User:Alexander Roidl/thesis-outline4: Difference between revisions

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==Body==
==Body==
====Software Art and Generative Art====
 
===Software Art and Generative Art===
* History of Software Art
* History of Software Art
* Software Arts can be seen as a reaction to the limited interaction with the tools in Generative Arts
* Software Arts can be seen as a reaction to the limited interaction with the tools in Generative Arts
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* Software as the main subject to research
* Software as the main subject to research


====Machine learning in the arts====
===Drawing parallel between Software Arts and the current state of machine learning===
* Making machine learning the main field of research
* Artists are not yet interacting with the deeper source -> how can the algorithm itself be the art?
** What would be an intervention in the spirit of Software Arts?
** How can this idea be extended
 
===Machine learning===
====Development of Machine Learning====
* How engineers talk about machine learning
* Language: Talking about features, generalisation, over and underfitting, training-data
====Artists in AI====
* Current examples of artistic engagement
* Current examples of artistic engagement
* discussion about authorship
* Discussion about authorship
 
====Drawing parallel between Software Arts and the current state of machine learning====
* making machine learning the main field of research
* A parallel case can be seen with machine learning: Artists are not yet interacting with the deeper source.
** What would be an intervention in the spirit of Software Arts?


==Conclusion==
==Conclusion==

Revision as of 15:45, 19 November 2018

Thesis Outline

Thesis Statement

Topic

Learning Algorithms

Focus

a Software Art approach

Argument

Software Art can provide useful ideas and tools to approach machine learning algorithms.

Scope

The process oriented approach of Software Arts provides a useful theory that can be applied to machine learning algorithms in order to engage with them as an artist.

This refers mostly to the current use of machine learning and how this hype is used by artists to generate more or less random art using prewritten algorithms. And also how it is referred to as the magical tool for everything, providing surprising outputs with less effort. This thought can be discussed controversially. But still I think that there is something that can be learned from those kind algorithms and how arts approach it. And that is what I want to find out with my work. I want to find out why it is important to understand them in order to work with them in a more meaningful way.

I want to write an analytical essay that will relate to my practical work and build its theoretical foundation.

Introduction

Background

Recently new kinds of algorithms found their way into digital systems: Machine Learning Algorithms. They are increasingly implemented in our daily life and perform better than other kinds of previous system. At the same time machine learning raises other questions regarding e.g. opacity, bias and moral. Following this huge current attention machine learning is also being used in the arts. While recent artistic approaches are limited in their engagement with the technological understanding, I want to investigate on the use of machine learning in a more sustainable way, asking what can be left after the hype of machine learning. What makes those algorithms different from other existing ones? How is their output relevant for visual culture?

Body

Software Art and Generative Art

  • History of Software Art
  • Software Arts can be seen as a reaction to the limited interaction with the tools in Generative Arts
  • Software Art and its process based approach
  • Software as the main subject to research

Drawing parallel between Software Arts and the current state of machine learning

  • Making machine learning the main field of research
  • Artists are not yet interacting with the deeper source -> how can the algorithm itself be the art?
    • What would be an intervention in the spirit of Software Arts?
    • How can this idea be extended

Machine learning

Development of Machine Learning

  • How engineers talk about machine learning
  • Language: Talking about features, generalisation, over and underfitting, training-data

Artists in AI

  • Current examples of artistic engagement
  • Discussion about authorship

Conclusion

more references and readings