Latest revision as of 13:54, 1 November 2018

Thesis Outline

Thesis Statement

Topic

Ways of understanding complex algorithms. Ways of understanding machine learning algorithms. trough reverse engineering.

Reverse Engineering Algorithmic Images

Visual Interventions

Focus

in times of machine learning

Argument

Machine Learning is intervening in our visual world and therefore shaping how we perceive.

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

»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

Introduction

Background

I'm specifically interested in algorithms and their influence on visual culture
Machine learning algorithms are everywhere and increasingly intervene in our world and how we perceive it

Body

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

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

Understanding Machine Learning: Reverse Engineering

Experimental approach
Black Box and how to approach this complexity

Towards endless image production

The computer allows to generate every imaginable image (imagination = image)
From mechanical reproduction to mechanical production

Conclusion

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

@@ Line 13: / Line 13: @@
 in times of machine learning
 ===Argument===
-Machine Learning is intervening in our visual world.
+Machine Learning is intervening in our visual world and therefore shaping how we perceive.
 ===Revise===
-Machine Learning is increasingly intervening in our visual world. Algorithms analyse images, process data and even generate new images.
+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===
@@ Line 23: / Line 23: @@
 ==Introduction==
 ===Background===
+* I'm specifically interested in algorithms and their influence on visual culture
+* Machine learning algorithms are everywhere and increasingly intervene in our world and how we perceive it
 ==Body==
-===Machine Learning===
+===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
+===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
 ===Understanding Machine Learning: Reverse Engineering===
+* Experimental approach
+* Black Box and how to approach this complexity
-===Image Language===
-====On the visible and invisible====
-* Computer vision
-** 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
-**** Relatable to images or models or databases or technology and lack of understanding
-====Understanding Images====
-* Images as analytical tools
-* Features of images and how we talk about images
 ====Towards endless image production====
 * The computer allows to generate every imaginable image (imagination = image)
 * From mechanical reproduction to mechanical production
-(Do not know where to put this questions:)
-* machine to machine, why would they need vision?
-* why do we need images?
 ===Conclusion===
-Images change the way we perceive the world and therefore we need to be able to understand the way images are being created.
+How understanding and approaches like reverse engineering help influencing complex systems.
-Turn it around!
-From specific problem > more broader terms

User:Alexander Roidl/thesis-outline2: Difference between revisions