User:Alexander Roidl/thesis-outline4

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

Machine learning

Machine Learning in Engineering

Argument: Programers engage with machine learning algorithms in a very pragmatic way. Why it can be useful to separate those algorithms from their usefulness and investigate on their cultural meaning.

• 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

Drawing 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
• Software Art and its process based approach
• Software as the main subject to 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?

Conclusion

more references and readings