Artistic Intelligence: Machine Learning, Art Ed & Gen C
This week I facilitated a full-day, pre-conference workshop and gave a keynote at the New York State Art Teachers Association or NYSATA. The theme of the conference is/was Artistic Intelligence. The workshop, titled “Art & Algorhythms,” explored algorithms and machine learning as tools to uncover new ways of working and creating. The session consisted of activities and provocations such as prompt engineering and using AI art generators that are trained on vast datasets of images. They were asked to imagine a library with millions of artworks from various styles, periods, and artists that can be combined or remixed to make art.
The educators participated in a Teachable Machine-inspired, pre-AI design thinking activity. Teachable Machine is a web-based tool that introduces people to creating machine learning models. They also delved into prompt engineering, which is the process of writing instructions to guide AI training models to generate specific responses (ex. images, audio). Remixing (remix culture) was highlighted, as was looking at the traditional art-related movements such as Cubism, Dadaism and Surrealism that served as archetypes for AI developers, engineers and researchers.
Both NYSATA events were well-received. While I was at the conference I saw a new blog post by Ryan Jenkins, educator and tinkerer at Wonderful Idea Co. Ryan facilitated a workshop on tinkering with AI and creative computing in collaboration with the NEXUS experiments group for local researchers, librarians, teachers and after-school educators to test out ideas around playful tinkering-based explorations of AI topics.
Taking inspiration from Mitchel Resnick’s article “Generative AI and Creative Learning: Concerns, Opportunities, and Choices” (https://lnkd.in/eFwQAKBG) and Eric Rosenbaum’s experiments bringing generative AI images into Scratch projects(https://lnkd.in/eNbQ236w), we made interactive digital stories using the face sensing extension on Scratch Lab (https://lnkd.in/eBiZJVC5) and reflected on the ways that AI-based tools could be introduced to our learners with open-ended projects that build a sense of agency.
Note: In 2021 and 2023 we explored Scratch face sensing as part of a pre-college summer course titled “Art, AI & Robotics.”
One of the NYSATA educators asked me about how AI (AI art) was impacting the workforce. I showed the teachers a few examples of artists and designers who were using machine learning to augment their work, including Erykah Badu, Iris van Herpen, King Willonius, and Bill T. Jones. Mattel recently released a job notice seeking a Creative Lead to join their Future Lab Discovery team that is specifically focused on ideating and validating responsible AI-driven products and experiences. Gen C podcast hosts Sam Ewen and Avery Akkineni talked with Mattel VP Ron Friedman about the rise of immersive, personalized, and virtual brand experiences (e.g., roblox, tiktok, youtube and minecraft).
Roblox and Minecraft are online game platforms that allows users to program and/or play games created by themselves or other users. Tiktok and YouTube are social media platforms that attract young influencers or wanna-be influencers and audiences. Of course companies like Mattel who develop products for young people want to follow the trends. However, educators like Ryan Jenkins and the teachers we work with in MA are looking for ways to enlighten and engage diverse learners. They are also working hard at keeping pace or catching up with the latest developments. Thus, the need for more professional learning and development experiences and experiments with machine learning or AI.
The landscape that merges art/creativity, culture, human and machine learning, and workforce readiness is an ever-evolving and dynamic area that dissolves silos that isolate systems, processes, departments from others. Each field or discipline is being challenged to think differently about roles, methods and ways of doing to better prepare their members for what is to come. I agree to do workshops and run courses as a way to make AI and specifically machine learning more accessible to historically marginalized, underrepresented, underestimated, or undervalued people.