User:Manetta/graduation-proposals/proposal-0.1: Difference between revisions
(→people) |
No edit summary |
||
| Line 14: | Line 14: | ||
===what do you want to do?=== | ===what do you want to do?=== | ||
setting up a publishing platform / magazine to reveal inner workings of technologies that systemize language. | |||
/ | |||
setting up a publishing platform / magazine to reveal inner workings of technologies of systemization / automation / machine learning that work with simplification / probability / modeling ... | |||
... departing from underlying technological issues, and bringing them to a broader cultural context | |||
... in order to look for alternative perspectives | |||
== Relation to previous practice == | == Relation to previous practice == | ||
| Line 20: | Line 27: | ||
In the last year, i've been looking at different '''tools''' that contain linguistic systems. From ''speech-to-text software'' to ''text-mining tools'', they all '''systemize language''' in various ways in order to understand '''natural language'''—as human language is called in computer science. These tools fall under the term 'Natural Language Processing' ('''NLP'''), which is a field of computer science that is closely related to Artificial Intelligence (AI). | In the last year, i've been looking at different '''tools''' that contain linguistic systems. From ''speech-to-text software'' to ''text-mining tools'', they all '''systemize language''' in various ways in order to understand '''natural language'''—as human language is called in computer science. These tools fall under the term 'Natural Language Processing' ('''NLP'''), which is a field of computer science that is closely related to Artificial Intelligence (AI). | ||
As a continutation of that i took part at the Relearn summerschool in Brussels last August, to propose a working track in collaboration with Femke Snelting on the subject of 'training common sense'. With a group of people we have been trying to deconstruct the truth-construction in '''algorithmic cultures''', by looking at data mining processes, deconstructing the mathematical models that are used, finding moments where semantics are mixed with mathematic models, and understanding which cultural context is created around this field. | As a continutation of that i took part at the Relearn summerschool in Brussels last August, to propose a working track in collaboration with Femke Snelting on the subject of 'training common sense'. With a group of people we have been trying to deconstruct the truth-construction process in '''algorithmic cultures''', by looking at data mining processes, deconstructing the mathematical models that are used, finding moments where semantics are mixed with mathematic models, and understanding which cultural context is created around this field. | ||
Another entrance to understanding what happens in algorithmic practises such as machine learning, is by looking at '''training sets''' that are used to train software that is able to recognize patterns | Another entrance to understanding what happens in algorithmic practises such as machine learning, is by looking at '''training sets''' that are used to train software that is able to recognize certain patterns in a set of data. These training sets could contain a large set of images, texts, 3d models, or video's. By looking at such datasets, and more specifically at the choises that have been made in terms of structure and hierarchy, steps of the construction a certain 'truth' are revealed. | ||
There are a few datasets in the academic world that seem to be basic resources to built these | There are a few datasets in the academic world that seem to be basic resources to built these training sets upon. In the field they are called ''''knowledge bases''''. They live on a more abstract level then the training sets do, as they try to create a 'knowlegde system' that could function as a universal structure. Examples are WordNet (a lexical dataset), ConceptNet, and OpenCyc (an ontology dataset). | ||
== Relation to a larger context == | == Relation to a larger context == | ||
"i could have written that" will be a magazine reflecting on the topic of 'communication', with an approach that is very much based on an aim of revealing the inner working processes of technologies that systemize natural language. | |||
other magazines (touching the same topics): | |||
* [http://www.servinglibrary.org/ the Serving Library (US)] | |||
* [https://worksthatwork.com/ Works that Work (NL)] | |||
* [http://neural.it/ Neural (IT)] | |||
* [http://www.aprja.net/ Aprja (DK)] | |||
* [http://ds.ccc.de/download.html die Datenschleuder, Chaos Computer Club publication] | |||
== Thesis intention == | == Thesis intention == | ||
== Practical steps == | == Practical steps == | ||
how? | ===how?=== | ||
* | * keeping in the back of my mind the approach of '''i will tell you everything' (my truth is a constructed truth)'', which took the structure of a machine learning training set (called the SUN dataset), and applied this to a set of objects that formed an exhibition together. The training set was (also literally) a voice over of this exhibition, that framed the objects by speaking from the choices that have been made to construct the SUN dataset. | ||
* writing/collecting from a | * writing/collecting from a technological point of departure, as has been done before by: | ||
- Matthew Fuller, powerpoint | - Matthew Fuller, powerpoint | ||
- Constant, pipelines | - Constant, pipelines | ||
| Line 43: | Line 57: | ||
- Angie Keefer, Octopus | - Angie Keefer, Octopus | ||
===questions of research=== | |||
* how to built and maintain a collaborative publishing project? | |||
** technically: what kind of system to use? wiki? | |||
** publishing: online + print --> inter-relation | |||
* forms of communication that could produce alternative perspectives to bring technologic 'issues' to a broader field? | |||
== References == | == References == | ||
===datasets=== | ===datasets=== | ||
| Line 63: | Line 83: | ||
===notes and related projects=== | ===notes and related projects=== | ||
[http://pzwart1.wdka.hro.nl/~manetta/annotations/txt/bak-algorithmic-cultures-2015.html BAK lecture: Matthew Fuller, on the discourse of the powerpoint (Jun. 2015) - annotations]<br> | [http://pzwart1.wdka.hro.nl/~manetta/annotations/txt/bak-algorithmic-cultures-2015.html BAK lecture: Matthew Fuller, on the discourse of the powerpoint (Jun. 2015) - annotations]<br> | ||
