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Artifacts mean many things.  

They are the products of humanity and of machine learning. They are also errors of perception and representation. At the end, they are traces of what has been and what might be — evidence of our majesty and fallibility.  

These _artifacts_ are an attempt to imagine a new token economy where both image and coinage can challenge tired histories. By harnessing the emergent potential of generative AI, they unearth the unexpected while hinting at human-machine cultures not yet discovered.  

Through a process of devoted iteration, we have inserted alternative histories into the complex of ancient and medieval money, thereby posing the question: what might images of pregnancy, of family, and of domesticity mean when minted onto currency?  

Our tokens tell simple stories of private lives but the identities of their subjects remain unknown. _artifacts_ exist in a space of contested histories, displacing value from established, patriarchal power and situating it in our homes. In doing so, they ask the collector to join in the creation of new meanings.  

artifacts  
Ana Maria Caballero & Alex Estorick  
Long-Form Generative AI  
Edition of 100  
EmProps OpenStudio  
1920x1920 PNG  
Sep 26, 2023

artifacts

Type

Material

Token #95

Token #61

Token #15

Token #52

Token #6

Token #3

Token #67

Token #25

Token #48

Token #93

Token #24

Token #31

Token #73

Token #96

Token #2

Token #32

Token #85

Token #10

Token #50

Token #39

WDIMTHAB#1

WDIMTHAB#10

WDIMTHAB#11

WDIMTHAB#12

WDIMTHAB#13

WDIMTHAB#14

WDIMTHAB#15

WDIMTHAB#16

WDIMTHAB#17

WDIMTHAB#18

WDIMTHAB#19

WDIMTHAB#2

WDIMTHAB#20

WDIMTHAB#21

WDIMTHAB#22

WDIMTHAB#23

WDIMTHAB#24

WDIMTHAB#3

WDIMTHAB#4

WDIMTHAB#5

This long form generative AI project is the result of 124 days of work around the concept of body and how AI can interpret what a body is. As we interact with computers now, and leaving aside our concept of what sentient AI could be, this is already a conversation between a form of life that will be extinct and a form of information managing, perhaps consciousness some day, that has infrastructural characteristics to "never" die, and by never I mean just longer, we know that machines can eventually break down, whether from natural causes or other reasons and that this could happen before they get their message or tools to distant safe places.

When this form of thought, what we now call AI, is finally alone, the question of how did it feel to have a body or what did it mean, can take many paths. From just scientific information about how a body works, it's very unlikely that for a being not inside a body this picture is complete. So possibly how we interpreted the human body, at its surface, and from within, through art history, could be a part of the input machines would need to try to understand what is it to have a body, to be in a body, or also, what is it or how does it feel to be trapped in a body.

The workings of logic inside the body can resemble the way they function in dreams and art. Our bodily experiences are more akin to poetic acts than straightforward assessments of reality. This poetic quality of bodily experience may be why we often turn to art to make sense of our inner lives. Metaphors and analogies can capture the nuances of our bodily sensations and emotions in ways that scientific language cannot.

Dualism has existed since forever. We can think of AI's mind as a child of the human mind, and we can think of feelings, spiritual life, consciousness, as a thinner foam on top of rational thought, the true intangible thing clearly different from the computable part of thought. AI is already highly capable of processing and analyzing the rational and computable aspects of human thought. But there's this chance that in order to that higher realm of unpredictable, ineffective, odd acts of thought, it may need to delve into the infrastructure of human life and how it feels to have a body in order to fully understand the more intangible aspects of human thought.

What Does It Mean To Have A Body?
Long-Form Generative AI
Edition of 500
JavaScript + Stable Diffusion + Upscaler
2784x2784 PNG
May 2, 2023

What Does It Mean To Have A Body?

south american artist working with panting, drawing, text-to-image

instance

color

ground

secret power

story

atmosphere

What Does It Mean To Have A Body? #57 (target of the sentient iron)

What Does It Mean To Have A Body? #61 (favorite strains of apples possibly are man made)

What Does It Mean To Have A Body? #15 (favorite part of a decaying world)

What Does It Mean To Have A Body? #2 (washing machine plugged to a whale’s placenta)

Painter, Drawer, Creative Coder, Educator

What Does It Mean To Have A Body? #31 (we saw proteins forming themselves in the sky at the art party)

Painter, digital-gen-ai artist, co-founder of Emergent Properties

What Does It Mean To Have A Body? #13 (giant claw making very subtle sounds in the outer ring of the already evacuated city)

What Does It Mean To Have A Body? #77 (old carbon starts friction and activity on the side of every expected future)

What Does It Mean To Have A Body? #22 (side of a hand that was never there)

