Revision as of 15:20, 9 November 2018

Thesis Outline

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 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.

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.

@@ Line 1: / Line 1: @@
 =Thesis Outline=
-==Thesis Statement==
-===Topic===
-Influence of machine learning on visual culture
-===Focus===
+====Topic====
-in times of machine learning
+The use of machine learning algorithm for visual production
-===Argument===
+====Focus====
-How understanding machine learning leads to
+What can be learned from machine learning in the fields of visual production (art & design)?
-===Revise===
-Machine Learning is intervening in our visual world and therefore shaping how we perceive. Algorithms analyse images, process data and even generate new images, while these algorithms are black boxes.
-===Scope===
+====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.
-=Outline=
+I want to write an analytical essay that will relate to my practical work and build its theoretical foundation.
 ==Introduction==
 ===Background===
-* I'm specifically interested in algorithms and their influence on visual culture
+With my background as a graphic designer I am interested 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.
-* Machine learning algorithms are everywhere and increasingly intervene in our world and how we perceive it
 ==Body==
-===Machine Learning Interventions in visual culture===
+===PART 1: Machine Learning Interventions in visual culture===
 * Examination of current applications of machine learning algorithms
 ** Influence on visual culture
@@ Line 26: / Line 26: @@
 * Problematic of machine learning algorithms and models
-===Examining Machine Learning===
+===PART 2: Examining Machine Learning===
 * What machine learning is, where it is and what it does
 ====Computer Vision====
@@ Line 46: / Line 46: @@
 * How we talk about images in relation to how they are generated
-===Understanding Machine Learning===
+===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.

User:Alexander Roidl/thesis-outline3: Difference between revisions