User:Alexander Roidl/new–projectproposal
What do you want to make?
New advanced algorithms allow for new methods of form production. We see ourselves confronted with a weird set of new phenomenas: algorithms that generate endless new photorealistic images from a certain dataset, deeply weird shapes emerging from deep dream or computer vision or images that manipulate themselves from speech-input. Even before computers were invented artists where working on algorithmic form creation. Later, with the use of program code, images where generated in ever more diverse forms. Programers and technicians have only recently developed images that are generated by machine learning that imitate photorealistic material. Images, that follow more than only simple rules, but are far from pure randomness. Images, that are created based on a model that is not readable by humans, a model, which is feed by a database of existing images. How can these images be categorised? Is it photos, drawing, renderings, generation? I want to investigate on the implications of this new kind of image generation. I’m looking to answer the question: What does algorithmic image generation mean for cultural production in times of machine learning? In order to answer this question I need to ask: How does machine learning change the generation of image. Why and how is it different from other forms of image generation? What are the implications on culture and art production through those generated forms.
These kind of algorithms have been used to generate image alike Van Gogh or other famous artists, but I want to challenge these algorithms to generate new, more »native digital« images. I want to understand how these images come to be and how I as an artist / designer can make use of them.
How do you plan to make it?
I want start with short history of algorithmic image & form generation and what to connect it technological changes. I want to draw connections from the technical to the cultural, which means connecting machine learning in relation to visual culture. In a comparison between existing image generation techniques and new A.I.-enhanced forms, I hope to find parallels and differences in aesthetic, use and function. I plan to make experiments going along with the research that will eventually accumulate to one final work. In a playful approach I want to bend models of machine learning generators, try to make them fail, produce new material, irritate algorithms.
What is your timetable?
Along the writing of the thesis that will be based on careful reading and research, I want to make experiments with image generation algorithms.
Why do you want to make it?
Online you constantly see news about new machine learning algorithms. I encountered a lot of technical papers in the past, where technicians are talking about images (quite strangely). But instead of talking about the technical improvements that can be done on these images, I want to think about what these images mean for fields traditionally concerned with image-making, especially arts and design. Furthermore Machine Learning is embedded in many contemporary digital systems that drive our world, and it is hard for us to understand them fully. So I think images are becoming a more important tool to make visible what these algorithms are doing and also to make visible where they fail. It is important to understand these systems and their implications in order to be able to influence them.
Images dominate the human perception (80% of your perception is based on vision)
Who can help you and how?
- PZI Tutors > Research / Prototyping
- AI Now Institute https://ainowinstitute.org/
- XPUB Gang
Relation to previous practice
- I'm trained as a graphic designer
- My work enveloped to be related to visual material and programmatic approaches
- I program myself and I'm interested in how this influences contemporary aesthetics
Relation to a larger context
- Machine Learning is embedded in many contemporary digital systems that drive our world
- Images dominate the human perception (80% of your perception is based on vision)
References
Relations: In 2012 James Bridle established the term »New Aesthetic« in 2012, an ongoing collection of images on a tumblr blog for these kind of new images.
Soft image
Into the Universe of Technical Images
Understanding Opacity in Algorithms