Learnings after 2.5 years of running an AI lab at an Art University (XLab)
- 🎸 It’s a wild time for Generative AI
- 🌄 A new era of (creative) AI has begun
- 🗣️ Language input has democratized AI
- 🔲 Foundation Models are here to stay
- 🖼️ “AI art” is now mainstream
- 😎 AI myths/bullshit & how to stay cool
- 🧑🏫 What to teach? Expertise/Skillset/Literacy
- 🧠 Building up ML Intuition
- 🦮 Help for self-help
- 🤓 Technical soft skills are equally important
- ⚔️ Teaching a critical attitude towards AI
- 🕹️ The evolution of no-code tools
- 🥨 AI education needs constant and diverse integration into teaching
- 👩🎓 There will always be these 3 types of students
- 📚 The problem with knowledge bases
- 🌎 The institutional landscape for Creative AI
- Wrap up
In spring 2020 the University of Art and Design Burg Giebichenstein launched the XLab — the first dedicated laboratory for Artificial Intelligence & Robotics at an Art University in Germany. My focus was on (creative) AI/ML, while my colleague Simon Maris was responsible for everything related to (creative) robotics.
What is a “lab”? When one thinks of a lab, one usually thinks of research. This report, however, will be about teaching, namely, what it means to teach Artificial Intelligence at a university that trains future artists and designers.
Before we start — Is there a need to explain why AI should be taught at an art school in the first place? The question has many different answers to it, check out my article here!
At XLab we developed different formats, from a podcast to student lab “residencies”. In the summer of 2022, we exhibited some of our research results at Futurium in Berlin.
UPDATE 11/2022: I am working at the AI+D Lab at Hochschule für Gestaltung Schwäbisch-Gmünd, as part of KITeGG, a joint project of five German universities on the integration of AI in design teaching.
🎸1. It’s a wild time for Generative AI
The (research) field of Generative machine learning has seen a crazy boost in the last 2,5 years. The synthetic creation of text, sound and images is getting more realistic and accessible at an unprecedented speed.
- You can now create photo-realistic images based on your wildest imagination — right on your laptop. (Stable Diffusion)
- You can tell your editor to write code for you instead of typing it yourself, e.g. to build a website (CoPilot)
- You can even create realistic video sequences without ever using a camera lens
- Have you been to a Prompt Battle yet?
What does this mean to fields like art and design mostly concerned with creating (generating) things?
🌄 2. A new era of (creative) AI has begun
During the last 2 years, some things have changed fundamentally. It’s a development that started with GPT-3 and culminated with DALLE-2 and Stable Diffusion.
- Language input has democratized AI
- Foundational models
- “AI art” is now mainstream
🗣 3. ️ Language input has democratized AI
The text-to-x paradigm enables a new democratization of AI tooling — the capability of writing in a natural language (mostly English) is everything that’s needed (and of course access to a computer and the internet)
🔲 4. Foundational Models are here to stay
The shift towards foundation models is a game changer. They are big models based on gigantic datasets that no single individual would be able to train on their own. These foundational models will not go away, they will only become more powerful.
What does this mean for us as creators? In the long term, it might shift our agency towards:
- Finetuning these models
- Combining different models
- Building integrations, interfaces and applications on top of these models
🖼️ 5. “AI art” is now mainstream
The conception that a computer can generate art has become commonplace. 2022 was the year that AI Art * became mainstream — mainly through the breakthrough of text2image tools like DALLE and Stable Diffusion.
Is this a bad thing? No. but it might require a redefinition of the identity of a lab that calls itself an “AI lab”. Or does it?
Many people don’t know that AI art existed long before 2022! There is a fascinating history of AI art, and there are entire research fields such as Computational Creativity that have existed for decades! One of my goals is to raise awareness of this history and the foundational work that has been done before the hype!
