj_score()
0.125
synsets_sentiments([make_senti_synset(word, 'a') for word in 'happy sad angry heavy light depressing'.split()])
(2.5, 1.5, 2.0, -0.5)
Not every word has a sentiment score, hence the try/except block in synsets_sentiments.
WordNet is sensitive to senses and it’s hard to automatically resolve senses when processing arbitrary text. When generating text and using WordNet to find words, it’s important (and easier) to set the correct sense for the synset.
>>> colour = make_synset('colour', 'n', 6)
>>> all_hyponyms(colour)
set([Synset('chrome_red.n.01'), Synset('primary_color.n.01'), Synset('light_brown.n.01'), Synset('sallowness.n.01'), Synset('hazel.n.04'), Synset('iron-grey.n.01'), Synset('olive_green.n.01'), Synset('tan.n.02'), Synset('pastel.n.01'), Synset('coal_black.n.01'), Synset('pinkness.n.01'), Synset('vandyke_brown.n.01'), Synset('beige.n.01'), Synset('blue.n.01'), Synset('shade.n.02'), Synset('achromatic_color.n.01'), Synset('whiteness.n.03'), Synset('coral.n.01'), Synset('chromatism.n.02'), Synset('apatetic_coloration.n.01'), ...
This gives concepts on different levels. Maybe if we try the peers of a colour.
>>> all_peers(make_synset('red'))
set([Synset('red.n.01'), Synset('pastel.n.01'), Synset('purple.n.01'), Synset('green.n.01'), Synset('olive.n.05'), Synset('complementary_color.n.01'), Synset('brown.n.01'), Synset('blue.n.01'), Synset('blond.n.02'), Synset('yellow.n.01'), Synset('orange.n.02'), Synset('pink.n.01'), Synset('salmon.n.04')])
OK maybe if we try the children of a concept.
>>> all_hyponyms(make_synset('chromatic_color'))
set([Synset('chrome_red.n.01'), Synset('light_brown.n.01'), Synset('hazel.n.04'), Synset('olive_green.n.01'), Synset('tan.n.02'), Synset('pastel.n.01'), Synset('pinkness.n.01')
Perhaps the leaf nodes.
def _recurse_leaf_hyponyms(synset, leaf_hyponyms):
synset_hyponyms = synset.hyponyms()
if synset_hyponyms:
for hyponym in synset_hyponyms:
_recurse_all_hyponyms(hyponym, leaf_hyponyms)
else:
leaf_hyponyms += synset
def leaf_hyponyms(synset):
"""Get the set of leaf nodes from the tree of hyponyms under the synset"""
hyponyms = []
_recurse_leaf_hyponyms(synset, hyponyms)
return set(hyponyms)
>>> leaf_hyponyms(make_synset('chromatic_color'))
set([Synset('taupe.n.01'), Synset('snuff-color.n.01'), Synset('chrome_red.n.01'), Synset('light_brown.n.01'), Synset('hazel.n.04'), Synset('olive_drab.n.01'), Synset('old_gold.n.01'), Synset('chocolate.n.03'), Synset('yellowish_pink.n.01'), Synset('yellowish_brown.n.01'), Synset('tyrian_purple.n.02'), ...
That looks good. All colours, no intermediate concepts.
We can use this set of words to choose colours, or to categorize words as colours.
I hope this demonstrates that WordNet can be a very useful resource for Generative Art and Digital Humanities projects.
Agorizing aesthetics and philosophy means producing them using market pricing mechanisms. The model for this is the market-based software of agoric computing. The advantage of such a system is that it incentivises both production and efficiency. By internalizing market forces, the perverse incentives of gamified systems such as academic research points can be avoided.
An agoric system needs a currency, for efficiency’s sake we will use a cryptocurrency. There are several ways of implementing a currency for an agoric system using cryptocurrency systems. We can use coloured coins, nxt or Counterparty assets, or Ethereum contracts similar to the Token System example in the Ethereum White Paper to create coins, tokens, or other quantifiable valences. For simplicity’s sake I will call all of these “tokens”. Different implementations allow different capabilities: fungibility, revocation, transferrability.
