The Tools of Generative Art, From Flash to Neural Networks
Metadata
Highlights
- Flash helped create a new breed of artist/developer, unschooled in traditional computer science and unafraid to dive in and experiment by sharing code and learning by doing. Many contemporary generative artists cite Davis’s site praystation.com as the inspiration for their interest in creative coding.
- Reas was interested in the phenomenon of emergence, a process in which a collective entity, such as a flock of birds or a school of fish, begins to exhibit properties that its individual members do not. His work MicroImage (2002) is an animation built from repetitions of relatively simple parts and commands. From thousands of dots, each programmed to react simply to its surroundings, a more complex system takes shape. The result is a gorgeous procedural animation that feels like a living, breathing, pen-and-ink drawing.
- The figures in Klingemann’s work are pale and gaunt, resembling those in old photos of asylum patients or medical catalogues documenting human deformities. The faces enter the world only briefly, but they all have the look of old souls carrying the weight of a troubled past. This haunted aesthetic is Klingemann’s hallmark; he has always avoided the tendency to make digital art with a polished and shiny aesthetic. GANs trained on photos tend to introduce bizarre quirks as they struggle to produce something like the input images, and Klingemann relishes the results. They are quite different from generative art that uses iterative commands to draw vector-based shapes to the screen.
- Sougwen Chung, Anna Ridler, and Helena Sarin are some of the artists who train GANs on their own drawings and paintings—bodies of visual information that are distinctly theirs, unlike large public data sets. Sarin has also used visuals generated by AI as the basis for works made with various analog processes, from glass fusing and pottery to monotypes and screen-printing. By combining these physical art-making methods with cutting-edge digital techniques, Sarin has developed her own language that is warmer and more physically engaging than push-button GAN images.
- Many artistic processes can be described as algorithmic. Artists follow sequences or steps in the production of their own work. Often what makes one artist’s practice more interesting than another’s is spontaneity, a willingness to challenge their own system. Sometimes, it is computerized tools that make this leap possible.
The Tools of Generative Art, From Flash to Neural Networks
Metadata
Highlights
- Flash helped create a new breed of artist/developer, unschooled in traditional computer science and unafraid to dive in and experiment by sharing code and learning by doing. Many contemporary generative artists cite Davis’s site praystation.com as the inspiration for their interest in creative coding.
- Reas was interested in the phenomenon of emergence, a process in which a collective entity, such as a flock of birds or a school of fish, begins to exhibit properties that its individual members do not. His work MicroImage (2002) is an animation built from repetitions of relatively simple parts and commands. From thousands of dots, each programmed to react simply to its surroundings, a more complex system takes shape. The result is a gorgeous procedural animation that feels like a living, breathing, pen-and-ink drawing.
- The figures in Klingemann’s work are pale and gaunt, resembling those in old photos of asylum patients or medical catalogues documenting human deformities. The faces enter the world only briefly, but they all have the look of old souls carrying the weight of a troubled past. This haunted aesthetic is Klingemann’s hallmark; he has always avoided the tendency to make digital art with a polished and shiny aesthetic. GANs trained on photos tend to introduce bizarre quirks as they struggle to produce something like the input images, and Klingemann relishes the results. They are quite different from generative art that uses iterative commands to draw vector-based shapes to the screen.
- Sougwen Chung, Anna Ridler, and Helena Sarin are some of the artists who train GANs on their own drawings and paintings—bodies of visual information that are distinctly theirs, unlike large public data sets. Sarin has also used visuals generated by AI as the basis for works made with various analog processes, from glass fusing and pottery to monotypes and screen-printing. By combining these physical art-making methods with cutting-edge digital techniques, Sarin has developed her own language that is warmer and more physically engaging than push-button GAN images.
- Many artistic processes can be described as algorithmic. Artists follow sequences or steps in the production of their own work. Often what makes one artist’s practice more interesting than another’s is spontaneity, a willingness to challenge their own system. Sometimes, it is computerized tools that make this leap possible.