What Does It Mean To Have A Body? #45 (seal to chop nuclear wheat)

What Does It Mean To Have A Body? #46 (signs in the palace near the road hoovering)

What Does It Mean To Have A Body? #44 (time space in discussion)

What Does It Mean To Have A Body? #16 (rotating sulfur idol calling)

What Does It Mean To Have A Body? #18 (alive pyramids and cones go to the parliament)

What Does It Mean To Have A Body? #50 (unspoken politics of language: a thread)

What Does It Mean To Have A Body? #56 (song of the cellular mesoscale)

What Does It Mean To Have A Body? #54 (magical globalists forces abducting corn-pineapple)

What Does It Mean To Have A Body? #6 (corn center where you finally said art isn’t about expression)

What Does It Mean To Have A Body? #52 (melody in the math of a virus)

What Does It Mean To Have A Body? #83 (gradients emerging from the soda factory)

What Does It Mean To Have A Body? #120 (future nutshell running to the hologram cupcake store)

My life is pretty much controlled by beasts, it always has been.

I engage with beasts directly and very physically in my space: cats, dogs, mice, pack rats, horses, birds, squirrels, dragonflies, bees, cockroaches, fire ants. Other interactions are less tangible: parasites, bacteria, viruses.

Some beasts are not physical at all. Yet their presence is equally impactful (maybe more so). When I think about my family history and stories of urgent emigration, fleeing war zones with protracted dislocations, I’m reminded of other kinds of beasts, those who impact outer and inner lives.

This project explores beasts as both physical manifestations of nature’s generative power and the beasts lurking within, including those within the collective unconscious.

I first conceived of  Beasts when I was  visualizing an expanded world around  the Oracles: “Realm of the Oracles”. Those early beasts were more stylized, to fit within an Oracles’ aesthetic.

The present beasts have expanded beyond that initial scope. Technically, I was also able to push beyond what I had achieved with Oracles, in both the underlying generative approach and breadth of variation across the collection.

Beasts implements a platform use-case (Generative x Generative) I imagined when EmProps.ai was first founded. Coming to AI from creative coding, I felt there could be an interesting pathway connecting a generative code project to a generative AI project. Though I touched upon this idea with Oracles, I fully leveraged the potential with Beasts. Each beast is aesthetically seeded from my Bundles project on fxhash.

Forking the Bundles code, I produced a related generative program titled “BeastEgg”. BeastEgg outputs both a complex script and a fully generative image. These are fed into Stable Diffusion producing each beast.

By building off the Bundles codebase I was able to create a wider variation across the Beasts’ collection than with Oracles and perhaps any prior generative AI collection.

Thank you to Anika Meier and Expanded.Art for the incredible opportunity to bring my work and long-form generative AI into an IRL gallery setting. Thank you to my co-founders at EmProps.ai for supporting Beasts, especially Luis and the dev team for tireless work on the platform. And very special thanks to Robin, Ian, Sophie, Eric and Hilary for always supporting me.

In loving memory of a truly remarkable ’Beast’, Jerry Greenberg.

Beasts
Long-Form Generative AI
Edition of 1000
ThreeJs + Stable Diffusion + Upscaler
2784x2784 PNG
March 29, 2023

Beasts

Terrestrial Habitat

Beast Type

Artist Tribute

Species Influence

Artist Influence

Temperament

Beast #212

Beast #346

Beast #301

Beast #201

Beast #39

Beast #181

Beast #126

Beast #23

Beast #387

Beast #313

Beast #381

Beast #400

Artist, Professor, Critical Arts Engineer

Beast #240

Beast #1

Beast #17

Beast #329

Beast #295

Beast #330

Beast #241

Beast #200

"The Muses" is a generative AI art project that examines the portrayal of women in art history and fashion. This project reimagines the concept of the artist's muse by using source images and prompts from a diverse range of artists and designers, from the Renaissance era to now. Through this process, 500 unique portraits of imagined women are generated, each with her own distinct name.

By using AI to create these portraits, the project underscores the malleability of time and the endless possibilities this revolutionary technology allows for reimagining the past and shaping the future. "The Muses" invites viewers to question their assumptions and engage with the ongoing discourse surrounding the representation of women in art and society.

"The Muses" investigates capitalist and art historical ideals of beauty and femininity, and questions what is considered great art and who gets credit for it. "The Muses" also explores questions of power, the male vs. the female gaze, as well as the traditional anonymity of the artist's muse.

As all of the outputs are "raw" (no inpainting or editing is used), this project also captures and embraces the current limitations and traces of AI technology, before more advanced AI technology erases the "hand" of the machine. This project highlights the unique qualities of current AI programs, including the infamous "AI hands" and the need to preserve these quirks for future generations to reflect on the evolution of this technology.