An artist group or artistic movement can use a store of tokens allocated by vote or other mechanism to artists or artworks that they deem part of the movement and its output. The Cypherfunks project is an existing example of such a system. Such a system combines the nominative practices of Dadaist, Conceptual and some Pop/Post-Pop art with the social and aesthetic function of materially identifying in and out groups. A single-artist token could be used to create an oeuvre in the style of Duchamp or Kostabi.
Art critics can use tokens to embody critique. A single-value token can be used to embody critical approval, a pair of oppositely valanced tokens to represent approval/opprobrium (and to revise critical opinion should it later change), a family of tokens with different star rankings can be used to implement a star system at the cost of fungibility. The critic sends tokens either to the artist’s address in the cryptocurrency system or to an address representing the hash or proxy hash of the artwork. The artist or artwork’s standing can be found by counting the number and kind of critical tokens associated with it.
Philosophical treatises can be constructed agorically. Axioms and citations, logical and rhetorical moves can be assigned point costs either as classes (premise, objection, rebuttal) or individually (Derrida, Arendt, Meillassoux). Each usage increases the price of the essay. Point costs and budgets can be assigned per hundred words or for a given form (short essay, review, thesis, journal article etc.) or context (particular journals, web sites, or educational institutions). Essays are then written to the budget. Or price essays according to the system and then let publishers (and readers) choose which to consume on that basis. For a more dynamic system prices can be set using a PageRank-style system as a product of the cost of cited works.
Pricing this essay is left as an exercise for the reader…
Run, don’t walk, to get a subscription to the new quarterly art/technology/weirdness journal Hack Circus. It’s like the Fortean Times as published by Make Magazine, or Mondo 2000 by Strange Attractor Journal. Issue 2 is just out and contains articles on the nature of reality, choreography as code & vice versa, and personality mimicking bots on Twitter amongst other things. I can’t recommend it highly enough.
Decommissioned Sea Fort Reborn as Luxury Island Retreat | Earth911.com
“While the reimagined Spitbank Fort features ultra-modern fixtures and decor elements, its jaw-dropping design schemes incorporate much of the original structure, including rugged exposed brick and armor-plated walls.”
Velvet Underground, Andy Warhol Foundation Settle Banana Album Dispute | Billboard
“The Velvet Underground and The Andy Warhol Foundation for the Visual Arts have split their differences over the iconic cover art for the 1967 album The Velvet Underground & Nico.”
EFF Makes Formal Objection to DRM in HTML5 | Electronic Frontier Foundation
“the Electronic Frontier Foundation (EFF) filed a formal objection to the inclusion of digital rights management (DRM) in HTML5”
“The strategy these companies use for radical innovation is decades out of date. We need to update it. We learned to do it at DARPA and we’re going to bring it to mobile, and it will have cascading effect on industry.”
“$200 puts you in the driver’s seat. Steer the telescope and snap a photo of the celestial object of your choosing — a nebula, Jupiter, Saturn’s rings, another galaxy, maybe even an asteroid.”
From a Free Software Movement to a Free Safety Movement | Local Autonomy Networks
“This is a call to all feminist hackers, anti-racist coders, gender hackers, genderchangers, queer and trans hackers, political hackers, anti-violence activists and networked activists to help stop violence against queer and trans* people, people of color, disabled people and women.”
The New Aesthetic — A secret network of 20 roadside listening stations…
“criminals are attempting to jam GPS signals on a regular basis”
Survivorship bias: why 90% of the advice about writing is bullshit right now | Tobias Buckell Online
“the self assured expertise of anecdotes drives me nuts”
2013 Internet Trends — Kleiner Perkins Caufield Byers
“The latest edition of the annual Internet Trends report finds continued robust online growth.”