[[User:Manetta/semantic-systems/knowledge-bases/wordnet | project: Wordnet]] | [[User:Manetta/semantic-systems/knowledge-bases/wordnet | project: Wordnet]] | ||
| Line 86: | Line 105: | ||
Last January (2015) an interdisciplinary arts-lab in Brussels called [http://www.constantvzw.org/site/-About-Constant,7-.html Constant] organized a worksession called 'Cqrrelations' in which we reflected on the construction of machine learning models in the field of text-mining. Together we learned about a software package developed by the CLiPS research centre of the University of Antwerp, called Pattern. With a smaller group we trained an algorithm to tell us if a certain text could be called 'patternalistic' or not. | Last January (2015) an interdisciplinary arts-lab in Brussels called [http://www.constantvzw.org/site/-About-Constant,7-.html Constant] organized a worksession called 'Cqrrelations' in which we reflected on the construction of machine learning models in the field of text-mining. Together we learned about a software package developed by the CLiPS research centre of the University of Antwerp, called Pattern. With a smaller group we trained an algorithm to tell us if a certain text could be called 'patternalistic' or not. | ||
As a continutation of that i took part at the Relearn summerschool in Brussels last August, to propose a working track in collaboration with Femke Snelting on the subject of 'training common sense'. With a group of people we have been trying to deconstruct the truth-construction in '''algorithmic cultures''', by looking at data mining processes, deconstructing the mathematical models that are used, finding moments where semantics are mixed with mathematic models, and understanding which cultural context is created around this field. | As a continutation of that i took part at the Relearn summerschool in Brussels last August, to propose a working track in collaboration with Femke Snelting on the subject of 'training common sense'. With a group of people we have been trying to deconstruct the truth-construction process in '''algorithmic cultures''', by looking at data mining processes, deconstructing the mathematical models that are used, finding moments where semantics are mixed with mathematic models, and understanding which cultural context is created around this field. | ||
* understanding algorithmic processes, | * understanding algorithmic processes, | ||
| Line 97: | Line 116: | ||
A way of ''speaking back'' to these algorithmic cultural fields would be by publishing a critical fork of the text-mining software package called Pattern. The fork will be called #!PATTERN+, which will be a new release of the original package developed by the CLiPS research group at the university of Antwerp. | A way of ''speaking back'' to these algorithmic cultural fields would be by publishing a critical fork of the text-mining software package called Pattern. The fork will be called #!PATTERN+, which will be a new release of the original package developed by the CLiPS research group at the university of Antwerp. | ||
* to reveal machine-human labour (pingponging) | |||
* to deconstruct truth-systems of data-mining | |||
* to talk back to a piece of software | |||
== Relation to previous practice == | == Relation to previous practice == | ||
| Line 113: | Line 136: | ||
* text mining case studies - a reflection on the research projects that gave been done/are going on, working with examples and demo's | * text mining case studies - a reflection on the research projects that gave been done/are going on, working with examples and demo's | ||
* culture of data-mining - a more context-based approach, looking at the communication conventions that are present in the field of data-mining | * culture of data-mining - a more context-based approach, looking at the communication conventions that are present in the field of data-mining | ||
== References == | == References == | ||
Revision as of 14:55, 23 September 2015
graduation proposal +0.1.1
title: "i could have written that"
alternatives:
- turning words into numbers
- machine-human-machine
Introduction
For in those realms machines are made to behave in wondrous ways, often sufficient to dazzle even the most experienced observer. But once a particular program is unmasked, once its inner workings are explained in language sufficiently plain to induice understanding, its magic crumbles away; it stands revealed as a mere collection of procedures, each quite comprehensible. The observer says to himself "I could have written that". With that thought he moves the program in question from the shelf marked "intelligent" to that reserved for curios, fit to be discussed only with people less enlightened that he. (Joseph Weizenbaum, 1966)
what do you want to do?
setting up a publishing platform / magazine to reveal inner workings of technologies that systemize language.
/
setting up a publishing platform / magazine to reveal inner workings of technologies of systemization / automation / machine learning that work with simplification / probability / modeling ...
... departing from underlying technological issues, and bringing them to a broader cultural context ... in order to look for alternative perspectives
Relation to previous practice
what are you doing?
In the last year, i've been looking at different tools that contain linguistic systems. From speech-to-text software to text-mining tools, they all systemize language in various ways in order to understand natural language—as human language is called in computer science. These tools fall under the term 'Natural Language Processing' (NLP), which is a field of computer science that is closely related to Artificial Intelligence (AI).
As a continutation of that i took part at the Relearn summerschool in Brussels last August, to propose a working track in collaboration with Femke Snelting on the subject of 'training common sense'. With a group of people we have been trying to deconstruct the truth-construction process in algorithmic cultures, by looking at data mining processes, deconstructing the mathematical models that are used, finding moments where semantics are mixed with mathematic models, and understanding which cultural context is created around this field.
