Revision as of 14:00, 6 January 2016

outline

With 'i-could-have-written-that' i would like to look at technologies that process natural language (NLP). By regarding NLP software as cultural objects, i'll focus on the inner workings of their technologies: how do they systemize our natural language? For the occassion of graduating this year, i would like to look at data-mining, text-mining and machine learning, the technologies that are used to gain information from large amounts of data by recognizing patterns.

intro

NLP, natural language processing
current focus: data-mining field (a data-fashion)

hypothesis

The results of data-mining software are not mined, results are constructed.
What elements do allow for algorithmic agreeability?

project

voice: accessible for a wider public

problem formulations:

terminology ('mining', 'data')
text-processing
- from: able to check results with senses (OCR), to: intuition (data-mining) [what are the differences?]
- parsing, how text is treated: as n-grams, chunks, bag-of-words, characters
use of wordclouds
- data as autonomous entity; from: information, to: data science [what are the differences?]

algorithmic agreeability case study objects (from the wild)

terminology & anthropomorphism: data 'mining' (wiki-page)
terminology & anthropomorphism: 'machine learning'
terminology: 'data'
wordclouds

thesis

voice: more technical? + theoretical

theory

solutionism & techno optimism

algorithmic agreeability case study objects (field-specific)

workflow mining-software (eg. Pattern, Wecka)
- software workflow diagram
- the use of mathematical graphs & dimensions

research material

→ filesystem interface, collecting research related material (+ about the workflow)
→ wikipage for 'i-could-have-written-that' (list of prototypes & inquiries)
→ little glossary

mining as ideology

* from mining minerals to mining data

anthropomorphism

* anthropomorphic qualities of a computer (?)
* the photographic apparatus → the data apparatus (annotations)
* Joseph's (Weizenbaum) questions on Computer Power and Human Reason

annotations

Alan Turing - Computing Machinery and Intelligence (1936)
The Journal of Typographic Research - OCR-B: A Standardized Character for Optical Recognition this article (V1N2) (1967); → abstract
Ted Nelson - Computer Lib & Dream Machines (1974);
Joseph Weizenbaum - Computer Power and Human Reason (1976); → annotations
Water J. Ong - Orality and Literacy (1982);
Vilem Flusser - Towards a Philosophy of Photography (1983); → annotations
Christiane Fellbaum - WordNet, an Electronic Lexical Database (1998);
Charles Petzold - Code, the hidden languages and inner structures of computer hardware and software (2000); → annotations
John Hopcroft, Rajeev Motwani, Jeffrey Ullman - Introduction to Automata Theory, Languages, and Computation (2001);
James Gleick - The Information, a History, a Theory, a Flood (2008); → annotations
Matthew Fuller - Software Studies. A lexicon (2008);
- Language, Florian Cramer; → annotations
- Algorithm, Andrew Goffey;
Marissa Meyer - the physics of data, lecture (2009); → annotations
Matthew Fuller & Andrew Goffey - Evil Media (2012); → annotations
Antoinette Rouvroy - All Watched Over By Algorithms - Transmediale (Jan. 2015); → annotations
Benjamin Bratton - Outing A.I., Beyond the Turing test (Feb. 2015) → annotations
Ramon Amaro - Colossal Data and Black Futures, lecture (Okt. 2015); → annotations
Benjamin Bratton - On A.I. and Cities : Platform Design, Algorithmic Perception, and Urban Geopolitics (Nov. 2015);

bibliography (five key texts)

Language, Florian Cramer (2008); → annotations
Antoinette Rouvroy - All Watched Over By Algorithms - Transmediale (Jan. 2015); → annotations
The Journal of Typographic Research - OCR-B: A Standardized Character for Optical Recognition this article (V1N2) (1967); → abstract

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+<div style="width:100%;max-width:800px;">
 =outline=
+With 'i-could-have-written-that' i would like to look at technologies that process natural language (NLP). By regarding NLP software as cultural objects, i'll focus on the inner workings of their technologies: how do they systemize our natural language? For the occassion of graduating this year, i would like to look at data-mining, text-mining and machine learning, the technologies that are used to gain information from large amounts of data by recognizing patterns.
 === intro===
@@ Line 6: / Line 13: @@
 ==hypothesis==
-The results of data-mining software are not mined, results are created. <br>
+The results of data-mining software are not mined, results are constructed. <br>
 What elements do allow for algorithmic agreeability?
@@ Line 12: / Line 19: @@
 <small>voice: accessible for a wider public </small>
-problem formulations:
+===problem formulations:===
 * terminology ('mining', 'data')
 * text-processing
@@ Line 20: / Line 27: @@
 ** data as autonomous entity; from: information, to: data science ''[what are the differences?]''
-===algorithmic agreeability case study objects===
+===algorithmic agreeability case study objects (from the wild)===
 * terminology & anthropomorphism: data 'mining' [[User:Manetta/i-could-have-written-that/from-mining-minerals-to-mining-data | (wiki-page)]]
 * terminology & anthropomorphism: 'machine learning'
@@ Line 32: / Line 39: @@
 * solutionism & techno optimism
-===algorithmic agreeability case study objects===
+===algorithmic agreeability case study objects (field-specific)===
 * workflow mining-software (eg. Pattern, Wecka)
 ** software workflow diagram

User:Manetta/thesis/thesis-outline: Difference between revisions