In The Gutenberg Parenthesis (my upcoming book), I ask whether, “in bringing his inner debates to print, Montaigne raised the stakes for joining the public conversation, requiring that one be a writer to be heard. That is, to share one’s thoughts, even about oneself, necessitated the talent of writing as qualification. How many people today say they are intimidated setting fingers to keys for any written form — letter, email, memo, blog, social-media post, school assignment, story, book, anything — because they claim not to be writers, while all the internet asks them to be is a speaker? What voices were left out of the conversation because they did not believe they were qualified to write? … The greatest means of control of speech might not have been censorship or copyright or publishing but instead the intimidation of writing.”
Thus I am struck by the opportunity presented by generative AI — lately and specifically ChatGPT— to provide people with an opportunity to better express themselves, to help them write, to act as Cyrano at their ear. Fellow educators everywhere are freaking out, wondering how they can ever teach writing and assign essays without wondering whether they are grading student or machine. I, on the other hand, look for opportunity — to open up the public conversation to more people in more ways, which I will explore here.
Let me first be clear that I do not advocate an end to writing or teaching it — especially as I work in a journalism school. It is said by some that a journalism degree is the new English degree, for we teach the value of research and the skill of clear expression. In our Engagement Journalism program, we teach that rather than always extracting and exploiting others’ stories, we should help people tell their own. Perhaps now we have more tools to aid in the effort.
I have for some time argued that we must expand the boundaries of literacy to include more people and to value more means of expression. Audio in the form of podcasts, video on YouTube or TikTok, visual expression in photography and memes, and the new alphabets of emoji enable people to speak and be understood as they wish, without writing. I have contended to faculty in communications schools (besides just my own) that we must value the languages (by that I mean especially dialects) and skills (including in social media) that our students bring.
Having said all that, let us examine the opportunities presented by generative AI. When some professors were freaking out on Mastodon about ChatGPT, one prof — sorry I can’t recall who — suggested creating different assignments with it: Provide students with the product of AI and ask them to critique it for accuracy, logic, expression — that is, make the students teachers of the machines.
This is also an opportunity to teach students the limitations and biases of AI and large language models, as laid out by Timnit Gebru, Emily Bender, Margaret Mitchell, and Angelina McMillan-Major in their Stochastic Parrots paper. Users must understand when they are listening to a machine that is trained merely to predict the next most sensible word, not to deliver and verify facts; the machine does not understand meaning. They also must realize when the data used to train a language model reflects the biases and exclusions of the web as source — when it reflects society’s existing inequities — or when it has been trained with curated content and rules to present a different worldview. The creators of these models need to be transparent about their makings and users must be made aware of their limitations.
It occurs to me that we will probably soon be teaching the skill of prompt writing: how to get what you want out of a machine. We started exercising this new muscle with DALL-E and other generative image AI — and we learned it’s not easy to guide the machine to draw exactly what we have in mind. At the same time, lots of folks are already using ChatGPT to write code. That is profound, for it means that we can tell the machine how to tell itself how to do what we want it to do. Coders should be more immediately worried about their career prospects than writers. Illustrators should also sweat more than scribblers.
In the end, writing a prompt for the machine — being able to exactly and clearly communicate one’s desires for the text, image, or code to be produced — is itself a new way to teach self-expression.
Generative AI also brings the reverse potential: helping to prompt the writer. This morning on Mastodon, I empathized with a writer who lamented that he was in the “I’m at the ‘(BETTER WORDS TK)’ stage” and I suggested that he try ChatGPT just to inspire a break in the logjam. It could act like a super-powered thesaurus. Even now, of course, Google often anticipates where I’m headed with a sentence and offers a suggested next word. That still feels like cheating — I usually try to prove Google wrong by avoiding what I now sense as a cliché — but is it so bad to have a friend who can finish your sentences for you?
For years, AI has been able to take simple, structured data — sports scores, financial results — and turn that into stories for wire services and news organizations. Text, after all, is just another form of data visualization. Long ago, I sat in a small newsroom for an advisory board meeting and when the topic of using such AI came up, I asked the eavesdropping, young sports writer a few desks over whether this worried him. Not at all, he said: He would have the machine write all the damned high-school game stories the paper wanted so he could concentrate on more interesting tales. ChatGPT is also proving to be good at churning out dull but necessary manuals and documentation. One might argue, then, that if the machine takes over the most drudgerous forms of writing, we humans would be left with brainpower to write more creative, thoughtful, interesting work. Maybe the machine could help improve writing overall.
A decade ago, I met a professor from INSEAD, Philip Parker, who insisted that contrary to popular belief, there is not too much content in the world; there is too little. After our conversation, I blogged: “Parker’s system has written tens of thousands of books and is even creating fully automated radio shows in many languages…. He used his software to create a directory of tropical plants that didn’t exist. And he has radio beaming out to farmers in poor third-world nations.”
By turning text into radio, Parker’s project, too, redefines literacy, making listening, rather than reading or writing, the necessary skill to become informed. As it happens, in that post from 2011, I starting musing about the theory Tom Pettitt had brought to the U.S. from the University of Southern Denmark: the Gutenberg Parenthesis. In my book, which that theory inspired, I explore the idea that we might be returning to an age of orality — and aurality — past the age of text. Could we be leaving the era of the writer?
And that is perhaps the real challenge presented by ChatGPT: Writers are no longer so special. Writing is no longer a privilege. Content is a commodity. Everyone will have more means to express themselves, bringing more voices to public discourse — further threatening those who once held a monopoly on it. What “content creators” — as erstwhile writers and illustrators are now known — must come to realize is that value will reside not only in creation but also in conversation, in the experiences people bring and the conversations they join.
Montaigne’s time, too, was marked by a new abundance of speech, of writing, of content. “Montaigne was acutely aware that printing, far from simplifying knowledge, had multiplied it, creating a flood of increasingly specialized information without furnishing uniform procedures for organizing it,” wrote Barry Lydgate. “Montaigne laments the chaotic proliferation of books in his time and singles out in his jeremiad a new race of ‘escrivains ineptes et inutiles’ ‘inept and useless writers’ on whose indiscriminate scribbling he diagnoses a society in decay…. ‘Scribbling seems to be a sort of symptom of an unruly age.’”
Today, the machine, too, scribbles.