Critical Topics: AI Images

Eryk Salvaggio

 

Critical Topics: AI Images was an undergraduate class delivered for Bradley University in Spring 2023. It was an overview of the emerging contexts of AI art-making tools that connected media studies and histories of new media art with data ethics and critical data studies. Through this multidisciplinary lens, we examined current events and debates in AI and generative art, with students thinking critically about these tools as they learned to use them. They were encouraged to make work that reflected the context and longer history of these tools.

As a final project, students collected 500-1000 of their own images, cleaning them to create a unique, personalized dataset. Then, using RunwayML, they extended StyleGAN2’s training data with their datasets to create a custom generative model. Along the way, we discussed the politics of image assembly and archives, the human labor of datasets and content moderation, and more.

The course included interviews with AI artists from a variety of perspectives. Students responded to each with short essays highlighting the diversity of thoughts and opinions about what AI art means, how it is made, and the ethics that surround it.

This website collects all of the asynchronous video lectures, alongside works referenced in the lectures. Guest lectures and artist talks are also archived, with permission.

I continue to write on these topics on my mailing list, Cybernetic Forests, which is free to read. You can sign up at the button below.

 

Are you using any of these materials in a class, paper, or other way?
Please let me know (I am happy for it, but documenting it helps me out!)

Thank you to Harvard’s and FU Berlin’s metaLAB for linking to this course across their AI Pedagogy Project, which is an excellent resource for instructors teaching critically with or about AI.

CLASS 1

Love in the Time of Cholera

An introduction to the idea of AI generated images an infographics, or data visualizations. We compare AI images to John Snow’s 1855 maps of the London Cholera epidemic to see how data moves into images, maps, and visualizations — and how reality is transformed as it is collected and represented.

This is a short introduction to the class.

Works Referenced:

  • Salvaggio, Eryk (2022) How to Read an AI Image. CyberneticForests. (Link)

CLASS 2

Cybernetic Serendipities

The history of the idea of Artificial Intelligence is paired alongside trajectories of art history, starting with automated chessboards in 1914 to computer-generated drawings in 1968. This section tackles neural networks, Turing machines and cybernetics alongside the work of Jasia Reichardt, Vera Molnar, Ben Laposky, Lilllian Schwartz, Gordon Pask, John Cage, Nam June Paik, Andy Warhol and Harold Cohen. 

To Read:

Works Referenced:

  • Up and Atom (YouTube Channel) Computer Science Was Invented On Accident

  • McCulloch, W.S., Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943). https://doi.org/10.1007/BF02478259

  • Eames, C., & Eames, R. (Directors). (1953). A Communications Primer [Film]. Eames Office, LLC.

  • Birhane, Abeba. (2020). Fair Warning: For as long as there has been AI research, there have been credible critiques about the risks of AI boosterism. Real Life. https://reallifemag.com/fair-warning/

  • Kubrick, Stanley (1968). 2001: A Space Odyssey.

  • Brecht, George (1965). Universal Machine II.

  • Reichardt, Jasia (1968). Cybernetic Serendipity (Interview). BBC Late Night Lineup, via YouTube.

  • Cohen, Harold (1994). The Further Exploits of AARON, Painter. Stanford Humanities Review. (PDF).

  • Cohen, Paul. (2017). Harold Cohen and AARON. AI Magazine, 37(4), 63-66. https://doi.org/10.1609/aimag.v37i4.2695

  • Cohen, Harold (1974) On Purpose: An Enquiry Into the Possible Roles of the Computer in Art. Studio International. (PDF).

CLASS 3

AI & Cybernetics: Computation & Art

A guest talk from Paul Pangaro of Carnegie Mellon University. President of the American Society for Cybernetics. Pangaro has restored one of Gordon Pask’s artworks, Colloquy of Mobiles, shown at the Cybernetic Serendipities show in 1968. In this talk, Pangaro speaks from his own expertise on cybernetics as we walk through Pask’s body of work connecting conversations and machines.

Read: Gordon Pask (1972) A Comment, a Case History, and a Plan
Watch: Interview with Lillian Schwartz, a pioneer of computer animation at Bell Labs. (9 Minutes).

