User:Alexander Roidl/thesis-outline2

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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.