This is a joint module for BFA students (ZHdK) and BA students of Computer Science (ETH) to bring together artistic and technological perspectives. Our starting point is to consider «Artificial Intelligence» (AI) as a historical-material practice, i.e. shaped by the concrete conditions of its development and use. We focus on “Generative AI” as a technological and artistic field, as well as a site of critical interrogation. We will cover themes such as “Bias in AI”, “Digital Colonialism”, and the potentials and limits of current AI approaches.
The presentations will be discussed in depth and key publications from computer science and art theory will be read and discussed. Experts from different fields and artists will be invited and selected artworks will be discussed. At the end of the module, interdisciplinary teams will develop concepts for joint practice-oriented projects.
This module is a cooperation between the Department of Fine Arts (ZHdK) and ETH AI Center. It is open to BA students from both institutions and requires no prior technical or theoretical expertise. On the first day, there will be a hands-on introduction to Machine Learning for BFA students.
Course requirements (for ZHdK students)
Time: 10:00–17:00
Place: @ZHDK Viaduktraum ZT 2.A05
Introduction to Machine Learning with Alexandre Puttick (ZHdK students only)
Alexandre Puttick works as a data scientist and researcher in applied AI. His research focuses on applications in Mental Health, bias detection and mitigation in language models and explainable AI. He is also a collaborator on the ZHdK-based artistic research project “Latent Space: Performing Ambiguous Data,” which explores the state in which different valid readings co-exist with data-driven systems.
Machine Learning Resources
Ben Grosser: Metrics in Social Media
Time: 10:00-17:00
Place: @ZHDK Viaduktraum ZT 2.A05
Nora Al-Badri is a multi-disciplinary and conceptual media artist with a German-Iraqi background. Her works are research-based as well as paradisciplinary and as much post-colonial as post-digital.
by Dr. Eva Cetinić, Postdoctoral Fellow. Digital Visual Studies, UZH
Her research interests focus on studying new research methodologies rooted in the intersection of artificial intelligence and art history. Particularly, she is interested in exploring deep learning techniques for computational image understanding and multimodal reasoning in the context of visual art.
Abstract:
Introduction to the concept of multimodality within deep learning - the “revolution of 2021” with multimodal foundation models (e.g. CLIP). Discussion of the various aspects and problems that arise from models being trained on hundreds of million image-text pairs from the Internet (e.g. bias, cultural specificity, limitations of risk mitigation techniques, consent of content use and copyright issues, etc.). Discussion of the concept of “prompting”; the notion of similarity between word and image; the relation to art - using existing art, creating new “art”; the aesthetics of generated images - how it started and where it is going; the potential impact on the perception of images and media content.
Mario Klingenmann & Google Culture. X Degrees of Separation. 2018
Bruno Moreschi. Recoding Art, 2021, 14 Min
!Mediengruppe Bitnik. Dada. State of Reference. (2017) & Same Same. Watching Algorithms. Cabaret Voltaire Edition. (2015)
Time: 10:00 - 17:00
Place: ETHZ, LFW B3, Universitätsstrasse 2, 8092 Zürich
Bias as “incorrect representation”/“systematic distortion” vs bias as “unacknowledged standpoint” (dt. Übersetzung)
Amazon Go - SNL, 13.03.2022
Bias In Data
Bias in Labelling
Bias in Institutional Interest
Bias in Modelling
Exploring Data in Machine Vision
The case of LLM (eg. ChatGPT)
Videos, artistic work
Reading in class
Sabelo Mhlambi. 7/8/2020. “From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for Artificial Intelligence Governance.” Carr Center Discussion Paper Series, 2020-009. Full-text PDF.
Further Reading
Develop a conceptual sketch of a project (situation or application) that deals with one or more issue(s) that are particularly relevant to the group from the discussions on bias, digital colonialism AI.
The sketch project can be based on AI, but doesn't need to be. You can use whatever medium you like to address the issues.
Situation or application that touches and references one or more issues that were discussed in the seminar AND that relates to a field or interest or an existing practice/ experience from the team members.
Goal is to present a conceptual sketch
Mixed teams (ZHdK and ETH)
Time: 10:00 - 17:00
Place: ETHZ, LFW B3, Universitätsstrasse 2, 8092 Zürich
Hannes Bajohr (Fellow Collegium Helveticum): Post-Artifical Writing: Authorship in the Age of Artificial Intelligence.
Time: 10:00 - 17:00
Place: ETHZ, LFW B3, Universitätsstrasse 2, 8092 Zürich
UChicago scientists develop new tool to protect artists from AI mimicry. Feb 14, 2023 | arxiv.org PDF
Eryk Salvaggio. CRITICAL TOPICS: AI IMAGES. Syllabus for 26 classes.