CLASS 4

Who Decided the Colors of Birds?

 

Contemporary AI is defined by the use of data. But what is data? Where does it come from? We look at the most recent developments in AI imagery over the last decade. We begin with a unique concept of a dataset: the evolution of labeling and organizing colors, from hand-painted scientific folios to contemporary digital color palettes. How does data change our definitions and experiences of the world? We look at the historical dimension of AI images over the past 10 years.

Read: Catherine D’Ignazio and Lauren F. Klein (2020) Why Data Science Needs Feminism

Works Referenced:

  • Beverly Thompson and William Thompson (1985) Inside an Expert System. Byte Magazine. (Link)

  • Diana Forsythe (1993) Engineering knowledge: the construction of knowledge in artificial intelligence. Social Studies of Science 23: 445. 

  • CNN’s First Reports on the World Wide Web. YouTube.

  • Adam Curtis (2011). Loren Carpenter SIGGRAPH Experiment 1991. From the BBC Documentary, All Watched Over By Machines of Loving Grace.

  • MG Siegler (2010). Eric Schmidt: Every 2 Days We Create As Much Information As We Did Up To 2003. TechCrunch. (Link)

  • Digital Future Society (2021). Interview with Mary Gray. YouTube.

  • Hinton, G. E., Osindero, S. and Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation 18, pp 1527-1554. (Paper)

  • Alexander Mordvintsev, Christopher Olah, and Mike Tyka (2015). Inceptionism: Going Deeper into Neural Networks. Google Research Blog. (Link)

  • Jonah Nordberg (2015) “Inside an Artificial Brain.” Generative Artwork. (Link)

  • Abraham Werner (1812). A Nomenclature of Colours. William Blackwood Publishers. (Link)

  • Eryk Salvaggio (2020). Who Decided the Colors of Birds? Four Lessons About Data From the History of Color. Cybernetic Forests. (Link)

CLASS 5

 

Images and Surveillance: Nobody is Always Watching You

Some of the same AI technologies and training data used to make AI art tools are also used in surveillance systems, both in the United States and abroad. In this class session, we'll explore some of the social issues around AI images. Particularly, AI image datasets of faces issues of race and gender in surveillance technologies. Adapted from my 2021 keynote talk, From Big Brother to Big Data: Nobody is Always Watching You.

To Read:

Works Referenced:

Part 1: Studio Reflection Assignment

CLASS 6

 

For your first creative assignment, I’ll ask you to create a piece of art, using an AI tool of your own choice, that reflects on a topic of interest from the previous readings or classes. I’ll provide a list of tools and some examples, but you will be asked to define “AI” for yourself — and can include tools like George Brecht’s box of cut-up images (“Universal Machine II”) or contemporary AI tools.

We’ll also hear from Anuradha Reddy on a particular way of making art with AI generated images that reflects some of the conversations we’ve had in our class: using AI to connect to the roots and origins of computing back to Ada Lovelace and the Jacquard loom.

In your readings, you’ll encounter a number of artists working with AI today, from a variety of perspectives.

Part 2: Artist Talk: Anuradha Reddy

Anuradha Reddy (website) is an interdisciplinary design researcher based in Sweden. Her research practice includes interaction design, user research, data technologies, creativity, and hacking. She has a practice-based PhD in Interaction Design from Malmö University, Sweden. By day, she works in the software industry as a design researcher. Outside her day job, she continues researching ways to bridge professional ML/AI expertise with informal communities of makers/hackers through craftivist and critical approaches to design. Her recent design work has been covered in conferences, journals, periodicals and zines. She is an open advocate of FOSS/libre design and collaborative technologies. She holds workshops, talks, and writes for magazines.

Generative Adversarial Network Fever

CLASS 7

 

We look at Generative Adversarial Networks (GANs) and how they might reinforce — or disrupt — the logic of archives. How do the images generated by these systems tell stories about the underlying datasets? How can we, as artists, use this process to tell our own stories? We will also look at the history of GANs and their early reliance on datasets of European oil painting. How do the datasets available to us shape the work we make? How do these datasets reflect cultural contexts of museums and digitization?

