User:Alexander Roidl/thesis-outline2: Difference between revisions
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in times of machine learning | in times of machine learning | ||
===Argument=== | ===Argument=== | ||
Machine Learning is intervening in our visual world. | Machine Learning is intervening in our visual world and therefore shaping how we perceive. | ||
===Revise=== | ===Revise=== | ||
Machine Learning is | 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=== | ||
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==Body== | ==Body== | ||
===Machine Learning=== | ===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=== | ===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. | |||
Revision as of 12:13, 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
Body
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.