Another entrance to understanding what happens in algorithmic practises such as machine learning, is by looking at training sets that are used to train software that is able to recognize certain patterns in a set of data. These training sets could contain a large set of images, texts, 3d models, or video's. By looking at such datasets, and more specifically at the choises that have been made in terms of structure and hierarchy, steps of the construction a certain 'truth' are revealed.
There are a few datasets in the academic world that seem to be basic resources to built these training sets upon. In the field they are called 'knowledge bases'. They live on a more abstract level then the training sets do, as they try to create a 'knowlegde system' that could function as a universal structure. Examples are WordNet (a lexical dataset), ConceptNet, and OpenCyc (an ontology dataset).
Relation to a larger context
"i could have written that" will be a magazine reflecting on the topic of 'communication', with an approach that is very much based on an aim of revealing the inner working processes of technologies that systemize natural language.
other magazines (touching the same topics):
- the Serving Library (US)
- Works that Work (NL)
- Neural (IT)
- Aprja (DK)
- die Datenschleuder, Chaos Computer Club publication
Thesis intention
Practical steps
how?
- keeping in the back of my mind the approach of 'i will tell you everything' (my truth is a constructed truth), which took the structure of a machine learning training set (called the SUN dataset), and applied this to a set of objects that formed an exhibition together. The training set was (also literally) a voice over of this exhibition, that framed the objects by speaking from the choices that have been made to construct the SUN dataset.
- writing/collecting from a technological point of departure, as has been done before by:
- Matthew Fuller, powerpoint - Constant, pipelines - Steve Rushton, feeback - Angie Keefer, Octopus
questions of research
- how to built and maintain a collaborative publishing project?
- technically: what kind of system to use? wiki?
- publishing: online + print --> inter-relation
- forms of communication that could produce alternative perspectives to bring technologic 'issues' to a broader field?
References
datasets
* WordNet (Princeton)
* ConceptNet 5 (MIT Media)
* OpenCyc
people
algorithmic culture
Luciana Parisi Matteo Pasquinelli Antoinette Roivoy Seda Gurses
other
Matthew Fuller
reading list
BAK lecture: Matthew Fuller, on the discourse of the powerpoint (Jun. 2015) - annotations
project: i will tell you everything (my truth is a constructed truth)
graduation proposal +0.1.2
title: #!PATTERN+
Introduction
what are you doing?
Last January (2015) an interdisciplinary arts-lab in Brussels called Constant organized a worksession called 'Cqrrelations' in which we reflected on the construction of machine learning models in the field of text-mining. Together we learned about a software package developed by the CLiPS research centre of the University of Antwerp, called Pattern. With a smaller group we trained an algorithm to tell us if a certain text could be called 'patternalistic' or not.
As a continutation of that i took part at the Relearn summerschool in Brussels last August, to propose a working track in collaboration with Femke Snelting on the subject of 'training common sense'. With a group of people we have been trying to deconstruct the truth-construction process in algorithmic cultures, by looking at data mining processes, deconstructing the mathematical models that are used, finding moments where semantics are mixed with mathematic models, and understanding which cultural context is created around this field.
* understanding algorithmic processes, * questioning where choises are made for the construction * finding moments where semantics are mixed with math * looking at the data-mining culture
what do you want to do?
- publishing about this truth construction process in algorithmic cultures, by taking Pattern as a case-study object
A way of speaking back to these algorithmic cultural fields would be by publishing a critical fork of the text-mining software package called Pattern. The fork will be called #!PATTERN+, which will be a new release of the original package developed by the CLiPS research group at the university of Antwerp.
- to reveal machine-human labour (pingponging)
- to deconstruct truth-systems of data-mining
- to talk back to a piece of software
Relation to previous practice
Relation to a larger context
Thesis intention
Practical steps
how?
The critical fork '#!PATTERN+' will contain annotations in the form of commands inside the code, files that reflect questions that arose, and alternative tutorials will be added to the original package. By asking the question how is algorithmic 'truth' constructed?, these #!PATTERN+ notes will be developed in one of the following 3 sub-fields:
- Knowledge Discovery in Data (KDD) steps - a technical approach, revealing the process from 'raw' data to the presentation of the results
- text mining case studies - a reflection on the research projects that gave been done/are going on, working with examples and demo's
- culture of data-mining - a more context-based approach, looking at the communication conventions that are present in the field of data-mining
References
people
Luciana Parisi Matteo Pasquinelli Antoinette Roivoy Seda Gurses
events
BAK lecture: Algorithmic Culture (Jun. 2015) - annotations
software
* CLiPS Pattern, official website
reading list
via Femke: Seda [Gurses preparing a session on Machine Learning]
Transmediale lecture: All Watched Over By Algorithms (Jan. 2015) - annotations
notes taken during Relearn Aug. 2015, training common sense
the Annotator, Cqrrelations Jan. 2015, on etherpad
notes Cqrrelations day 1, day 2, day 3, day 4, day 5