We look to the work of Hannah Hoch, Lorna Simpson, Helena Sarin, Mario Klingemann, Gene Kogan, Obvious, and Robbie Barrat.

Works Referenced:

  • Ian Goodfellow, et al. (2020) "Generative adversarial networks." Communications of the ACM 63.11, 139-144. (Link)

  • Tomas Smits and Melvin Wevers, Abe Lincoln Image Classification Series (Image).

  • Ian Goodfellow (2019) 4.5 years of GAN progress on face generation. (Image) Twitter.

  • Google, Machine Anatomy: Diagram of Generative Adversarial Network.

  • Tero Kerras, NVIDIA Labs (2019) FFHQ Dataset. (Link)

  • Jacques Derrida (1994) Archive Fever: A Freudian Impression. University of Chicago Press. p. 3, p. 17

  • Jianglin Fu et al, NVIDIA Labs (2022) StyleGAN-Human: A Data-Centric Odyssey of Human Generation. (Link)

  • Hannah Hoch (1971) On Collage. In The Ends of Collage, ed. Yuval Edgar (2017) Luxembourg & Dayan Press p. 143.

  • Lorna Simpson (2018) Collages. Chronicle Books LLC.

  • Helena Sarin (Date Unknown) BashoGAN via AIartists.org.

  • Obvious (2018) Edmond de Belamy. Painting.

  • Robbie Barrat (2018) Output from Neural Network. Twitter.

  • Mario Klingemann (2018) Memories of Passerby. Interactive Video work.

  • Gene Kogan (2019) Generative Antipodes. AI/Video work.

  • Amadou-Mahtar M'Bow (1978) A Plea for the return of an irreplaceable cultural heritage to those who created it. UNESCO Courier. (Link)

CLASS 8

Diffusion, CLIP & LAION: Flowers Blooming Backward into Noise

 

How does a Diffusion model turn pure noise into an image of flowers in bloom? We discuss Diffusion models, the technology at the heart of DALLE2, Stable Diffusion and MidJourney. We’ll explore how Diffusion works and how language models steer images into being based on what you write. Then we’ll think about where the artistry lies in this process: is the AI making the art? Is it dreaming or imagining these images? We’ll look at John Searles’ “Chinese Room” thought experiment to think through those questions. Finally, we look at whether AI art is a radical shift in art making, or serves to extend a 60-year-old history of Generative Art from computers.

Artists in today’s lecture include Mezei Leslie, Georg Nees, Robert Mueller and Tim Klein and the 1972 Computer Art exhibition organized by Laxmi Sihara in New Delhi.

I aim to diversify examples of early generative artists for future iterations of this lecture, recommendations are welcome.

Works Referenced

Birhane, Abeba, et al. “Multimodal Datasets: Misogyny, Pornography, and Malignant Stereotypes.” ArXiv [Cs.CY], 2021, doi:10.48550/ARXIV.2110.01963.

Canaday, John (1970) “More Computers, Less Art.” New York Times.  

Carlini, Nicholas, et al. “Extracting Training Data from Diffusion Models.” ArXiv [Cs.CR], 2023, doi:10.48550/ARXIV.2301.13188.

Cole, David. “Artificial Intelligence and Personal Identity.” Synthese, vol. 88, no. 3, 1991, pp. 399–417, doi:10.1007/bf00413555.

Cox, G. Generator: The Value of Software Art. Edited by J. Rugg and M. Sedgwick, 2007, pp. 147–162.

Galanter, Philip (2003) What is Generative Art? Complexity Theory as a Context for Art Theory. Conference Paper, GA2003 – 6th Generative Art Conference. (Link)

Higgins, Hannah B., and Douglas Kahn, editors. Mainframe Experimentalism: Early Computing and the Foundations of the Digital Arts. University of California Press, 2012.

Mueller, Robert E. (1978) Idols of Computer Art. Creative Computing Magazine pp. 100-106. (PDF)

Schuhmann, Christoph, et al. “LAION-5B: An Open Large-Scale Dataset for Training next Generation Image-Text Models.” ArXiv [Cs.CV], 2022, doi:10.48550/ARXIV.2210.08402.

