Creating an Model for Aesthetic AI part 1

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AI Aesthetics has been the field of studying the Impact of AI Generated art on Humans and human society.  I am proposing to re-imagine a framework in which AI can study human Generated art, and learn to be a creator of unique artistic objects, not derivative mimicking of a finished piece of human art, but to walk down the artistic process itself

MDSF Aesthetic Frame work  

1. There are many dimensions of  Art

2. Each Dimension has a Structural Footprint ( DSF )

3. Each DSF has a lifecycle, that is by nature dynamic

4. Each DSF can be measured and mapped, like a face or a finger print is measured and converted to a  unique (or rare)  digital identifier.

5.When many DSF interact together in an art process, the MDSF is the measure of relatedness to other DSFs

6. As the complexity of this MDSF measurement grows, it develops its own "vibe" "Style" or  Meta structure

7. This meta structure becomes an aesthetic that a learning AI can interpret, and have art response.

8. Dissonance and Resonance are at opposite poles of relatedness of the MDSF

The Dimensional Lifecycle

Concept phase, Design Phase, Haptic Construction, Presentation, Interpretation

 Concept Assumptions

The MDSF Aesthetic Framework building blocks

1. Dimensional roster

2. Dimensional Structural Footprints DSF

2. Multidimensional Structural Footprints MDSF

3.  DSF Footprint mapping algorithms, MDSF Relatedness Algorithms

4. MDSF Art process and Object

5. Dimensional Lifecycle

5. Interpretation art response

-Understanding Artistic Motivation. 

AI Aesthetics refers to the sensory experience we have with content created by artificial intelligence. This includes the images, music, and text that AI systems generate, and our personal, human response to them. It is the feeling you get when you see a piece of digital art that seems to know your taste, or the curiosity sparked by a poem written by a machine.


This field of study looks at the qualities of what is made and also at the invisible processes behind the creation.

This field of study looks at the qualities of what is made and also at the invisible processes behind the creation.-https://lifestyle.sustainability-directory.com/term/ai-aesthetics/

https://lifestyle.sustainability-directory.com/term/ai-aesthetics/


Lev Manovich

  • Media theorist and digital artist whose recent work focuses on how generative AI reshapes visual culture, creativity, and design.​

  • Co‑author of Artificial Aesthetics: Generative AI, Art and Visual Media, which proposes a systematic framework for understanding “cultural AI.”​

Emanuele Arielli

  • Philosopher of art and aesthetics who co‑authored Artificial Aesthetics with Manovich, bringing perspectives from aesthetics, philosophy, and psychology of art to AI imagery.

an‑Noël Thon

  • Media and narrative theorist who co‑edited AI Aesthetics: AI‑Generated Images between Artistics and Aisthetics (Routledge), one of the first dedicated volumes explicitly framing “AI aesthetics” as a research field

  • o‑editor of AI Aesthetics: AI‑Generated Images between Artistics and Aisthetics, contributing to the distinction between aesthetics as connoisseurship and as embodied perception in the context of generative AI.​

  • Works at the intersection of media studies, visual culture, and critical AI studies.​

Mario Klingemann (practice‑led)

  • Artist often cited as a pioneer of neural‑network‑based art, whose practice is central in discussions of AI aesthetics and computational creativity

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