User:Alexander Roidl/thesis-outline2: Difference between revisions
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==Thesis Statement== | ==Thesis Statement== | ||
===Topic=== | ===Topic=== | ||
Ways of understanding complex algorithms | Ways of understanding complex algorithms. | ||
Ways of understanding machine learning algorithms. | |||
trough reverse engineering. | |||
Reverse Engineering Algorithmic Images | Reverse Engineering Algorithmic Images | ||
Visual | Visual Interventions | ||
===Focus=== | ===Focus=== | ||
in times of machine learning | in times of machine learning | ||
===Argument=== | ===Argument=== | ||
Machine Learning is intervening in our visual world and therefore shaping how we perceive. | |||
===Revise=== | ===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=== | ===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. | »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 | ** How we help computers to help them see | ||
** Therefore: how computers help us 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 | * Images as analytical tools | ||
* Features of images | * 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==== | ====Towards endless image production==== | ||
* The computer allows to generate every imaginable image (imagination = image) | * The computer allows to generate every imaginable image (imagination = image) | ||
* From mechanical reproduction to mechanical production | * From mechanical reproduction to mechanical production | ||
===Conclusion=== | ===Conclusion=== | ||
How understanding and approaches like reverse engineering help influencing complex systems. | |||
Latest revision as of 14:54, 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.