User:Alexander Roidl/thesis-outline3

Thesis Outline

Thesis Statement

Topic

The use of machine learning algorithm for visual production

Focus

What can be learned from machine learning in the fields of visual production (art & design)?

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. 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

»Intelligent« algorithms are everywhere and they challenge the way we see. They generate new kinds of images that raise a new meaning and understanding of the world. How can we understand these algorithms in order to understand their influence on our visual perception.

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

Introduction

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?

Body

PART 1: Machine Learning Interventions in visual culture

• Examination of current applications of machine learning algorithms
  • Influence on visual culture
  • short history of algorithmic image production / analysis
• Problematic of machine learning algorithms and models

PART 2: Examining Machine Learning

• What machine learning is, where it is and what it does

Computer Vision

• 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 ->

Computer Visual Generation

• Algorithms that generate visual material -> images

Image Language (A bag of features)

• Images as analytical tools
• Features of images
• How we talk about images in relation to how they are generated

PART 3: Understanding Machine Learning

Reverse Engineering

• Experimental approach
• Black Box and how to approach this complexity

PART 4: Deep Intervention

• What

Conclusion

How understanding and approaches like reverse engineering help influencing complex systems.