User:Alexander Roidl/thesis-outline: Difference between revisions
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==Thesis Statement== | ==Thesis Statement== | ||
===Topic=== | ===Topic=== | ||
Digital Image / Form Perception / Generation | Digital Image / Visual Form Perception / Generation | ||
===Focus=== | ===Focus=== | ||
in times of machine learning | in times of machine learning | ||
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(Machine learning algorithms can be used to generate new photorealistic images. These images enable a new kind of aesthetic and allow for a new understanding of these technologies at the same time. ) | (Machine learning algorithms can be used to generate new photorealistic images. These images enable a new kind of aesthetic and allow for a new understanding of these technologies at the same time. ) | ||
===Scope=== | ===Scope=== | ||
»Intelligent« algorithms are everywhere and they challenge the way we see. They generate new kinds of images that | »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. What do these images mean for cultural production like art and design? | ||
==Outline== | ==Outline== | ||
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Interest / Why I'm going to do is. | Interest / Why I'm going to do is. | ||
Examples. | |||
====Background==== | |||
====Thesis Statement==== | ====Thesis Statement==== | ||
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====Database (History of image generation in art and design)==== | ====Database (History of image generation in art and design)==== | ||
* Tracing back the history of algorithmic image generation and how it changed due to new technologies | * Tracing back the history of algorithmic image generation and how it changed due to new technologies | ||
** Instructions as Art (LeWitt) | ** Instructions as Art (LeWitt, Gerstner) | ||
** Draw analogies to the invention of the camera (Walter Benjamin: Essay on Photography) | ** Draw analogies to the invention of the camera (Walter Benjamin: Essay on Photography) | ||
** also Cinema and introduction of databases (Lev Manovich: The database as symbolic form) | ** also Cinema and introduction of databases (Lev Manovich: The database as symbolic form) | ||
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====Model (From Database to Model)==== | ====Model (From Database to Model)==== | ||
in realation to images | |||
* Instructions/Algorithm become invisible | * Instructions/Algorithm become invisible | ||
* Images are generated on basis of a database | * Images are generated on basis of a database | ||
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====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 | ||
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===Conclusion=== | ===Conclusion=== | ||
?? | |||
Images change the way we perceive the world and therefore we need to be able to understand the way images are being created. | |||
Turn it around! | |||
From specific problem > more broader terms | |||
Latest revision as of 16:35, 18 October 2018
Thesis Outline
Thesis Statement
Topic
Digital Image / Visual Form Perception / Generation
Focus
in times of machine learning
Argument
New advanced algorithms allow for new form understanding and generation.
Revise
New advanced algorithms allow for new form understanding and generation. This means a new understanding of image perception and production in the arts and design. (Machine learning algorithms can be used to generate new photorealistic images. These images enable a new kind of aesthetic and allow for a new understanding of these technologies at the same time. )
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. What do these images mean for cultural production like art and design?
Outline
Introduction
Interest / Why I'm going to do is.
Examples.
Background
Thesis Statement
Body
Database (History of image generation in art and design)
- Tracing back the history of algorithmic image generation and how it changed due to new technologies
- Instructions as Art (LeWitt, Gerstner)
- Draw analogies to the invention of the camera (Walter Benjamin: Essay on Photography)
- also Cinema and introduction of databases (Lev Manovich: The database as symbolic form)
- database-art (The aesthetic of the database)
Model (From Database to Model)
in realation to images
- Instructions/Algorithm become invisible
- Images are generated on basis of a database
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.
Turn it around!
From specific problem > more broader terms