DLCL 103 / Italian 103, Autumn 2023, Stanford University
- Laura Wittman, Associate Professor of French and Italian, DLCL
- Quinn Dombrowski, Academic Technology Specialist, DLCL
- Eric Kim, PhD candidate, Slavic Languages and Literatures, DLCL
- Andrew Nepomuceno, PhD candidate, Epidemiology and Population Health
This course will engage students in reading and analysis of the current generation of text- and image-generation tools and related technologies that fall under the broad umbrella of “artificial intelligence.” Students will gain historical, legal and philosophical context for these tools, develop an understanding of how the technology actually works, and cultivate their own critical perspective on how the tools interact with human creativity. For their final project, students can choose between a model-building track where they work with technical mentors from Research Computing or in the Library's Research Data Services group to develop or fine-tune a large language model to address their own needs or interests, a critique track where they write a well-researched position piece on AI in their unique voice as a writer, or a creative track where they can incorporate AI "creativity" into a piece of creative writing or art, or use creative media to visualize, critique or interrogate AI. The creative and technical tracks will be accompanied by a creator's statement tying the work to the course themes and reflecting on the student's process and the challenges they encountered. No prior technical experience is required for the course.
- To learn not just the basics of how AI works, but its broader social, cultural, artistic, and philosophical implications.
- To learn how to research and discuss issues related to AI - as they unfold in real time, since the situation is changing fast.
- To practice discussing, presenting, and debating these issues with a group, in a collegial, civic setting.
- To formulate a project - be it creative, computational, or analytical - that has a clear goal; then to take that project to completion and produce a poster presentation about it.
The first week of class will include a quick recap of the last year in AI research, and an overview of the current state-of-the-art in large language models and image generation. We'll then take a step back and talk about the history of machine-generated text, dating back over a century. Students will use a method called Markov chains to generate new text from text they already are familiar with. We'll then talk concretely about large language models like ChatGPT, how they generate text, and how that text is shaped both by the algorithm's user-configurable settings and by the training data. We'll also cover the labor dynamics at play in training data, and how they intersect with power, gender, and race.
Introductions & logistics.
Some hands-on play with AI + what is AI and how does it work?
Small Group Activity: talk to each other about what you've used AI for, what has worked and what hasn't (or what you have heard about it/read about it/want to know more about it). Choose someone to report out.
Whole class activity: Make sure you can log into ChatGPT.
Reading:
- Karawynn Long, "Language is a Poor Heuristic for Intelligence". Nine Lives blog, June 26, 2023.
Readings:
- Stephen Wolfram, "What is ChatGPT Doing... And Why Does It Work?". Writings blog, February 14, 2023.
- Emily Bender, "Thought experiment in the National Library of Thailand. Medium, May 24, 2023.
- Small Group Activity: Nicholas Cardini's AI Forecasting Challenge
- Small Group Activity: Markov chain poetry.
We'll expand on the conversation about training data to consider the Anglophone-centricity of the field of natural language processing (NLP), as well as easily-available corpora for model training. What are the impacts on people whose language is "under-resourced" with NLP tools? We'll make a visit to the Encode/Include exhibit at Green Library as well as to the Textile Makerspace.
Digital and digitized language and the mystery of corpora.
Readings:
- Erin Kissane, "Meta in Myanmar, Part I: The Setup". September 28, 2023.
10/9 no class for Indigenous People's Day
Visit Green Library + Textile Makerspace (2 groups will swap locations)
- Rebecca L Johnson et al, "The Ghost in the Machine has an American accent: value conflict in GPT-3", read sections #1.2, #2, #3, and #4.
- Amy E. Elkins, "Introduction: The Weaver's Handshake" (through "Modernity Gets Crafty").
Assignment 1: Write a short reflection on something surprising that you discovered during one of your two visits this week (3 pages, double spaced).
How does text printed on paper become a digital text? Why do we digitize printed text -- and whose printed texts are prioritized for digitization? Who owns the digitized text and to what end? What becomes possible with digitized text that we can't do with printed text? What do we lose when we digitize a book? We will also discuss "born digital" materials, and the advantages and disadvantages of working with those texts compared to their digitized counterparts.
Copyright law is often invoked in objections to AI, but the landscape of copyright law relevant to AI is extremely complex. This week we'll cover issues including fair use, derivative works, transformative works, and consider how they apply both to what is being created through training a model, and to what is produced. We'll draw on case studies, and read write-ups of recent law suits. We'll consider the power dynamics behind them, and who would benefit from their success or failure.
A key question is how cultural capital is transformed when the ability to manipulate corpora is leveraged to appropriate creative material.
Digitization and copyright
Readings:
- Cade Metz, "Lawsuit Takes Aim the Way AI Is Built." The New York Times, Nov. 23, 2022.
- Kate Knibbs, "The Battle Over Books3". Wired. September 4, 2023.
- George Pike, "Copyright and AI," Information Today, Nov/Dec 2022.
Cultural capital in the age of AI
Readings:
- Pierre Bourdieu and Jean-Claude Passeron, "Cultural Reproduction and Social Reproduction" (1977).
