Daniel Kersh - Dec 01 2020

Generative Abstraction

"Errors in the Artificial Intelligence model have the exceptional ability to create ambiguity in an image and infringe upon our eyes power for discernment."

Generative Abstraction is a technique utilizing artificial intelligence, machine learning, and other computational methods as a tool to construct works of abstract art.

What sets Generative Abstraction apart from human generated forms of abstract art, is the combination of hyper-realism and deep-abstraction within the same image.

Hyper-realism and deep-abstraction define the outer limits of what is real and what is abstract through the use of massive libraries of data.

For example, an image of 1 tree may be composed of the data from 1,000 trees.

Because the initial data used to create the work is photographic and real, we see the foundation of the work as being realistic.

As the abstraction or ambiguity in the image is dialed up, more and more is demanded from the viewer’s creativity and imagination to complete the image.

It is this combination of hyper-realism and deep-abstraction that makes Generative Abstract art so engaging.

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