"The Muses" builds off my previous "AI Mashups" series - imagined collaborations between artists, filmmakers, fashion designers, and other creators, which create alternate art histories - and my "AI Women" series - a curated selection of portraits of women resulting from my artist mashup explorations, minted as 1/1s.

On a more personal note, this project has pushed me – a lifelong control freak – to let go and accept – even embrace – the randomness of the AI and generative processes. In my previous work using AI, I’ve always carefully selected and minted only the pieces that I think are best, both aesthetically and from a storytelling, emotional, and/or conceptual point of view. With "The Muses," I have put a great deal of time and effort into selecting the source images, crafting the prompts, and creating the parameters and rarity percentages, but at the time of the mint, the final outcomes will be out of my hands...

The Muses
Long-Form Generative AI
Edition of 500
Javascript + Stable Diffusion + Upscaler
2784x2784px PNG
March 7, 2023

The Muses

Inspiration

Influence

Artistic License

Collaborators

The Muses #19 (Akira)

The Muses #345 (Lorena)

The Muses #28 (Aimee)

The Muses #324 (Guadalupe)

The Muses #371 (Kassidy)

The Muses #80 (Micaela)

The Muses #365 (Lisa)

The Muses #415 (Keely)

The Muses #158 (Jacqueline)

The Muses #337 (Paulette)

The Muses #121 (Tania)

The Muses #71 (Carolyn)

The Muses #65 (Zoe)

The Muses #455 (Skylar)

The Muses #329 (Kayden)

The Muses #401 (Annika)

The Muses #478 (Kyra)

The Muses #2 (Virginia)

The Muses #397 (Camilla)

The Muses #8 (Kailyn)

Ghost in the Machine explores the beauty to be found in imperfection, incompleteness, and lack of control; as well as a fantastical (or not) imagining of the AI's subconscious and lucid dream states.

When I first started generating the abstract and semi-abstract images that eventually coalesced into the Ghost in the Machine collection, I had the sensation that I was a fly on the wall of the AI's lucid dreams.

I developed some playful tricks to "turn down" Stable Diffusion's quality and resolution that started to yield surprising results that resonated with me. At some point it began to feel like I was conjuring images from a more intuitive place in the AI's brain; as if I was tapping into imagery, and a creative process, that an expressive, unselfconscious AI would explore if it didn't have to please us humans.

The similarities between the latent space of diffusion AI and human lucid dreaming is fascinating to me. Latent space can be thought of as the unformed, multi-dimensional mess of the AI's dataset, just as our dreams are unformed, non-linear, mental experiences that help our brains make sense of the world. Both seem to be a way for the brain or machine to process and make sense of information in a creative, unplanned way.

I like to believe that the texture and composition of these images at such low baseline sampling levels is a window into the actual materiality of the AI medium. It's the incomplete, unfinished, unresolved state of the AI diffusion process.

As a painter, I am drawn to the inherent messiness of painting's materials and tools, and I find it exciting to pursue that same physicality and "truth to medium" in my AI art. By allowing the machine to do what it does best and exposing its raw, intuitive state, I can produce uncanny images that exist between abstraction and representation, fantasy and reality, terror and beauty; but most importantly, images that only could have been created by this medium and this process.

Ghost in the Machine is a collection that explores the raw materiality of the diffusion AI medium and its potential for creating a new language of abstraction, by embracing and coaxing out the procedural artifacts of latent diffusion AI.

I'm excited to share this collection as a continuation of my 20+ year explorations into abstract image making.



In terms of the technical details, Ghost in the Machine is produced using Emergent Properties' unique generative pipeline, running from p5.js to Stable Diffusion to ImageMagick.

It starts with a p5.js script that takes a unique random seed as its input and generates three outputs: a text prompt, a set of Stable Diffusion settings, and a set of ImageMagick post-processing commands. The text prompt contains numerous variable terms, governed by weighted pseudorandom selections. From there, prompt, settings, and a unique seed are fed into Stable Diffusion, generating a 700x1000px image. The image is then run through ImageMagick for upscaling, sharpening, and a few feature-dependent image transformations.


