User:Alexander Roidl/thesis-outline4

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Thesis Outline

Thesis Statement

Topic

Learning Algorithms

Focus

a Software Art approach

Argument

Software Art can provide a useful framework and methods to approach machine learning algorithms artistically

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. The ideas and the deep investigation on the algorithms help to question and analyse the concepts of machine learning.

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. But still I think that there is something that can be learned from those kind algorithms and how arts can approach it in a more meaningful and lasting 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 the use of machine learning in a more sustainable way, asking what can be left after the hype of machine learning. How can we understand those algorithms as a object of studies or a piece of art itself?

Body

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

Software Art and Generative Art

Argument: Software Arts can be seen as a reaction to the limited interaction with the tools in Generative Arts

  • History of Software Art & Generative Art
  • Software Art and its process based approach
  • Software as the main subject of research

Software Art applied to machine learning

Argument: Methods and ideas of Software Arts and Software Studies can be applied to Machine Learning Algorithms and Software to create a more meaningful and lasting research.

  • Making machine learning the main field of research and the algorithm itself part of the art
  • Artists are not yet interacting with the deeper source -> how can the algorithm itself be the art?
  • Process based machine learning
    • What would be an intervention in the spirit of Software Arts?
  • Sidenotes: Code Brutalism

Learning Algorithms

Machine Learning in Engineering

Argument: Programers engage with machine learning algorithms in a very pragmatic way. Why it can be useful to free those algorithms from their usefulness.

  • Example Language: How engineers talk about machine learning. Talking about features, generalisation, over and underfitting, training-data
  • Example Ideas: Usefulness and Speed as primary goals of development.
  • Example Resources: Only the fastest GPUS are used, who can’t afford it or who doesn’t have the data is out of the game.

Machine Learning in Art

Argument: Current artistic use of machine learning is rather unsatisfying. Mostly these algorithms are used as tools to produce surprising outcome, but there is no artistic engagement with the algorithm itself.

  • Examples of current machine learning art

Neural Networks in Depth

Argument: A close up look at the algorithm reveals important key concepts

  • The architecture / structure of learning algorithms
  • Learning as a concept of loops


https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist Could this be an (unwanted) example for performative machine learning art?

Conclusion

How the investigation and intervention in the process of neural networks can make artistic expression possible and comment on current tendencies in the field.



more references and readings