Max Chu
book designer + creative techonolgist
Design
Media
Other
Information
maxchu@gmail.com
@crabapplefeeder
Max Chu
book designer + creative techonolgist
Design
Media
Other
Information
maxchu@gmail.com
@crabapplefeeder
Let Me Collect Myself
Completed as capstone project of the NYU IMA program
2023-2024
Pen on paper
Completed as capstone project of the NYU IMA program
2023-2024
Pen on paper
This project investigates how the development of machine learning models changes the labor relations of artists with their art.
There’s a new type of labor that exists within image generation models that is distinctly unique and distinctly invisible compared to art practices of the past, and due to the newness, the language and modes to critique image generation is still in the process of being developed. Therefore, this project aims to participate in this development of language through interrogation of the implicit, “invisible” labor conducted in order to create data sets with which machine learning models are dependent on. Many of the foundation models that are used as the basis of ML image generation are trained on data sets with unattributed sources, such as ImageNet, or data sets compiled by Common Crawl. Further, much of the essential tagging of the data in the sets is performed through crowd-work, using such services as Amazon Mechanical Turk. This disambiguation of labor results in a system that is difficult to audit, and therefore, difficult to critique. In response, the goal of this project is to present a data set created entirely by one individual, in order to create a framework with which critique may more easily occur.
Final paper can be found here
More portraits can be found @10000plus2
There’s a new type of labor that exists within image generation models that is distinctly unique and distinctly invisible compared to art practices of the past, and due to the newness, the language and modes to critique image generation is still in the process of being developed. Therefore, this project aims to participate in this development of language through interrogation of the implicit, “invisible” labor conducted in order to create data sets with which machine learning models are dependent on. Many of the foundation models that are used as the basis of ML image generation are trained on data sets with unattributed sources, such as ImageNet, or data sets compiled by Common Crawl. Further, much of the essential tagging of the data in the sets is performed through crowd-work, using such services as Amazon Mechanical Turk. This disambiguation of labor results in a system that is difficult to audit, and therefore, difficult to critique. In response, the goal of this project is to present a data set created entirely by one individual, in order to create a framework with which critique may more easily occur.
Final paper can be found here
More portraits can be found @10000plus2