User:Alexander Roidl/new–projectproposal: Difference between revisions
No edit summary |
No edit summary |
||
Line 1: | Line 1: | ||
==What do you want to make?== | ==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 | 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 and computer vision or images that manipulate themselves from text-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? | 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 want to investigate on the implications of this new kind of image generation. | ||
Line 11: | Line 11: | ||
==How do you plan to make it?== | ==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 start with short history of algorithmic image & form generation and what to connect it to recent technological changes. | ||
I want to draw connections from the technical to the cultural, which means | I want to draw connections from the technical to the cultural, which means outlining 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. | 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. | 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. | ||
Line 26: | Line 26: | ||
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. | 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) | Images dominate the world and mostly human perception (80%? of your perception is based on vision). We saw different phenomenas arising from image culture: memes, new aesthetics, photomanipulation, the aesthetics of censorship | ||
While Walter Benjamin saw himself confronted with a mechanical reproducibility of art, we are now facing digital self-production and are even challenged in the way we see by machines. | |||
==Who can help you and how?== | ==Who can help you and how?== | ||
* PZI Tutors > Research / Prototyping | * PZI Tutors > Research / Prototyping | ||
* AI Now Institute https://ainowinstitute.org/ | * AI Now Institute https://ainowinstitute.org/ | ||
I reached out to them and hope to be able to get some insights in their research about the social impact of artificial intelligence | |||
* XPUB Gang | * XPUB Gang | ||
* Interaction Station Javier (They are researching about machine learning in creative practices right now) | |||
== Relation to previous practice == | |||
== Relation to previous practice == | I'm trained as a graphic designer and have been fascinated by different kinds of visual material. In addition to that I gained interest in new media and technology that would enhance humans. I am interested in understanding these new phenomenas and their effects. | ||
== Relation to a larger context == | == Relation to a larger context == | ||
Nowadays 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) | |||
Revision as of 14:31, 15 October 2018
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 and computer vision or images that manipulate themselves from text-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 to recent technological changes. I want to draw connections from the technical to the cultural, which means outlining 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 world and mostly human perception (80%? of your perception is based on vision). We saw different phenomenas arising from image culture: memes, new aesthetics, photomanipulation, the aesthetics of censorship While Walter Benjamin saw himself confronted with a mechanical reproducibility of art, we are now facing digital self-production and are even challenged in the way we see by machines.
Who can help you and how?
- PZI Tutors > Research / Prototyping
- AI Now Institute https://ainowinstitute.org/
I reached out to them and hope to be able to get some insights in their research about the social impact of artificial intelligence
- XPUB Gang
- Interaction Station Javier (They are researching about machine learning in creative practices right now)
Relation to previous practice
I'm trained as a graphic designer and have been fascinated by different kinds of visual material. In addition to that I gained interest in new media and technology that would enhance humans. I am interested in understanding these new phenomenas and their effects.
Relation to a larger context
Nowadays 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)