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

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


Reverse Engineering Neural Networks
Reverse Engineering Algorithmic Images
Reverse Engineering Algorithmic Images


Visual Forms of machine learning
Visual Interventions


===Focus===
===Focus===
in times of machine learning
in times of machine learning
===Argument===
===Argument===
New advanced algorithms allow for new form understanding and generation.  
Machine Learning is intervening in our visual world.  
===Revise===
===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 is increasingly intervening in our visual world. Algorithms analyse images, process data and even generate new images.  
(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 raise a new meaning and understanding of the world. What do these images mean for cultural production like art and design?
»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==
===Machine Learning===


==Outline==
===Introduction===


====Background====


====Thesis Statement====


===Body===
===Understanding Machine Learning: Reverse Engineering===
====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=====
===Image Language===
====On the visible and invisible====
* Computer vision  
* Computer vision  
** How we help computers to help them see
** How we help computers to help them see
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**** Relatable to images or models or databases or technology and lack of understanding
**** Relatable to images or models or databases or technology and lack of understanding


=====Understanding Images=====
====Understanding Images====
* Images as analytical tools
* Images as analytical tools
* Features of images and how we talk about images
* Features of images and how we talk about images
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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.
Images change the way we perceive the world and therefore we need to be able to understand the way images are being created.


Revision as of 12:00, 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.

Revise

Machine Learning is increasingly intervening in our visual world. Algorithms analyse images, process data and even generate new images.

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

Machine Learning

Understanding Machine Learning: Reverse Engineering

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

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