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Learning Algorithms
Learning Algorithms
====Focus====
====Focus====
What can be learned from the use of machine learning in the fields of visual production (art & design)?
a Software Art approach
 
A Software Art approach towards


====Argument====
====Argument====
The current application of machine learning in artistic fields comes down to the output of some pre-made open source scripts without a deeper investigation on the inner workings of these tools.
Software Art can provide a useful framework and methods to approach machine learning algorithms artistically
Why is it important to have a deeper understanding of these algorithms in order to create meaningful outputs with them? What can be learned from machine learning algorithms and what is its relation to visual culture?


====Scope====
====Scope====
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 that is beyond »interesting« images. And that is what I want to find out with my work. I have the feeling that there is space for intervention in the very fundamental layers of neural networks and how they operate. I want to further investigate in the inner workings of them and also find out why it is important to understand them in order to work with them in a more meaningful way.
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.
I want to write an analytical essay that will relate to my practical work and build its theoretical foundation.
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==Introduction==
==Introduction==
===Background===
===Background===
With my background as a graphic designer I am interested visual culture and in algorithmic generation of visual output. Recently new kinds of algorithms found their way into digital systems: Machine Learning. They are increasingly implemented in our daily life and perform better than other kinds of calculations. 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?
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==
==Body==
===PART 1: Machine Learning: Interventions in visual culture===
====Current State of machine learning====
* Description of machine learning in our daily life in different applications
** What machine learning is, where it is and what it does
** The controversial discussion on machine learning (a short overview)


* Current situation of artists/designers using machine learning algorithms in the realm of visual production
===Drawing a parallel between Software Arts and the current state of machine learning===
** Influence on visual culture: While computer vision has been used a lot by artists, artificial image generation is still less explored
====Software Art and Generative Art====
** it is mostly used to generate images from existing algorithms, there is no deeper engagement with the technique / tool itself
Argument: Software Arts can be seen as a reaction to the limited interaction with the tools in Generative Arts
** Is machine learning just the next filter that will be outdated in 2 years?
* History of Software Art & Generative Art
* Software Art and its process based approach
* Software as the main subject of research


====New Image Language====
====Software Art applied to machine learning====
''Images as an example for visual production''
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.
* How we talk about images has changed. Technicians deconstruct images to their features and have invented a strange language for images.
* Making machine learning the main field of research and the algorithm itself part of the art
** Visual Culture has not yet intervened in that field 
* 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?


* At the same time: Images gain new qualities: They are not only being valuable as datasets, but with algorithms we are able to add another layer of metadata to them. A layer of language, that describes the image. Images of our time are infected with texts; they visualize texts. Our image makers’ imaginations are infected with conceptual thinking, with trying to hold processes still. (Flusser, 2011, p. 13)
* 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.


===PART 2: Understanding Machine Learning===
====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


(maybe this needs to become smaller)
====CNN / Computer Vision====
''Insight in one specific kind of algorithm''
* Algorithms that see and interpret our world
* How algorithms intervene in our world
* Visibility for Computers and Humans
** How we help computers to help them see
** Therefore: how computers help us see
** Computer challenge what and how we see
** Machines see things that remain invisible for humans
** Relation to the invisibility of models in machine learning algorithms


====GAN / Generative Algorithms====
* Algorithms that generate visual material -> images
* Effects / surprising outputs, but why is it different from other techniques
** Learning as engagement with material


====Images and imagination of machine learning====
https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist Could this be an (unwanted) example for performative machine learning art?
* The blurry understanding of machine learning creates imaginations and anthropomorphizing of machine learning
** This insights creates a certain fascination for machine learning images
 
 
(maybe this needs to be deleted)
====Reverse Engineering neural networks====
* Experimental approach in understanding
* Black Box and how to approach this complexity
 
===PART 3: Intervention===
''Ways to find a use of machine learning that is beyond effects''
 
====comparison between machine learning and other algorithms ====
* The effect of machine learning as an universal tool
** The pro and cons of this «all-eating-machine»
* Complexity and surprise
** Complicated statistical algorithms generate a moment of surprise
** How any new technique has this momentum
 
====Drawing parallel between Software Arts and the current state of machine learning====
* Software Arts can be seen as a reaction to the limited interaction with the tools in Generative Arts
* 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==
Summary of what can be learned from machine learning and how this can be applied.
How the investigation and intervention in the process of neural networks can make artistic expression possible and comment on current tendencies in the field.
Hope to create a new discussion how we talk about machine learning and share insights.






=References=
Flusser, V. (2011). Into the Universe of Technical Images. (N. A. Roth, Trans.) (1 edition). Minneapolis: Univ Of Minnesota Press.


[[User:Alexander Roidl/new-new–projectproposal#References|more references and readings]]
[[User:Alexander Roidl/new-new-new-new-projectproposal#References|more references and readings]]

Latest revision as of 12:24, 10 January 2019

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

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