Archeology of Generative AI

Display Schedule

Code Completion Credits Range Language Instruction Semester
373MGAI credit 1 9 hours (45 min) of instruction per semester, 18 to 23 hours of self-study English summer

Subject guarantor

Georgy BAGDASAROV

Name of lecturer(s)

Georgy BAGDASAROV

Contents

Generative AI, which uses machine learning algorithms to create new content based on patterns learned from existing data, has become increasingly popular in recent years. From its early days as a theoretical concept to its current state as a ubiquitous technology, AI has transformed the world in ways that were once thought impossible. There are opportunities for generative AI to enhance creative practice in new and exciting ways, to augment the creativity of human artists by suggesting new ideas. Additionally, generative AI can be used to create content that is impossible or difficult for humans to generate on their own. By generating a range of possibilities, artists can experiment with different styles and techniques, allowing them to discover new ways of working and develop their own unique voice.

However, as with any technology, the rapid pace of technological innovation can cause AI to become obsolete. Machine learning algorithms that were state-of-the-art just a few moments ago may now be considered outdated. This can lead to a situation where AI systems that were once considered cutting-edge become obsolete very quickly.

Another cause of AI obsoleteness is changes in societal values and consumer preferences. While generative AI has the potential to revolutionize the creative process, there is also concern about the potential for biased outputs. These biases can be reflected in the content generated by the AI, perpetuating stereotypes, discrimination and reinforce societal prejudices. Legal and regulatory changes can also contribute to the obsoleteness of AI.

Exploring generative AI algorithms in artistic practice can be a thrilling and rewarding experience, opening up new avenues for creative expression and experimentation. However, it is important to approach this exploration with care and consideration for the ethical and philosophical implications of generative AI in creative practice.

Learning outcomes

As generative AI becomes more sophisticated and powerful, it raises important ethical and social questions about the impact of technology on creativity, the role of the artist in the creative process, and the nature of innovation and originality. In this course, we're planning to explore generative AI algorithms in artistic practice as well as raise questions about the role of technology in creativity and the future of artistic expression.

Prerequisites and other requirements

Bring your laptop. Basics in programming can be an advantage. If you don't know what it is, we can live with that.

Literature

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? . In Conference on Fairness, Accountability, and Transparency (FAccT ’21), March 3–10, 2021

Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J. W.; Wallach, H.; Daumé, H.; Crawford, K. Datasheets for Datasets; arXiv, 2018. https://doi.org/10.48550/ARXIV.1803.09010.

Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y. Zhao. 2023. GLAZE: Protecting Artists from Style Mimicry by Text-to-Image Models. arXiv. DOI:https://doi.org/10.48550/ARXIV.2302.04222

Evaluation methods and criteria

Active participation in discussions and working on your own project during the class.

Note

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Further information

This course is an elective for all AMU students

Schedule for winter semester 2022/2023:

The schedule has not yet been prepared

Schedule for summer semester 2022/2023:

Date Day Time Tutor Location Notes No. of paralel
14.04.2023 10:00–17:00 Georgy BAGDASAROV Room No. 1
Lažanský palác		 lecture parallel1

The subject is a part of the following study plans