Creative AI: Images and Media
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There are 3 modules in this course
This course explores how artificial intelligence is transforming the way we create, interpret, and engage with images and visual media. Focusing on generative tools, datasets, and cultural impact, you’ll learn how AI systems are trained to generate images, how artists are using them creatively, and what ethical, legal, and political questions arise as a result. By the end of the course you will be able to:
- Explore how AI can be used to generate and manipulate images using techniques like GANs, CLIP, and diffusion models. - Understand the impact of datasets on the aesthetics and biases of generative AI, and how dataset design influences creative output. - Evaluate the ethical and legal implications of AI image-making, including issues of consent, appropriation, and authorship. - Experiment with text-to-image tools and other generative systems, gaining insight into how artists are working with AI in practice. Through hands-on activities, creative walkthroughs, and interviews with artists and researchers, you’ll investigate how generative systems work, reflect on how they relate to earlier image-making technologies like photography, and examine the social debates surrounding AI art platforms and dataset ethics. Featuring perspectives from leading artists and technologists working at the cutting edge of AI and visual culture, this course provides both the technical understanding and critical insight to begin experimenting with AI in your own creative media practice. No technical experience is required, just curiosity and a willingness to engage with new visual tools and ideas.
In this module, we’ll explore how AI can be used to generate images using a type of algorithm called a Generative Adversarial Network (GAN). We’ll highlight artists who have created work with GANs and examine how these tools are shaping contemporary creative practices. Alongside this, we’ll draw parallels between the history of photography and the rise of generative AI, considering how both technologies have transformed image-making and influenced the course of art history.
What's included
4 videos9 readings1 assignment2 discussion prompts1 ungraded lab
4 videos•Total 16 minutes
- As uncanny as a body•3 minutes
- Preserving cultural practices with AI art•5 minutes
- Media ecologies•4 minutes
- Generative AI and art history•3 minutes
9 readings•Total 75 minutes
- Introduction to generating images with AI•5 minutes
- The GAN algorithm •5 minutes
- Real or fake money?•5 minutes
- GAN training•10 minutes
- Introducing StyleGAN and deepfakes•10 minutes
- What is latent space?•10 minutes
- Artists working with GANs•10 minutes
- The changing media/technology landscape•10 minutes
- Module 1 Summary•10 minutes
1 assignment•Total 30 minutes
- Generative AI's impact on art•30 minutes
2 discussion prompts•Total 20 minutes
- Does your DoppleGANner resemble you?•10 minutes
- Share your favourite artists working with AI•10 minutes
1 ungraded lab•Total 60 minutes
- Code Walkthrough: Latent space•60 minutes
In this module, we’ll focus on how image datasets for AI are made, and explore the political and legal issues that arise from their creation. We’ll examine how these practices impact creative practitioners, consider how artists are engaging with AI, and discuss the legal challenges facing large AI art platforms.
What's included
7 videos8 readings1 assignment1 discussion prompt
7 videos•Total 36 minutes
- Breaching Copyright•6 minutes
- Are there legal risks to AI tools?•4 minutes
- Protections for artists•7 minutes
- Does this artwork breach copyright?•5 minutes
- The role of artists making datasets•2 minutes
- Making datasets for creative projects•6 minutes
- Ethics of making datasets•6 minutes
8 readings•Total 75 minutes
- The emergence of AI art platforms•5 minutes
- The backlash against AI art•10 minutes
- Lawsuits against AI art and tech platforms•10 minutes
- Protecting your work from generative AI•10 minutes
- Watch Blade Runner: auto encoded•10 minutes
- The legal concepts of fair use and fair dealing •10 minutes
- Artists who make their own dataset•10 minutes
- Module 2 summary•10 minutes
1 assignment•Total 30 minutes
- AI image datasets and consent•30 minutes
1 discussion prompt•Total 10 minutes
- Your thoughts on Blade Runner: auto encoded•10 minutes
In this final module of the course, we’ll take an in-depth look at text-to-image AI systems, which have risen rapidly in popularity in recent years. We’ll hear from artists and researchers who have worked extensively with these tools, using their projects as a lens to understand the creative and artistic implications of text-to-image generation. You’ll also take part in a practical activity reimagining animation using text-to-image techniques, inspired by the work of artist Adam Cole.
What's included
5 videos8 readings1 assignment1 discussion prompt1 ungraded lab
5 videos•Total 25 minutes
- What is prompt engineering?•4 minutes
- The story of Twitter artbot•4 minutes
- The story of Kiss/Crash•6 minutes
- AI art and readymade•4 minutes
- Historical parallels of generative art•7 minutes
8 readings•Total 80 minutes
- Associating text and images•10 minutes
- Generating images from text•10 minutes
- Generative Art on Twitter •10 minutes
- Kiss/Crash•10 minutes
- Reimagining animation with diffusion models•10 minutes
- The future of video generation•10 minutes
- Parallels with art history•10 minutes
- Future of AI art •10 minutes
1 assignment•Total 30 minutes
- Comparing text and image pairs•30 minutes
1 discussion prompt•Total 10 minutes
- Discuss your outcome•10 minutes
1 ungraded lab•Total 60 minutes
- Code walkthrough: stable diffusion•60 minutes
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University of the Arts London
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LearnKartS
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