Learnings after 2.5 years of running an AI lab at an Art University (XLab)

The XLab at University of art and design Burg Giebichenstein started in spring 2020 (together with Corona). Simon Maris (left) and Alexa Steinbrück (right)
  1. 🎸 It’s a wild time for Generative AI
  2. 🌄 A new era of (creative) AI has begun
  3. 🗣️ Language input has democratized AI
  4. 🔲 Foundational Models are here to stay
  5. 🖼️ “AI art” is now mainstream
  6. 😎 AI myths/bullshit & how to stay cool
  7. 🧑‍🏫 What to teach? Expertise/Skillset/Literacy
  8. 🧠 Building up ML Intuition
  9. 🦮 Help for self-help
  10. 🤓 Technical soft skills are equally important
  11. ⚔️ Teaching a critical attitude towards AI
  12. 🕹️ The evolution of no-code tools
  13. 🥨 AI education needs constant and diverse integration into teaching
  14. 👩‍🎓 There will always be these 3 types of students
  15. 📚 The problem with knowledge bases
  16. 🌎 The institutional landscape for Creative AI
  17. Wrap up

🎸1. It’s a wild time for Generative AI

  • 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?

🌄 2. A new era of (creative) AI has begun

  1. Language input has democratized AI
  2. Foundational models
  3. “AI art” is now mainstream

🗣 3. ️ Language input has democratized AI

🔲 4. Foundational Models are here to stay

  • Finetuning these models
  • Combining different models
  • Building applications on top of these models

🖼️ 5. “AI art” is now mainstream

😎 6. AI myths/bullshit & how to stay cool

🧑‍🏫 7. What to teach? Expertise/Skillset/Literacy

🧠 8. Building up ML Intuition

🦮 9. Help for self-help

🤓 10. Technical soft skills are equally important

⚔️ 11. Teaching a critical attitude towards AI

🕹️ 12. The evolution of no-code tools

🥨 13. AI education needs constant and diverse integration into teaching

  • 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

  1. Those with no knowledge of programming and no ambition to learn it → use nocode tools
  2. Those with some experience in programming but are missing ML foundations, can work on code level up to a certain degree
  3. A few who are willing to tenaciously learn what is needed to build custom things

📚 15. The problem with knowledge bases

🌎 16. The institutional landscape for Creative AI (Institutions and Ecosystem in Germany and Europe)

  • KIM (Karlsruhe University of Arts and Design)
  • Schaufler Lab (Technical University Dresden)
  • 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)

17. Wrap up

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’

Appendix

XLab

AI+D Lab

KITeGG

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Alexa Steinbrück

A mix of Frontend Development, Machine Learning, Musings about Creative AI and more