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IM954 Generative AI: Histories, Techniques, Cultures, and Impacts

20/30 CATS - (10/15 ECTS)

Term 2

Module Convenor: Dr Michael Castelle

This module provides a general, hands-on, and critical introduction to recent developments in the field of contemporary artificial intelligence (AI) research, specifically focusing on the use of multi layered or “deep” artificial neural networks for productive or “generative” systems which create new artifacts, such as text, images, music, and speech; examples of these proliferating interactive artifacts include ChatGPT, which deploys generative text models in a dialogical mode and has gained widespread use for applications in communication, marketing, business, and e-learning; and StableDiffusion, which can generate an expansive variety of digital art given textual descriptions. Instead of treating these systems as radically novel inventions, students will learn to place them in their historical, philosophical, political, and ethical contexts of North American, European, and now global scientific and commercial research cultures as well as in cultural imaginaries of AI in popular literature and film; and while there are no prerequisites, students will have the opportunity to experiment with these models directly in an interactive environment, and thus gain a deeper — and increasingly crucial — technical understanding of both their powers and limitations. Finally, because the impact of Generative AI is an ongoing and evolving issue, the module will engage with up-to-date news media and political/regulatory developments in order to help students develop their ability to produce novel assessments and critiques of Generative AI-related impacts and controversies as they emerge and unfold.


20 CATs:

  • Group Presentation (marked collectively): 35%
  • Essay - 2500 words: 65%

30 CATs:

  • Group Presentation (marked collectively): 25%
  • Essay - 3500 words: 75%

Indicative Syllabus

  • Mechanical Production and Reproduction: A History of Generative Art, Language, and Music to the 1950s.
  • From ELIZA to AARON: A History of Generative Art, Language and Music, Part II: 1950s—2000s.
  • Artificial Neural Networks from the Perceptron to the Deep Learning Era.
  • Early Neurography: The ImageNet Dataset, Convolutional Neural Networks, and Creative AI.
  • Signals, Music, and Text: Wavenet, Jukebox, and Generative Pre-trained Transformers (GPTs).
  • Presentation and Discussion Week for Generative AI Controversies.
  • Multimodal Neurography: Text-to-Image Models (DALL-E, StableDiffusion).
  • Instruction Tuning, Human Feedback, and the Dialogical Transformer: Deconstructing ChatGPT.
  • Multimodal Dialogical Models: Consequences and Futures for Politics, Education, and Culture
  • Indicative Reading List

Agüera y Arcas, B. (2022). Do Large Language Models Understand Us?
Bakhtin, M. M. (1982). The Dialogic Imagination: Four Essays (M. Holquist, Ed.; C. Emerson, Trans.)
Barthes, R. (1977). The Death of the Author. In Image, Music, Text (pp. 142–148).
Bender, E. M., Gebru, T. et al. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Benjamin, W. (1968). The Work of Art in the Age of Mechanical Reproduction.
Boden, M. A. (1998). Creativity and Artificial Intelligence.
Bourdieu, P. (1996). The Rules of Art: Genesis and Structure of the Literary Field.
Brown, T. B. et al. (2020). Language Models are Few-Shot Learners.
Castelle, M. (2020). The social lives of generative adversarial networks.
Cohen, H. (1974). On Purpose: An enquiry into the possible roles of the computer in art.
Derrida, J. (1971). Signature Event Context.
Foster, D. (2023). Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play.
Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A Neural Algorithm of Artistic Style.
Globus, G. G. (1995). The Postmodern Brain.
Goodfellow, I. et al. (2014). Generative Adversarial Networks.
Jacobsen, B. N. (2023). Machine learning and the politics of synthetic data.
Luccioni, A. S. et al. (2023). Stable Bias: Analyzing Societal Representations in Diffusion Models.
McQuillan, D. (2022). Resisting AI: An Anti-fascist Approach to Artificial Intelligence.
Miller, A. I. (2019). The artist in the machine: The world of AI-powered creativity.
OpenAI. (2023). GPT-4 Technical Report.
Ouyang, L. et al. (2022). Training language models to follow instructions with human feedback.
Park, J. S. et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior.
Solaiman, I. (2023). The Gradient of Generative AI Release: Methods and Considerations.
Stark, L., & Crawford, K. (2019). The Work of Art in the Age of Artificial Intelligence: What Artists Can Teach Us About the Ethics of Data Practice.
Tunstall, L., Werra, L. von, & Wolf, T. (2022). Natural Language Processing with Transformers: Building Language Applications with Hugging Face.
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine.
Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgment to Calculation.

Learning Outcomes

By the end of the module, students should be able to:

  • Demonstrate in-depth knowledge of the conceptual foundations, cultural background, and historical impacts of generative systems for various forms of image/text/signal media
  • Demonstrate a critical understanding of the assumptions and present-day limitations of contemporary generative artificial intelligence (AI) models
  • Demonstrate a practical ability to interact with these systems in a hands-on and exploratory manner to help answer questions about generative AI
  • Demonstrate an appreciation and deeper understanding of the social, ethical, cultural, and philosophical implications and impacts of recent developments in generative AI
  • Demonstrate skill in analysis and interpretation of various media content around subjects in generative AI