Five Undercover Police Cars Sent To Arrest Single Alleged Movie Pirate | TorrentFreak
“What remains remarkable about FACT operations is how they are able to persuade the police to invest significant resources towards detaining individuals for non-violent crimes.”
“A deep topically relevant exploration of this cyber drama comes to light as The Acme Corporation presents If You Can Get To Buffalo: An Exploration of a Rape in Cyberspace by Julian Dibbell, a new work by Trish Harnetiaux.”
svg.js – A lightweight JavaScript library for manipulating and animating svg
“A lightweight library for manipulating and animating SVG.”
Jackson Pollock’s ‘One – Number 31, 1950’ Restored by MoMA – NYTimes.com
“That meant they were added after 1962,” Mr. Coddington said. “And since Pollock died in 1956, those photographs confirmed they were put there after his death.”
Me and Sad Keanu: A 3D-Printing Story – Alexis C. Madrigal – The Atlantic
“a meme materialized”
TRUSTAFARIANS OF THE BELLE ÉPOQUE | CAREER SUICIDE
“here’s just a few of the famous artists who conducted art as a full time hobby” with added photoshops.
Facebook agrees to block sexual assault ‘humor’ – NBC News.com
“Facebook is changing its policy on “humorous” posts and images that make light of rape and violence against women, following pressure from activists and advertisers.”
BSA Study Demonstrates Open Source’s Economic Advantage – Open Enterprise
“the fundamental premise of the latest BSA study – that licensed proprietary software is better in many ways than pirated copies – actually applies to open source software even more strongly”
Google defends its use of proprietary tech in Hangouts | TechHive
“Google is feeling the heat over its decision to build its new Hangouts IM and audio/video chat product with proprietary technology”
“Anita Sarkeesian’s Tropes vs Women in Video Games Is Back With More On That Oh-So-Irritating Damsel Trope”
“a few of the GIFs Google has created showing some of the most startling pockets of change”
Beneath the Fold: The Twisted Tale of Origami v. Sarah Morris | GalleristNY
“Ms. Morris did say that a friend pointed out to her that a group of origami zealots suing an artist was just about “the ‘least zen’ thing he’d ever heard.””
Where Looks Don’t Matter and Only the Best Writers Get Laid | The Cluster Mag
“How the feminist internet utopia failed, and we ended up with speculative realism”
Artfacts API
css rule application visualiser
“The Raspberry Pi Twitter Sentiment Server (I really must called RPTSS for short) is an ongoing tutorial on using R, Sqlite and some Python to measure Twitter sentiment on given hashtags.”
Werewolf fiction lacks the confidence of Vampire fiction. Vampire
fiction is novel, reflexive, indexical, and complete. It is novel
because vampirism is unprecendented in both the social and personal
realities of its characters. It is reflexive because it is about the
condition of vampirism qua vampirism as its subject. It is indexical
because the condition of vampirism is used allegorically or
metaphorically to animate contemporary concerns and to illustrate the
human condition. And it is complete because no other themes or
macguffins are used to make up for the perceived deficiencies of the
form.
“Dracula” and “Interview With The Vampire” are the two high points of
popular vampire fiction. The former takes the stuff of penny dreadfuls
and distant folk superstition as the absent core of a clash between
modernity and superstition that animates the hypocrisy and shear of
Victorian society. The latter ironises the displaced catholic theatrics
of an exhausted cinematic form into a tale of the betrayal of promise
and an illustration of the price of the impossible that does not require
its reader to have an immortal soul to lose in order for it to terrify them.
There is very little werewolf fiction that is novel, reflexive,
indexical, and
complete. I do not know why this is. Werewolfery can be an ironic symbol of
many key elements of the human condition and of their postmodern
situation. Take out the witches and faeries, the police procedural and
the pack dynamics, the hunters and the soap opera and lycanthropy can be a
prism rather than ballast.