Searle, John R. “Minds, Brains, and Programs.” Readings in Cognitive Science, Elsevier, 1988, pp. 20–31.

Sihari, Laxmi (1972) Computer Art. National Gallery of Modern Art, New Delhi. (PDF)

Studio Reflection Presentation Day

CLASS 9

CLASS 10

Exploring the Datasets, Part 1

 

A deeper look at what drives Diffusion models. We'll explore the images inside the datasets used to train neural networks. Composite photography, statistical correlations and eugenics were all created by the same man — the British sociologist Francis Galton - in the 19th Century. We examine the legacy of this common root as it has reconvened in image synthesis — and the underlying principles of categorization and representation they enact. We look into the training data to see how today’s image generators categorize and represent people. What lurks behind seemingly innocuous prompts such as “brave person” or “black girl?” We also offer an introduction to weights and biases in AI systems.

Works Referenced

Burbridge, Benedict (2013) “Agency and Objectification in Francis Galton’s Family Composites.” Photoworks, 28 Oct. 2013, https://photoworks.org.uk/agency-objectification-francis-galtons-family-composites/.

Cain, Stephanie (2018). “A Gay Wedding Is a Wedding. Just a Wedding.” The New York Times, The New York Times, 16 Aug.

Galton, Francis (1878) “Step one in assembling a composite photograph” in Popular Science Monthly Volume 13, (August).

Hassani, B.K. Societal bias reinforcement through machine learning: a credit scoring perspective. AI Ethics 1, 239–247 (2021). https://doi.org/10.1007/s43681-020-00026-z

Jo, Eun Seo, and Timnit Gebru. “Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning.” ArXiv [Cs.LG], 2019, http://arxiv.org/abs/1912.10389.

Lee-Morrison, Lila (2019). "Chapter 3: Francis Galton and the Composite Portrait". Portraits of Automated Facial Recognition: On Machinic Ways of Seeing the Face, Bielefeld: transcript Verlag, 2019, pp. 85-100. https://doi.org/10.1515/9783839448465-005

Lipton, Zachary C. “The Foundations of Algorithmic Bias.” Approximately Correct, 7 Nov. 2016, https://www.approximatelycorrect.com/2016/11/07/the-foundations-of-algorithmic-bias/.

Noble, Safiya Umoja (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. Ch. 3, Searching for Black Girls, p69. New York University Press.

Offert, Fabian, and Thao Phan (2022) “A Sign That Spells: DALL-E 2, Invisual Images and the Racial Politics of Feature Space.” ArXiv [Cs.CY], 2022, http://arxiv.org/abs/2211.06323.

Treitler, V. (1998). Racial Categories Matter Because Racial Hierarchies Matter: A Commentary. Ethnic and Racial Studies, 21(5), 959–968

Turner, J. H. (2020) Positivism: Sociological via International Encyclopedia of Human Geography (Cambridge, Massachusetts: Elsevier) 11827. (Link)

Alamy Limited. “1940’s Photograph Hitler Youth Medal Iron Cross.” Accessed 18 Feb. 2023.

Exploring the Datasets, Part 2

CLASS 11

 

Do all “great artists steal”? A look at artists in the dataset of AI images, and the relationship between names and style: is it OK to use their name in your work? Several lawsuits around image synthesis suggest that making the work could be seen as derivative; others suggests the models themselves are a copyright violation. We compare image synthesis tools & artistry from the early days of music synthesis to today’s visual synthesis.

Finally, we look at appropriation through the lens of power: who are you stealing from, and what does it say when you do?

Artists mentioned in today’s lecture include Heather Dewey-Hagborg, René Magritte, Greg Rutkowski, Henri Pousseur, Delia Derbyshire, Robert Rauschenberg, Sherry Levine, Walker Evans, Marcel Duchamp, Patricia Caulfield, Andy Warhol, Elaine Sturtevant, Agnieszka Kurant, and James Bridle.

Works Referenced

Babbitt, Milton (1961) “Past and Present Concepts of the Nature and Limits of Music.” In Collected Essays of Milton Babbitt, edited by Stephen Peles, 78–85. Princeton, NJ: Princeton University Press.

