This document contrasts handmade folk art with machine-generated folk art created with an AI system. Handmade art involves material costs, learning over time, and serendipity, while machine art is more efficient but relies on the system's tendencies. Both can be used for self-expression, stress relief, and entertainment. However, handmade art may better support poetry, visual exploration, and thinking while machine art excels at structure, cultural references, and finding online audiences. The author views machine-assisted art as a collaboration that should augment but not replace manual skills.

1. Contrasting My Beginner Folk Art
vs. Machine Co-Created Folk Art
with an Art-Making Generative AI
Shalin Hai-Jew
September 2023

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6. Presentation
• The SARS-CoV-2 pandemic inspired several years of experimentation
with common or folk art, involving mixed media, alcohol ink painting,
and other explorations. Then, with the emergence of art-making
generative AIs, there were further experiments, particularly with one
that enables generation of visuals from scanned art and photos, text
prompts, style overlays, and text-based visual modifiers. While both
types of artmaking are emotionally satisfying and helpful for stress
management, there are some contrasting differences. This
exploratory slideshow explores some of these differences in order to
partially shed light on the informal usage of an art-making generative
AI (artificial intelligence).
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7. “Common Art”
• Embodied
• Self-taught
• Personal motivations
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9. Common Art…For
1. Self-expression, poetry
2. Visual exploration, visual learning
3. Stress relief, distraction
4. Entertainment (for self, for others)
5. Social contribution, sharing
6. Thinking
7. Social commentary
8. Others
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11. Initial Simple Contrasts
Beginner Folk Art
• Material costs (with art pricing)
• Time costs
• Learning costs (painstaking)
• Getting hands dirty
• Lived experiential
• Feels more organic to life
Machine Co-Created Folk Art
• Time costs (but more efficient
than for analog materials)
• Learning costs (iterative, brute
force)
• Keeping hands clean
• Anticipating computational
processing of the visual
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12. Initial Simple Contrasts(cont.)
Beginner Folk Art
• Less control over outcomes,
without the power of digital
editing
• Can tap into serendipity
• Conscious mind, unconscious
mind
• Costs in material, energy
consumption, water of analog
materials (esp. synthetic plastic
paper)
Machine Co-Created Folk Art
• Emergence of surprises
• Tend towards convenience of
what the system outputs
• Costs in generative AI creation
(training, fine-tuning, UI, etc.),
energy consumption, water, of
generative AI
• More conventional sense of
what is art
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13. Initial Simple Contrasts(cont.)
Beginner Folk Art
• Tends to be more amorphous
• Benefits from the wild alcohol
ink colors
• Enables white spaces
• Can muddy colors
Machine Co-Created Folk Art
• Tends to follow classic
compositing and layout
• Has a defined area of visual
interest
• Tends to fill up the full canvas
(unless seeded with an image
with white space)
• Tends to have some go-to color
palettes
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14. Initial Simple Contrasts(cont.)
Beginner Folk Art
• Requires physical cleanup
Machine Co-Created Folk Art
• Provides structure and form from
algorithmic pareidolia
• Poor at anatomically correct limbs,
grids, mapping, and other aspects
• Can be dominant, creating whole
scenes and characters from a
simple text prompt
• Can run out of energy points when
on a “hot hand” run of creativity
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15. Initial Simple Contrasts(cont.)
Beginner Folk Art Machine Co-Created Folk Art
• Can be unduly influenced by the
visual tendencies of the
diffusion-based art-making
generative AI
• Can be inspired by fellow users
of the art-making generative AI
platform
• Sense of visual refinement,
visual finish
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16. Initial Simple Contrasts(cont.)
Beginner Folk Art Machine Co-Created Folk Art
• More cultural references
• More humor
• Less of an original artist hand
• Some eye-popping outputs
(when seeded with both textual
and visual prompts)
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18. Aesthetics
• Recurring themes, scenes, characters, messages, and aesthetics; the artist
“hand” (what is personally resonant); the artist “imagination”
• With analog art materials, aesthetics cannot be effectively expressed
because of the materials and the skills limitations
• The aesthetic is expressed in the conceptualization and the attempts
• With digital art, the aesthetic is expressed in the text and visual prompting,
digital image editing (and evolving)…and in the curation of what images are
kept (and how they are labeled and presented to the public)
• Generative AI as just another art tool, painting with pixels
• Different conventions in aesthetics based on the different art practices
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20. Finding Audiences
• Creating a following in real space is harder than online
• Galleries, farmers’ markets, and other venues cost money to both the
common artist (and the customers)
• Online audiences are prebuilt into the digital generative AI (GAI) tool
• Online audiences do not have to pay to engage except with their attention
and social commenting
• Other digital destinations include online newsletters, email campaigns, social
art-sharing sites, and others
• The works are all about craft, not high art.
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22. Some Early Thoughts
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24. Art-making Generative AI
• Art-making GAI is about the human-machine collaboration.
• A range of work pipelines may be applied for different visual effects.
• Digital image editing should be part of the sequence. Analog, too.
• This painting with pixels should be used for learning.
• It can be used as an extension of self-exploration and artistic visioning.
• It should not be used towards deskilling in the physical real though.
• Creating analog visuals to “seed” (or “prompt”) art-making from a generative AI is a
different sort of painting. It is about informing the generative AI with visual color,
layout, white space, and so on. Photos, social network graphs, data visualizations,
and other visuals may also be used to seed AI-generated images.
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25. Conclusion
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27. What Works Best?
Machine Co-Created Folk Art
Beginner Folk Art
x
x
Self-expression, poetry
x
Visual exploration, visual learning
x
x
Stress relief, distraction
x
Entertainment (for self, for others)
x
Social contribution, sharing
x
x
Thinking
x
Social commentary
Other
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28. Summary Findings
• The machine-assisted art generation ticks all the boxes for common
art art-making.
• For “self-expression, poetry,” “stress relief, distraction,” and
“thinking,” the manual common art-making also benefit.
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30. Conclusion and Contact
• Dr. Shalin Hai-Jew
• haijes@gmail.com
• The visuals here are from the Deep Dream Generator. All were seeded
with original analog works (and text prompts and modifiers) by the
presenter.
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