#MerZx: AI as Artist’s Augmentation
Unstoppable synergy of Art+Tech
Art has a need for disruption. Art has a desire to be disrupted. Art should be disrupted.
Dadaists, Surrealists, Oulipo, Fluxus, Postmodernists, Conceptualists — all proclaimed the need for a paradigm shift in art. Gone was the authoritarian and elitist relationship between art and the public. Well, not quite.
Umberto Eco spoke in 1969 at the symposium “Computers and Visual Research” (in the context of the generative art movement “New Tendencies”):
“The authoritairan relationship would change only if researchers and artists […] instead of creating objects, began to promote active participation” (New Tendencies, edited by Margit Rosen, 2011)
Fortunately, with the rise of generative AI since 2015, we are seeing this dream come true. ML researchers are diving deeper into developing creative tools, and artists are inspired and fascinated by AI models — they are coming together and delivering them:
Solutions.
The narrative of AI as the exclusive domain of nerdy research institutions is outdated. By enabling access to ML/DL approaches, AI will be democratised.
Generative AI offers artists new creative approaches. This doesn’t mean that all artists should jump on the AI bandwagon. But it does mean that we can’t — and shouldn’t — stop those who do. Creativity is untamed.
AI artis human art
There are so many discussions at the moment about art being negatively affected by AI. But also: there is a misunderstanding of what art is. Craft? Yes, it could be affected by AI — if models create visuals that are quantitatively and qualitatively more intense than their human counterparts. But is it art? Art is when human or machine visual products are embedded in concepts, stories, interpretations — made by humans. AI art is also a very human art. And art loves to question itself.
In our century, we are finally getting rid of the elitism and academism of art (at least in the generation of art). Everyone is empowered, everyone is an artist, as Joseph Beuys once said. (You will find more about this topic in my new book “KI-Kunst”.)
Two Types of Enablers
Two types of enablers give artists the power of AI:
- Software: AI-powered applications.
- Hardware: the resource-intensive route.
Both have their PROs and CONTRAs.
The Way of Software: Convenience.
The software approach is most useful for non-coders who want to experiment intuitively. New ML models are released almost daily, and new digital solutions are created: RunwayML, DALL-E, StableDiffusion, Artbreeder, Midjourney, Kaiber, TokkingHeads — the list goes on. They are all accessible via web applications and run in the cloud. You can even work with them on a smartphone and get amazing results.
The downside is the cost. You can use them until you run out of credit. Most of these solutions run as subscription services (some of them quite expensive), although most are based on open-source models. You have to balance the difference between intuitive use and the cost factor.
The Way of Hardware: More Effort
The Way of Hardware allows you to use open-source models indefinitely without worrying about running out of credit. All you need is a one-time investment: solid hardware and a fearless approach to coding.
Of course, cost is a factor here, too—you need a good GPU and processor for better and faster performance, which can be expensive. But this one-off investment gives you more freedom to test all the open-source models available without having to wait for them to be implemented in software. And you can run them as often as you desire without worrying about credits.
My transition
I am (or was?) a non-coder. My last computer language was BASIC (the old one, with numbered lines).
I was more of an aesthetic type of creative, trying new approaches if they were offered in an intuitive way. OK, I installed Deep Dream on my laptop back in 2016, which I recognize as my personal achievement. And yet, I was eager to try out new generative AI models despite having almost no knowledge of Python.
With the invention of Jupyter and Google Colab notebooks, we’ve had access to more and more novel approaches. You didn’t need to know how to code — just run it cell by cell. Sure, if you knew Python, you could experiment with more settings.
Thus, every time a new model was published, I was like…
All my articles — DeOldify, JukeBox, or GPT-2 — were based on Colab Notebooks.
But all the time, I was flirting with the idea of a hardware solution that would give me:
- unlimited computing power
- experiment with the latest models
- be independent of everyone and everything, and run the models locally.
And now it’s here. Using the hashtag #MerZx, I will tell you about my experiments with a local hardware workstation that opens up new possibilities.
HP Ambassador Merzmensch
Since the beginning of 2024, I joined the Hewlett Packard Ambassador program — with my agenda to support HP in developing a hardware (and software) path for artists.
In this blog I will focus on my experiences with:
- HP-Workstations Z6, Z8 etc (hence Zx in the title and hashtag)
- HP AI Studio (a completely new software solution, designed for machine learning specialists — but why not for artists?)
Sure, you can use your own hardware, but I will share my successes with the systems available for me, which were provided to me by HP.
Stay tuned to find out more!