Brody, Martin. “The Enabling Instrument: Milton Babbitt and the RCA Synthesizer.” Contemporary Music Review, vol. 39, no. 6, 2020, pp. 776–794, doi:10.1080/07494467.2020.1863011.

Douglas Crimp (1977). Pictures Catalog, Artists Space

Eklund, Douglas. “The Pictures Generation.” The Met’s Heilbrunn Timeline of Art History, 1 Jan. 1AD, https://www.metmuseum.org/toah/hd/pcgn/hd_pcgn.htm.

Heyer, Mark. 1975. Harry F. Olson, an Oral History. Hoboken, NJ: IEEE History Center. Accessed October 14, 2018. https://ethw.org/Oral-History:Harry_F._Olson

Scafidi, Susan (2005). Who Owns Culture?: Appropriation and Authenticity in American Law. Rutgers University Press, 2005.

Sembe, Karina (2023). “How Appropriation Works for Those Who Practice It and Those Who Fight It.” EastEast, https://easteast.world/en/posts/218. Accessed 20 Feb. 2023.

Siegel, Jeanne (1991). “After Sherrie Levine.” In Art Theory and Criticism: an Anthology of Formalist Avant-Garde, Contextualist and Post-Modernist Thought, edited by SallyEverett, 264-272. Jefferson: McFarland and Company Inc. Publishers.

Chloe Stead (2019). “Is AI Art Any Good?” Art Basel, 12 Dec. https://www.artbasel.com/news/artificial-intelligence-art-artist-boundary.

CLASS 12

Artist Talk: Caroline Sinders

Caroline Sinders (website) is a critical designer and artist. For the past few years, she has been examining the intersections of artificial intelligence, intersectional justice, systems design, harm, and politics in digital conversational spaces and technology platforms. She has worked with the United Nations, Amnesty International, IBM Watson, the Wikimedia Foundation, and others. Sinders has held fellowships with the Harvard Kennedy School, Google’s PAIR (People and Artificial Intelligence Research group), Ars Electronica’s AI Lab, the Weizenbaum Institute, the Mozilla Foundation, Pioneer Works, Eyebeam, Ars Electronica, the Yerba Buena Center for the Arts, the Sci Art Resonances program with the European Commission, and the International Center of Photography. Her work has been featured in the Tate Exchange in Tate Modern, Victoria and Albert Museum, MoMA PS1, LABoral, Wired, Slate, Quartz, the Channels Festival, and others. Sinders holds a Masters from New York University’s Interactive Telecommunications Program.

Pre-Readings

Read: Caroline Sinders (2020) In Defense of Useful Art. Pioneer Works.
Read: Abigail Echo-Hawk, Interview on "The Art and Science of Decolonizing Data." 

CLASS 13

Dataset Dissections

 

What’s in the training data of contemporary tools like Stable Diffusion and DALL-E 2? It’s not always easy to find out. In this class we look at image datasets similar to those that have been integrated into GAN and Diffusion models. We’ll learn how to look inside them, uncover where came from, and what the content means about the images these tools make.

This class introduces the Dataset Dissection assignment, reviewing a set of images from Kaggle image datasets and completing a “Datasheet for Datasets” evaluation.

  • Read: Kate Crawford & Trevor Paglen, Excavating AI. (Online)

  • Read: Gebru, Timnit, et al. “Datasheets for Datasets.” ArXiv [Cs.DB], 2018, doi:10.48550/ARXIV.1803.09010.

Works Referenced:

  • Abeba Birhane, et al. (2021). “Multimodal Datasets: Misogyny, Pornography, and Malignant Stereotypes.” ArXiv [Cs.CY], doi:10.48550/ARXIV.2110.01963.

  • Christoph Schuhmann, et al. (2022) “LAION-5B: An Open Large-Scale Dataset for Training next Generation Image-Text Models.” ArXiv [Cs.CV], doi:10.48550/ARXIV.2210.08402.

Dataset Dissections Working Day

CLASS X

Dataset Dissections Presentations

CLASS 14

CLASS 15

Seeing Like a Dataset

 

Now that we understand how machine learning tools work, how can we use them to make stuff? 

