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 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===
===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
 
===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====
====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
(Do not know where to put this questions:)
* machine to machine, why would they need vision?
* why do we need images?


===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.
How understanding and approaches like reverse engineering help influencing complex systems.
 
 
Turn it around!
From specific problem > more broader terms

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.