- Karla Ortiz, statement before US senate on AI and copyright. July 7, 2023.
Assignment 2: Find an image style that an image-generation model consistently gets wrong. Write up what you tried, and include some images to illustrate (3 pages, double spaced).
This week we will push large language models to write "creative" texts using specific forms of genres, and ask it to explain its work. How (consistently) successful is it at reproducing the conventions of different genres? How reliably can it produce well-structured forms? Do we think about AI-generated versions of these forms differently if we know they're AI-generated vs. human-created? We will do the same activity with image-generation models. What are the genres / forms / styles that these models are "good at"? Where do they struggle? How does this relate to the prestige and/or ubiquity of those creative forms?
Prompts and platforms
Readings:
- Lila Shroff, "Datasets as Imagination"
- Chiara Coetzee, "Generating a full-length work of fiction with GPT-4," Medium, March 24, 2023.
- Nina Beguš, "Experimental Narratives: A Comparison of Human Crowdsourced Storytelling and AI Storytelling", sections 1, 2, 3.1, 3.4, 3.5. ArXiv, October 19, 2023.
AI and the generation of new art
Readings:
- Laurie Clarke, "When AI can make art: what does it mean for creativity?" The Guardian, November 12, 2022.
- Zachary Small, "An Art Professor says AI Is the Future. It's the Students Who Need Convincing." The New York Times, May 1, 2023.
Assignment 3: Write a brief (< 1 page) proposal for your final project, indicating which track (technical, critique, creative) you intend to follow and the general direction you're thinking of taking.
This week we will explore authorial/authoritative voice, examining what characterizes the emphatically "neutral" voicelessness of AI-generated text. Where does it come from in the training data, and why do we perceive this style as authoritative? What are the gatekeeping functions of that style of writing, and how does AI's proficiency impact those functions?
With AI models capable of producing the style of writing that students take years to comfortably produce - not to mention being used ubiquitously to write anything from office memos to blogs to works of fiction -- how should that impact training in writing for humans?
AI models are also increasingly able to converse, mimicking not just a human, but a human with specific characteristics or even an individual human. What does it mean to have a relationship with an AI? (How) does the AI revolution take the social media turn a step further? How will this change human psychology and potentially our understanding of what it means to be a person?
Voice, voicelessness, and authority
Readings:
- Joseph Weizenbaum, ELIZA - A Computer Program for the Study of Natural Language Between Man and Machine. Communications of the ACM, 1966.
- Michelle Huang, Training an AI Chatbot on my Childhood Journal Entries, December 14, 2022.
- Mark Marino, "Can ChatGPT Copy Your Writing Style?" Medium, January 24, 2023.
Personality, psychology, and personhood
Readings:
-
Abrams, “AI is changing every aspect of psychology.” An overview of the situation from the American Psychological Association.
-
Jensen, “Johannes Eichstaedt: Exploring the Intersection of AI and Psychology” This is a very short piece about work currently going on Stanford.
-
Clinical Psychologist Lisa Damour interviewed by Ezra Klein on “The teen mental health crisis.” You can hear it or read a transcript here.
Assignment 4: Write the least AI-like thing on a prompt of the class's choosing (1 page, double spaced). We'll have a Jupyter notebook or other tools available to test the "AI-ness" of your writing.
Machine learning models are being deployed in many different contexts, from therapy to grocery shopping to research to art creation. The contexts where machines are gaining traction are different than the imaginary futures of the past, where manual drudgery would be automated first. We'll talk about where "making" is still primarily or exclusively a human activity (sewing, food preparation, crochet but not knitting), and the surprising challenges in automating that work. We'll also look at where advances in automation have come with a cost, and explore longstanding forms of hybrid making, such as embroidery and knitting machines, which combine human creativity with machine implementation. We'll examine the benefits of challenges of AI-assisted research of different types and consider how AI might change research questions and possibilities. How do all these hybrid crafts shift the balance of skill and time required to make something?
Manual doing and making
Readings:
- Michael Polanyi, Personal Knowledge: Towards a Post-Critical Philosophy (U Chicago Press, 1974), selections.
- Lauren Panepinto, "The Envy of Non-Creatives". Muddy Colors, September 7, 2023.
- Joseph Weizenbaum, excerpt from "Computer Power and Human Reason".
Analytical doing and making
Reading:
- Lise Jaillant and Arran Rees, "Applying AI to digital archives: trust, collaboration, and shared professional ethics." Digital Scholarship in the Humanities. November 17, 2022.
Assignment 5: Project check-in: write a brief (< 1 page) assessment of where you are in your final project and what still needs to be done.
Fearmongering about AI unleashing a sci-fi style apocalypse and annihilation of the human race is already in full swing, as are claims of AI sentience. We'll discuss these images, where they come from, and the predictions and the actual research they generate. How can knowing the history of how we have imagined AI before it existed help us understand what we now have created and what we can make of it? Does AI help us reconsider the difficult problem of what things like sentience, life, or thinking are?
The uncanny presence of AI
Readings:
- Philip K Dick, "Do Androids Dream of Electric Sheep?" 1968.