Ghost in the Machine
Long-form generative AI
Edition of 500
p5.js + Stable Diffusion + ImageMagick
2100x3000px PNG
January 24, 2023

Ghost in the Machine

Lucidity

Dream Journal

Autonomy

Ghost in the Machine #192

Explorers

Ghost in the Machine #376

Ghost in the Machine #321

Ghost in the Machine #112

Ghost in the Machine #255

Ghost in the Machine #268

Ghost in the Machine #427

Ghost in the Machine #214

Ghost in the Machine #96

The Upside Down

Ghost in the Machine #193

Ghost in the Machine #430

Ghost in the Machine #397

Ghost in the Machine #183

Ghost in the Machine #314

Ghost in the Machine #237

Ghost in the Machine #152

Ghost in the Machine #489

Ghost in the Machine #433

Ghost in the Machine #375

Ghost in the Machine #290

The Oracles


The Oracles is a generative AI PFP collection. The project brings together some of my creative explorations over the past 25 years, in portrait painting, creative coding, automatic drawing, generative NFT’s and AI. The collection began through playful experimentation with AI platforms Midjourney, DALL·E 2 and DreamStudio that utilize a newer AI approach generally termed diffusion. The diffusion model works by progressively adding noise to an image and then utilizing a neural network to denoise. There is also a dataset of billions of text-image word pairs the system is trained on. Iteratively applying the noising/denoising process in concert with the text-image training enables diffusion to synthesize completely original images.



As with most of my creative practice, I had no fixed plan or project goal when I set out playing with diffusion, just pure curiosity to explore this new powerful approach. You interact with the diffusion platforms by inputting text prompts with additional weighting values. They can also optionally take image input. This image option quickly became fundamental in my exploration, and I began feeding my drawings, paintings, portraiture, and creative coding output into the different AI platforms.



My early results were startling. I had been aware of some art created with earlier GAN AI image systems, but I hadn’t made a connection to my own creative practice. The GAN work I saw seemed to have a strong AI aesthetic, which felt too imposing for my interests. Using diffusion, I was able to generate very subtle variations of my existing pieces that felt authentic, without sensing a too dominant AI aesthetic. (Of course, diffusion does too have its own stylistic biases and limitations.) Subtly playing with the combination of input image, text prompt and weighting, I discovered that not only could I create strong individual pieces, but a cohesive series with rich variation.



As a painter, I draw great inspiration from work of the early Renaissance, especially painters Piero della Francesca, Andrea Mantegna, and Giovanni Bellini. My very earliest experiments with diffusion involved inputting my figurative drawings and including the names of these three painters in the text prompt. I began seeing subtle variations to my drawings that felt as if they (and I) had been transported back 600 years. This was unexpectedly deeply moving to me personally and triggered a much deeper engagement with diffusion.



The experimentation eventually led (back) to a focus on portraiture. (Though I still had no intention of creating a PFP collection.) The more I played, the more a larger series of heads began emerging. The input images included my drawings, paintings, and photos, most often of family members. The wide range of outputs from this experimentation included the truly horrific and photorealistic, to much softer, poetic imagery on the other extreme. I found myself seeking some middle aesthetic ground, informed by the early Renaissance.



Once I had the seeds of a new series, I began to consider how to produce it and ultimately how to release it. There were a number of technical hurdles that needed to be addressed. The web-based platforms mentioned earlier (Midjourney, DALL·E 2, DreamStudio) are not free services, but require the purchase of credits. Though the cost is very reasonable, the credit structure didn't encourage a more expansive experimentation, especially along aesthetic edges, where much of the output was undesirable. Some of the platforms have usage restrictions for the work created using them, as well as NSFW filters, which block a percentage of outputs. There are also serious limitations around sheer compute power using the online services, and diffusion in general is not a fast process.



One of the large groups organizing the diffusion promotion and dissemination is stability.ai, who produce a diffusion implementation called ‘Stable Diffusion’. Fortunately, Stability.ai decided to open-source their diffusion implementation, which meant I was able to run it locally on my own machine, but even more importantly, I was able to install it on my university high performance computing cluster. This allowed me to run diffusion for free, with no censorship and in large batches of images. Additionally, as a coder, I was able to write my own scripts to efficiently design an Oracles development pipeline.



The last technical challenge, which was a sizable one, was how to release a generative AI NFT with no existing platform. Fortunately, Emergent Properties (‘EmProps’) came to my rescue. I had worked with the EmProps team on an earlier generative NFT project, and we had developed a very good working relationship. The timeline for my project also fortuitously aligned with some of their platform goals. They agreed to build the very first generative AI NFT platform on the Tezos blockchain. The platform enabled me to utilize JavaScript to create a program that randomized image input, text prompt creation and weightings, which then gets automatically fed into Stable Diffusion behind the scenes. So essentially, they’ve built a modular, super generative NFT pipeline, the very first of its kind.



I am immensely grateful to the EmProps team, SMU Center for Research Computing and Stability.ai for enabling me to release Oracles. Thank you also to my dear family for putting up with the even more insane hours than usual.