This class starts the AI Art Project Proposal — a way of thinking about AI as a “technology of practice.” It asks how we might "see like a dataset" in ways that help us scope out a reasonable idea for creating our own training material. Later in the course we will collect those images in ways that help the model make sense of them and produce new images. At each step, we will ask critical questions about this relationship between our work as artists and the tools we use.

Training a GAN may not be successful. Thinking carefully about the scope of your dataset and how you'll capture it matter a great deal to the end result. The first step is thinking about what works for training an AI art project, and what might pose a challenge. 

We’ll also think about AI as a medium with specific connotations and affordances — the stories it tells - and what kinds of stories AI is best suited to tell.

Read: Ursula Franklin (1990) The Real World of Technology, Chapter 1. (PDF)
Read: Jenny Hall (2014). The Spirit in the Machine: Mutual Affinities between Humans and Machines in Japanese Textiles. Thresholds, vol. 42, no. 42, 2014, pp. 170–181, doi:10.1162/thld_a_00087.

CLASS 16

Your AI Is A Human

 

We discuss datasets and how they're assembled, and how they are "seen" (or not seen). Human labor is often behind even the most fundamental technologies we describe as "automated," including the datasets we're looking at. That includes the workers hidden away behind interfaces and content moderation systems (thanks to Sarah T. Roberts for the title and the reading assignment this week) -- to the role of automation and the labor it replaces, to the humans behind the art we treat as data, to the question of where the human fits into AI creativity at the copyright office.

Read: Sarah T. Roberts, Your AI is a Human, ch. 2 from Your Computer is on Fire.
Read: Lauren Lee McCarthy, Feeling at Home Between Human and AI.
Read: Adrienne Williams, Milagros Miceli and Timnit Gebru (2022). The Exploited Labor Behind Artificial Intelligence. Noema.

Works Referenced:

Tubaro, P., Casilli, A. A., & Coville, M. (2020). The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence. Big Data & Society, 7(1). https://doi.org/10.1177/2053951720919776

Ram A (2019) Europe’s AI start-ups often do not use AI, study finds. Financial Times. Available at: https://www.ft.com/content/21b19010-3e9f-11e9-b896-fe36ec32aece.

Newton, Casey. “Google and YouTube Moderators Speak out on the Work That’s Giving Them PTSD.” The Verge, 16 Dec. 2019, https://www.theverge.com/2019/12/16/21021005/google-youtube-moderators-ptsd-accenture-violent-disturbing-content-interviews-video 

Roberts, Sarah T. “Your AI Is a Human.” Your Computer Is on Fire, The MIT Press, 2021, pp. 51–70.

Winner, Langdon (1980) “Do Artifacts Have Politics?” Computer Ethics, Routledge, 2017, pp. 177–192.

Acemoglu, Daron, and Pascual Restrepo. “Automation and New Tasks: How Technology Displaces and Reinstates Labor.” The Journal of Economic Perspectives: A Journal of the American Economic Association, vol. 33, no. 2, 2019, pp. 3–30, doi:10.1257/jep.33.2.3.

Baio, Andy (2022) “Invasive Diffusion: How one unwilling illustrator found herself turned into an AI model,” WAXY.org. 

Shan, Shawn, et al. “GLAZE: Protecting Artists from Style Mimicry by Text-to-Image Models.” ArXiv [Cs.CR], 2023, http://arxiv.org/abs/2302.04222.

Copyright Office, Library of Congress. “Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence.” Federal Register, vol. 88, 16 Mar. 2023, pp. 16190–16194, https://www.federalregister.gov/d/2023-05321.


CLASS 17

Show Me How to GAN

UPDATE: RunwayML has removed all of the tools used in this walk-through as of November 2023. It’s left here mostly as a historical record of how this stuff worked.

An informal technical walk-through of RunwayML and how to train GANs once you have a dataset. No theory, just pure walk-through, showing where to find the right tools, how to prep your dataset, and what you get from it when you do.