- Erik Davis, "AI EEEEEEE!!!" Burning Shore, April 11, 2023.
Interpersonal AI
Reading:
- Qntm, "Lena", 2021.
Assignment 6: Draw on the novelist or philosopher of your choice (we can make suggestions) to reflect on any aspect of AI development, "sentience," or identity. Write a 3-page (double spaced) mini short story.
Alternative: Try a fully-manual and a hybrid (machine-assisted) craft (e.g. embroidery, knitting, papercraft). Write a short reflection on the process and the outcome (about 3 pages, double spaced). When and why might you choose each approach?
Since a 6-month moratorium on AI releases was proposed, a lively debate has emerged about whether we are capable of incorporating this technology in ways that benefit humans. The "alignment problem" takes on a new and urgent form here. What are the more-grounded concerns about the impact of AI on various aspects of society, education, and career paths? How do the potential threats to managerial and professional work impact the discourse around AI? Which nations, economic powerhouses, corporations, or individuals stand to benefit from specific AI uses? How can we regulate a technology that is changing so fast? What are different nations doing in terms of policy at the moment and why?
OpenAI drama
Readings:
- Jeff Jarvis, “Artificial General Bullshit”. BuzzMachine blog, November 19, 2023.
- Angie Wong, “Is My Toddler a Stochastic Parrot?” The New Yorker, November 15, 2023.
- Derek Thomson, “The OpenAI Mess is About One Big Thing”, The Atlantic, November 22, 2023.
Assignment 7: Project check-in (come prepared to explain your project to a group of peers and get feedback on what is missing).
Capitalism and alignment
Reading:
- Brian Christian, The Alignment Problem: Machine Learning and Human Values (Norton, 2020), selections.
For week 10, students will present their final projects as posters/presentations and get feedback from classmates and instructors. This will be intellectually stimulating but also a fun celebration of all we have learned throughout the quarter!
Final project posters/presentations
Assignment 8: Prepare a poster/presentations that explains your final project and its main takeaways; be ready to present it to the class and instructors. Write feedback for a subset of the week's presentations (1 page for each of 5 presentations).
Please note that assignments in italics are part of your final presentation preparation.
- Assignment 1: Write a short reflection on something surprising that you discovered during one of your two visits this week (3 pages, double spaced). Due 10/11.
- Assignment 2: Find an image style that an image-generation model consistently gets wrong. Write up what you tried, and include some images to illustrate (3 pages, double spaced). Due 10/18.
- Assignment 3: Write a brief (< 1 page) proposal for your final project, indicating which track (technical, critique, creative) you intend to follow and the general direction you're thinking of taking. Due 10/25.
- Assignment 4: Write the least AI-like thing on a prompt of the class's choosing (1 page, double spaced). We'll have a Jupyter notebook or other tools available to test the "AI-ness" of your writing. Due 11/1.
- Assignment 5: Project check-in: write a brief (< 1 page) assessment of where you are in your final project and what still needs to be done. Due 11/8.
- Assignment 6: Draw on the novelist or philosopher of your choice (we can make suggestions) to reflect on any aspect of AI development, "sentience," or identity. Write a 3-page (double spaced) mini short story. Alternative: Try a fully-manual and a hybrid (machine-assisted) craft (e.g. embroidery, knitting, papercraft). Write a short reflection on the process and the outcome (about 3 pages, double spaced). When and why might you choose each approach? Due 11/15.
- Assignment 7: Project check-in (come prepared to explain your project to a group of peers and get feedback on what is missing). Due 11/27.
- Assignment 8: Prepare a poster/presentations that explains your final project and its main takeaways; be ready to present it to the class and instructors. Due 12/4 or 12/6. Write feedback for a subset of the week's presentations (1 page for each of 5 presentations). Due 12/8 (Friday).
- Work with Research Computing and/or Library's Research Data Services group to develop or fine-tune a large language model to address their your own needs or interests (we can work with you to brainstorm something that would be manageable and interesting).
- Write 1-2 pages about the problem you were trying to solve, what data you used and why, how well it worked, along with documentation for the instructors to try out what you built.
- Prepare a poster/presentation explaining your work.
- Develop a creative project that you will accomplish with AI assistance, be it creative writing, visual or musical art, or some other creative endeavor.
- Examples:
- Write a short story on the theme of AI and culture
- Take AI-generated art (or AI-generated instructions) and recreate or reimagine it in a medium that depends on human labor
- Write a 1-2 page artist's statement that ties the work to the themes of the class.
- Prepare a poster/presentation explaining your work.
- Write a 5-page blog post in your own voice (no formulaic, neutral academic prose) that takes a position and makes an argument about some topic related to AI and culture. Include citations (to real sources that you've read) as appropriate, as well as your own experience using AI models.
- Prepare a poster/presentation explaining your work.
- 20% class participation, including in section
- 50% weekly assignments (1, 2, and 4 are 10% each; 6 is 20%)
- 50% final project (preparation assignments, 3, 5, and 7 are 5% each; the poster/presentation, actual implementation, and feedback to others, are 35%)