EOL Jan 22, 12:57 PM
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Dinosaurs Feb 8, 02:24 AM
Pattern poems combed from William Diller Matthew’s Dinosaurs:
With Special Reference to the American Museum Collections.
Auxotox Vertex May 19, 12:50 AM
Degradation Apr 28, 08:23 AM
Character-level, descending depth Markov rearrangement
PDF (11.29 M)
HTML (3.42 M)
Plain Text (2.74 M)
Code (Ruby)
Predaceous Diving Beetle Apr 23, 12:08 AM
This book was generated using 300 Likes, each with a lookahead of 1000 characters.
This version uses a thread per Like#_read so that the objects can read simultaneously rather than lining up and taking turns. If all objects are in motion, then the collective reading stops and each object prints out whatever it last liked forming a ‘final reading’. In the last version, the program would only cycle to the next text if the reading died (if all objects stopped moving).
Code (Ruby):
like
like_class.rb
Likes :: Apr 18, 04:56 PM
installation at CalArts – 20 April 2006
photography: 
-Text is laid out on a grid, one character per position.
-A specified number of Like objects (let’s say 500) are created and assigned to positions on the grid.
-As part of its creation (and at no other time), a pattern is generated for the object, determining what it ‘likes’. The object will look for this pattern in the text chunks it reads. The pattern consists of 3 sequential characters from the source text with a potential gap inserted between either position 1 and 2 or position 2 and 3. So, if ‘age’ is selected from the source text, ‘a.*?ge’ or ‘ag.*?e’ are the possible patterns. ’.*?’ means zero or more occurences of any character except newline. ‘a.*?ge’ will match ‘age’ and ‘a huge’.
-Each object reads a specified lookahead distance (let’s say 500 characters) ahead of itself in the text looking for its pattern, determining what it ‘likes’ in what it’s read. If the object’s pattern is ‘a.*?ge’ and it reads ‘ice age’, it will like ‘age’. If it reads ‘against geometry’, it will like ‘against ge’.
-If the object finds something that it likes in what it has read, it will print what it liked to the screenit and move ahead in the text to the end of that last chunk it read (500 characters) or will go to the beginning of the text if it is at the end.
-If the object doesn’t find something that it likes in the 500 character chunk, then it is unable to move. An immobile object waits for moving objects to enter its lookahead range. Of the objects in proximity, it chooses the one with the highest matchcount (the one that has found the most things that it likes) and copies a character from that object’s pattern to replace one of the characters in its own pattern. The object mutates itself in an attempt to continue progress through the text.
-If all objects are motionless, the reading is finished and the program cycles to a new text.
The idea is to create an environment for a text to read itself—to not only compare its potential patterns to its original presentation, but to execute those potentialities. An arbitrarily self-reflexive text. This project is also an attempt at modeling a system of self-regulating perception.
The environment consists of two programs. One running the text, objects, and sending reports over a network on the activity of individual objects. The second program receives report messages from the first and displays them on a different screen.
Code (Ruby):
like_loop
like_recv
like_class.rb
Scri Mar 1, 06:19 AM
Graphical, interactive text manipulation based on guided randomization. Freud’s “Dream Psychology” is the source text.
Scri is meant to disrupt the relationships between the visual, temporal, and significant dimensions of text perception. The project is an exploration of a hypothetical reading practice that breaks logical linearity and calls the signifying function of the word into question by foregrounding its visual characteristics and dislocating it in reference to its prescribed sequence. This presentation sets the stage for conjoined lingual and visual associations with the letter/word form through fracture, imposition, and interruption.
Online Version
Viralnet 2006 – Scri splash page
Processing – Streamlined Java for graphics
googler Jan 26, 07:05 AM
Text Visualization Dec 9, 10:40 PM
The script attempts to pick out all the parts of speech in a given text by looking up each of its words in the Moby database. The parts of speech are then mapped to colors and placed in order of appearance on a 8 1/2×11 postscript page. White space happens when the word in the text cannot be mapped to a part of speech in the database.
The color palette used is the 216-color ‘websafe’ palette. The corresponding hex code for each color is converted to a sequence of 3 floating point numbers (R,G,B) to be used with postscript. These 216 values are saved for reference.
Moby identifies a word’s part of speech with a single character. For instance, a noun is represented by ‘N’ and an adjective is represented by ‘A’. For each word in the text, once the part of speech is determined, the value used to index the color palette is the ASCII value of the character that identifies the part of speech. ‘A’ = 65, ‘N’ = 78. Many words are able to act as multiple parts of speech. Moby identifies this by stringing together a sequence of characters. A word that can serve as an adjective and a noun would be marked ‘AN’. In this case, the script averages the ASCII value of the two characters to determine where to index the color palette. ‘AN’ = 71 (remainders are discarded).
Here’s an example (from Whitman’s Leaves of Grass ) : Right here
The idea is to explore the possiblities for transcoded text (text turned into image, sound, etc). I want to see if this kind of transfer can maintain aspects of textuality (through color patterns, etc.), if not overall, maybe for certain texts or categories of text. Are words as important to a text as EVERYBODY wants you to believe? Maybe. I hope not.
The code:
textvis.py : class definitions
t_vis : script to generate the images
The Second View of a Blackbird Nov 2, 04:43 AM
This piece consists of texts pulled from the Web based on a Google search for the second section of the Wallace Stevens poem, Thirteen Ways Of Looking At A Blackbird . The collected texts were concatenated and subjected to Markov chain rearrangment. For each section on each page, the depth of the Markov analysis used starts at two and increments to thirteen. This creates a progress per page from greater to lesser degrees of randomization. By the end of the page, the section usually consists of an uninterrupted quote from the source text. As well as being read linearly, the pages can be laid out next to one another and viewed as a grid.
This piece is an attempt to explore the reproductive capabilites of a closed, individualized associative system (Stevens’ poem) when pushed into the context of an open, collective associative system (the Web). The consequences of this collision come in a cloud of narrative fragments that, although often highly disparate, are re-presented in an attempt to evoke novel cognitive integration. Through this process, I’m trying to subvert the vast, but associatively limited multi-linearity of hyperlinked web text and approach text-information on the Web as a nebulous entity of thought whose density and malleability lay the groundwork for entirely new modes of language encounter. Narratives imposed by web browsing sofware can be re-articulated through knowledge of the orginaztional systems they employ along with alternative methods of search and access.
Queneau – Hundred Thousand Billion Poems
The search term I used accidentally had a typo (that wasn’t corrected): “I was of three minds,Like a treeIn which there are three blackbirds.”