Additional Resources:

CLASS 18

Artist Talk: Eleanor Dare

 

Dr Eleanor Dare is an academic and critical technologist who works with Game Engines and virtual spaces, who now works at Cambridge University, Faculty of Education, as well as UCL, institute of Education. Dr. Dare has a PhD and MSc in Arts and Computational Technologies from the department of Computing, Goldsmiths, and has exhibited work at galleries and festivals around the world. Dr. Dare was formerly Reader in Digital Media and Head of Programme for MA Digital Direction, at the Royal College of Art.

Working Day

This was an open discussion period to explore reflections on what’s different about working with AI tools. We asked: What questions do you have about your results? How do you make sense of the relationship between the images in your dataset and the images the model creates? What stories can you tell with your dataset? What stories would you not tell with your dataset?

  • Read: Vilem Flusser (1983, English Translation 2000) Towards a Philosophy of Photography. Ch. 7, “The Reception of Photographs” & Ch. 9, “Why a Philosophy of Photography is Necessary.” (PDF)

CLASS X

CLASS 19

 

Artist Talk: Merzmensch

Vladimir Alexeev (Merzmensch), born in 1979 in Moscow, living in Germany, speaking Japanese is an international writer, artist, and researcher, who explores areas of tension between Historical Avant-Garde (especially Dadaism) and AI art. In his art and essays, he investigates the horizons and possibilities of human-machine creative collaborations. Harper's Magazine describes him as Data Journalist. In his artworks, he uses mixed media, his own poetry, and photography, but also advanced AI-driven models and approaches.

The pseudonym Merzmensch is coming from MERZ-Art by Kurt Schwitters, who is the big inspiration and spiritual mentor for Vladimir Alexeev. In his works, Merzmensch is trying to follow Schwitters' path to create new realms and realities from mixed media, where every element has its significance and is equal to others.

Cinema Without Cameras

CLASS 20

 

At their heart, movies are images — in sequence, fast enough to trick our eye into seeing motion. What does generative AI make possible for this form of storytelling? We look at the history of cinema without cameras, touching on the writers Vilem Flusser and Gene Youngblood. We look to how artists create “motion pictures” beyond the limits of frames on film, using digital tools to shape stories — and suggest ways of seeing the world — that traditional cinema can’t. This is only a brief introduction to a genre with a rich, vibrant history and contemporary practice.

Artists whose works are shown or referenced include Lee Harrison, Nikolai Konstantinov, Deniz Kurt, Refik Anadol, Sofia Crespo, Steina & Woody Vasulka, Ed Emshwiller, Memo Akten, Ed Catmill & Fred Park, and Nam June Paik.

Works Referenced:

Youngblood, Gene. “Cinema and the Code.” Leonardo. Supplemental Issue, vol. 2, 1989, pp. 27–30. JSTOR, https://doi.org/10.2307/1557940. Accessed 26 Mar. 2023.

Esser, Patrick, et al. “Structure and Content-Guided Video Synthesis with Diffusion Models.” ArXiv [Cs.CV], 2023, http://arxiv.org/abs/2302.03011.

Davis, Jenny L. How Artifacts Afford: The Power and Politics of Everyday Things. MIT Press, 2020.

Flusser, Vilem. Towards a Philosophy of Photography. Reaktion Books, 2013.

_blank. “Zen for Film.” _blank [at] Null66913, 1 Apr. 2023, https://null66913.substack.com/p/zen-for-film.

CLASS 21

Artist Talk: Moises Sanabria

 

Born in Caracas, Venezuela, Moises Sanabria is an artist interested in technology, internet culture and contemporary branding. He is one of the co-founders of the new media collective Art404 (Art Not Found), whose works often deal with legitimacy, value, and perception. Art404 often uses the online world as a medium for creating works, including projects involving Photoshop, the hacker group Anonymous, and the Sims.

Art404 has shown at Transmediale 2k+12 in Berlin, Gucci Vuitton in Miami, Ars Electronica Festival in Austria & Conflux Festival in NYC. Moises is currently attending the Cooper Union for the Advancement of Science and Art, where he is focusing on Interactive 3D graphics and physical computing. He lives and works in New York City.