(*) At least the generative side of AI art. There are of course many creative applications that leverage other ML capabilities, like classification, e.g. with sensor data
😎 6. AI myths/bullshit & how to stay cool
AI Myths are still prevalent — even among the smart and critical species of art students! While the demystification of AI should be a core part of AI education, it’s also important to be patient.
At the opening presentation of the XLab in 2020, one student in the audience raised his hand and asked: “And where can I now borrow the AI”? Implying that there was an embodied entity sitting in some corner of our lab.
I admit — It used to put me in rage every time I heard the phrase “an AI” or “the AI” because the wording implied that there really was something like “an intelligence” — a sentient being that acts according to its own goals and intentions.
This isn’t just a grammar quirk, but a categorical error: It’s confounding narrow AI (what we have) for AGI or strong AI (sci-fi dream). In consequence, this misconception influences what we expect from AI technology and how we interact with it — also creatively.
But it’s also important to find the right moments for doing demystification. Hands-on interaction with AI technology is probably the best way of demystifying:
“Oh really? GPT can not even count properly??!”
🧑🏫 7. What to teach? Expertise/Skillset/Literacy
It can't be the goal to replicate a computer science curriculum at an art university. That’s simply not feasible and also not desirable! So what are the skills and knowledge that need to be taught? What should an art design student know about AI?
Fact is: Machine Learning is orders of magnitude more complex than, let’s say, Frontend-Development. Is that even a fair/just comparison? I think yes because both are sets of technologies instead of just one technology.
Doing machine learning “from scratch” does not only require some proficiency in the basic software-dev stack (Python, command line, git), but also machine learning skills and some math and statistics (which is what all ML is, essentially).
But what does “from scratch” even mean? It certainly means a different thing today than 5 years ago. There are always abstractions in various degrees.
In the same way, as there are drag-drop no-code tools to build websites, there are drag-drop no-code tools to do machine learning. But abstractions come with the drawback of limited freedom/customization.
In my teaching experience, I put an emphasis on explaining general concepts like supervised learning (lots of labelled data, correlations, optimization), instead of going deep into implementation details (gradient descent, etc.)
🧠 8. Building up ML Intuition
It's easy to teach the use of AI tools, but it's quite hard to teach and train intuition for the general capabilities of ML, when it is useful and when not.
I always cite Rebecca Fiebrink at this point. She asks:
“When and why is it creatively useful to find patterns, make predictions and generate new data?”
(Rebecca Fiebrink at her 2018 Eyeo Talk, Video)
Often students came to our lab who simply wanted to somehow integrate AI into their projects. Like a vitamin kick. Or even worse: With the hope that “the AI” will automatically spit out a whole project, like the design for a website, in an end-to-end manner.
🦮 9. Help for self-help
Often it is helpful to just mention and explain certain terminology, then the students can do an internet search themselves:
Sentiment Analysis, Facial Landmarks, Latent Space Walk
Terminology is key to helping students to navigate the field independently. And being able to navigate independently is key because teachers and a lab have limited capacities for individual support.
🤓 10. Technical soft skills are equally important
Abstracting your usecase, use right terminology in order to search for tutorials and code on a similar problem and adapting the code to your usecase.
Debugging skills, strategic googling, frustration tolerance. Desensitization towards the red colour of error messages.
These are “soft” skills (or should we call them “essential” skills?) that are equally important.
⚔️ 11. Teaching a critical attitude towards AI
My goal was and still is to train students to participate in the social discourse about AI as critical thinkers.
This includes raising awareness about the real problems with AI: Worker rights instead of robot rights. Bias issues and the automation of inequality.
For artists and designers this is equally important!
🕹️ 12. The evolution of no-code tools
In early 2020, RunwayML was our go-to starting point. It was the no-code AI tool that was easiest to use. Besides being like an “app store” for pre-trained ML models, RunwayML also offered the possibility to train custom models with your own dataset. That turned out to be a good instrument and platform for hands-on teaching. However, students required more flexibility.
Google Colab was a platform that we also used in teaching. Students could run open-source repositories they found on Github, without the need for an (often complicated) local setup. Computing power (GPU) could be rented on demand. Drawback: Google Technology.
There is a tradeoff between high-level GUI tools (RunwayML, Teachable Machine) and low-level toolchains (individual Python scripts, Colab Notebooks, etc.) in terms of flexibility
Now, text2x tools (like GPT and DALLE) are the newest generation of no-code AI tools!
🥨 13. AI education needs constant and diverse integration into teaching
It isn’t ideal to teach AI as an isolated subject. Instead, it needs constant integration in teaching, it needs consolidation. It should be taught as “salt” to different applications, “rather than its own food group” (as mimi onuoha and mother cyborg put it in their “People’s guide to AI”). For example:
- ML for film/video: video editing, semantic footage management, etc.
- ML for drawing: assisted drawing, etc.
- ML for interface design: making sense of sensory data, etc.
- and many more…
👩🎓 14. There will always be these 3 types of students
- Those with no knowledge of programming and no ambition to learn it → use nocode tools
- Those with some experience in programming but are missing ML foundations, can work on code level up to a certain degree
- A few who are willing to tenaciously learn what is needed to build custom things
There will always be individual students vehemently committed to nerding out on something, and who go very far in that respect at certain points.
📚 15. The problem with knowledge bases
Knowledge bases can be a useful building block of AI education. However, knowledge bases require permanent maintenance because of the speed of development in the field. An up-to-date Google search usually brings more useful results than outdated knowledge. Persisted knowledge becomes stale.
🌎 16. The institutional landscape for Creative AI (Institutions and Ecosystem in Germany and Europe)
When we started the lab in 2020, there was only a small amount of German institutions that had an interest in creative AI, mostly as part of a broader critical AI discourse:
- KIM (Karlsruhe University of Arts and Design)
- Schaufler Lab (Technical University Dresden)
In the UK there were
- the Creative AI lab (a collab of Serpentine R&D & King’s College London)
- UAL Creative Computing Institute (with Creative AI education pioneer Rebecca Fiebrink)
In the US, there were many pioneering institutions and communities that very early acknowledged the potential of ML for art and design: The school of Poetic computation, Frank Ratchye Studio for Creative Inquiry, the Processing community, …
Not to forget, the online community AIxDesign was already striving, and still is! Check them out :-)
In December 2021 the German research project KITeGG (“Making AI Tangible and Comprehensible: Connecting Technology and Society Through Design”) was launched as a collaboration of 5 German art universities. One of its outstanding goals is to develop its own computing infrastructure that frees users from being reliant on Google’s cloud infrastructure.
17. Wrap up
I am far from having all the answers to the question: How do you teach AI at a university of the arts? And the answers have to be found anew in parallel to the constant developments.
We need to talk about what is considered basic or essential AI skills in art and design education:
How important are technical implementation skills really?
- Coding skills and proficiency in coding ecosystems
- Deep learning knowledge, including math and statistics
How much should we invest in “discourse skills”?
- AI ethics (bias, intellectual property, power structures, worker rights, environmental issues)
- Some immunity against AI bullshit in the media
- An intuition for the limits and drawbacks of AI solutions, when to say ‘no’
Fortunately, there is KITeGG, where implicit and explicit research is being done on these questions! And I am excited to be a part of that!!
Appendix
XLab
The XLab was part of the EFRE-funded project “BurgLabs” together with its two sister labs for sustainability (SustainLab) and biotechnology (BioLab) at the University of Art and Design Burg Giebichenstein in Halle, Germany. It started in May 2020.
AI+D Lab
Lab for AI and Design at the University of Design Schwäbisch-Gmünd, within the framework of the KITeGG research project.
KITeGG
A four-year BMBF-funded research project on design and artificial intelligence that started in December 2021. Behind KITeGG are 5 German (art) universities. Their research goal is among others to develop teaching concepts related to the topics of “Creative Machine Learning”.
