Tutorial: Interfaces

This week, we’ll be thinking about why ChatGPT represents a “moment” due to its interface - even though generative text has been around for a much longer time. Start by exploring the histories of generation, discussed in this week’s lecture and described in more detail below. Then, using William Chamberlain’s examples as a guide and following the steps detailed below, try to generate a compelling work of poetry using the ChatBot interface and document your process in this week’s discussion.

Histories of Generation

The level of attention that works authored by ChatGPT are receiving right now might suggest that generative text is new. But in my fields of electronic literature and digital humanities, what we typically refer to as “natural language generation,” or the use of software tools to produce written or spoken language, is not at all new, as text generation has a long history. We can even see precursors to text generation that don’t require the computer: Raymond Queneau’s 1961 book Hundred Thousand Billion Poems uses sliced pages to allow the reader to “generate” a seemingly endless number of possible sonnets, line by line. This type of work has inspired poets and authors to build generative text machines and bots with specific purposes: for instance, Nick Montfort’s 2009 Taroko Gorge is an endless poem that generates limitless text inspired by nature. These are just a few small examples from broad histories of text generation that speak to how this can be both a creative and intentional practice.

One example that frequently gets positioned as the first published “computer-authored” book is William Chamberlain's 1984 The Policeman's Beard is Half Constructed. A page demonstrating the range of poetic output curated by Chamberlain is screenshotted above. It’s not actually the first (that’s a subject for much longer debate than we’ll get into right now), but it was one of the first to get wide attention. That book is credited to a software program, Racter, but it's really an example of just how much human intervention, editing, and guidance is part of shaping compelling computer-generated text: the book’s text was generated by a program designed as an "artificial writer" by its programmer, who described the project in the introduction as an effort "to teach his computer to write prose." As one reviewer, Leah Henrickson, critiqued the software as a writer, “Racter is rigid, using fixed functions to complete a particular task. The program cannot interpret that which it produces and, indeed, not until a human interprets Racter’s output can it be assigned any cultural value.” This same critique can be brought to the output of current generative AI, which can produce a wide range of cultural objects (text, images, code, video, sound, etc) but should not be mistaken for an expert in that form. However, that doesn’t mean that the results aren’t compelling as poetry - decide for yourself how you feel about the example

For a broader sense of this history, both in the humanities and more broadly, I recommend Lillian-Yvotte Bertam and Nick Montfort’s 2024 edited collection Output: An Anthology of Computer Generated Text, 1953-2023. These two incredible poet-author-coders have themselves experimented continually with different types of algorithmic and natural language generation, and the work is helpful for positioning generative AI as just another step in a long history of using systems to generate a wide range of text: creative writing, research systems, reports, poetry, humor, conversations, and so forth.

These histories are filled with play and creativity: for over a decade, an event called National Novel Generation Month has brought author-coders together to generate novels. A lot of their projects are helpful in demonstrating the ways in which expertise and intention are important to meaningful text generation. That is to say, you don't get interesting output just by prompting a text bot. Knowing about and acknowledging that history can be really important for helping both your colleagues and your students stop thinking about things like ChatGPT as something that sprung up yesterday--and to provide models for how we can critically and creatively engage with text generation output without expecting it to substitute for expertise.

Discussion

After completing our readings and exploring William Chamberlain’s work, iterate towards generating a poem you find compelling using a chatbot AI interface. As with last week, feel free to use ChatGPT, Copilot, Gemini, Claude, or another similar tool.

Refine the poem through at least ten iterations and save the record of that conversation as a PDF. Document any frustrations and challenges as well as surprises along the way. How does the text prompt interface aide or hinder your process? Compare this to how William Chamberlain described his book and the discussions of computer-assisted writing processes from Matthew Kirshenbaum’s book this week. Keep in mind Lori Emerson’s discussion of the interface and think about how much control you have.

You might find it takes more than ten iterations for you to be satisfied with the output. Share the final results of your process in the discussion post, as well as the conversation as an attachment with your reflection. Remember to draw specific connections to this week’s readings. Consider how this act of interacting with the chatbot for a creative purpose differs from the conversational chat you had last week.