CLASS 22

Have A Coke and a Smile

 

Coca-Cola was one of the first corporations to make a foray into the world of generative art for a marketing campaign. We’ll look at the history of media, marketing and advertisements to understand how and why they work, and how we can make informed decisions as consumers about the messages that pay to be there.

How does biased generated content normalize certain expectations in their content - and how does that connect to the dreams sold to you by ads? Are our imaginations the latest landscape for product placement? Or is that where they have been all along?

Assignment: Studio Reflections 2

Using an AI tool of your choice - including analog interpretations of “AI” - read and respond creatively to one of the following texts. The response should be visual, with a text reflecting your thinking, your use of the tool, and how you interpreted the text creatively.

  • Read: Aarathi Krishnan, Angie Abdilla, A Jung Moon, Carlos Affonso Souza, Chelle Adamson, Eileen M. Lach, Farah Ghazal, Jessica Fjeld, Jennyfer Taylor, John C. Havens, Malavika Jayaram, Monique Morrow, Nagla Rizk, Paola Ricaurte Quijano, R. Buse Çetin, Raja Chatila, Ravit Dotan, Sabelo Mhlambi, Sara Jordan & Sarita Rosenstock (2022) AI Decolonial Manyfesto. (Link)

  • Read: Anja Kaspersen & Wendell Wallach (2023): Now is the Moment for a Systemic Reset of AI and Technology Governance. Carnegie Council. (Link)

  • Read: Dan McQuillan (2023) We Come to Bury ChatGPT, Not to Praise It. (Website).

  • Watch: Ali Alkhatib, To Live in Their Utopia: Why Algorithmic Systems Create Absurd Outcomes. (5 minutes)

Works Referenced

“Coca-Cola Invites Digital Artists to ‘Create Real Magic’ Using New AI Platform – News & Articles.” The Coca-Cola Company, The Coca-Cola Organization, 20 Mar. 2023, https://www.coca-colacompany.com/news/coca-cola-invites-digital-artists-to-create-real-magic-using-new-ai-platform.

Shaw, Adrienne. “Encoding and Decoding Affordances: Stuart Hall and Interactive Media Technologies.” Media, Culture, and Society, vol. 39, no. 4, 2017, pp. 592–602, doi:10.1177/0163443717692741.

Gurfinkle, Jenka. “AI and the American Smile.” Medium, 26 Mar. 2023, https://medium.com/@socialcreature/ai-and-the-american-smile-76d23a0fbfaf.

Baker, Camille. “What a Russian Smile Means.” Nautilus, June 2018, https://nautil.us/what-a-russian-smile-means-237120/.

Barrett, Lisa Feldman. How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin, 2017.

Charlotte Kent:  “Art’s Intelligence: AI and Human Systems.” The Brooklyn Rail, 1 Apr. 2023, https://brooklynrail.org/2023/04/art-technology/Arts-Intelligence-AI-and-Human-Systems.

Tucker, Emily. “Artifice and Intelligence.” Tech Policy Press, 17 Mar. 2022, https://techpolicy.press/artifice-and-intelligence/.

Williamson, Judith. Decoding Advertisements. Marion Boyars, 2014.

Artist Talk: Derrick Schultz

CLASS 23

 

Utilizing cutting edge machine learning technology, Derrick Schultz's work explores multisensory perception, generative abstraction, and the future of ecology. In addition to creating his own work, Derrick also teaches machine learning to artists, designers, and image makers.

In-Class Presentations

CLASS 24 & 25

Is the AI An Artist? Is the Human a Machine?

CLASS 26

 

This course has pulled apart the threads of AI image synthesis. In this final review session, we reconnect them, systematically, through four lenses: 1) Data, 2) Interface, 3) Image, 4) Artistry. For each layer of this system, we rethink the ethical complications posed by these systems. Is AI art theft? Can images be representative? At which layer of these systems are the questions most, and least, compelling or plausible?

We conclude by revisiting the idea that machines are inspired, or see the world, or make art, in the same way people do. By now we might suspect that this isn’t true - but if so, what can we make of the role of AI during this fast-paced period of change? Spoiler Alert: I don’t have answers. But we’ll try to find some footing for how to ask good questions.

Assigned Readings:

Recommended Readings: