Tag: AI

AIlingualism

/ dmueller / Leave a comment

By omitting a space and setting it in a san serif font, AIlingualism piles on ambiguities. On page or screen, it might tempt you to see all lingualism, the heteroglossiac babelsong, much like Adriano Celentano’s Prisencolinensinainciusol might tempt you to hear Anglophone snippets in what is stylized nonsense. “AIlingualism” sounds like eye-lingualism, I suppose, or the act of entongued seeing, which without going into the subtleties of synesthesia might be as simple as tracing tooth-shape, fishing for an offshed hair from a bite of egg salad, or checking the odontal in-betweens for temporarily trapped foodstuff. Hull from a popcorn kernel? When did I have popcorn? A similar phenomenon would be something like “retronasal olfaction,” which Michael Pollan describes in Cooked, as the crossover between senses, the role of olfactory processing within experiences of taste, or where smell and taste commingle and coinform.

Yet I mean something altogether different with AIlingualism. Used to be the over-assisted writing revealed itself owing to too many thesaurus look-ups. You’ve betrayed a faithful expressive act because we could almost hear Peter Roget himself whispering through your words. But thesaurus overuse is a lesser crime than the wholesale substitutive “assists” that walk us nearer and nearer to overt plagiarism: patchwriting, ghost writing, essay milling, unattributed quotation, and so on. An assist from a thesaurus was usually keyed to a smaller unit of discourse, which in turn amounted to petty ventriloquism. But as the discursive magnitude increases, so too does the feeling that the utterance betrays the spirit of humanistic communication, that fleshly-terrestrial milieu where language seats, swirls, and percolates, elemental and embodied. I think this is close to what Roland Barthes characterizes as the “pact of speech” (20) in “To Write: An Intransitive Verb” (1970) from The Rustle of Language (1989).

AIlingualism creates phrasal strings from a vast reservoir of language, not the ‘Grand Vat’ but in the vaguest of terms, a large language model, or LLM, whose largesse blooms on the shoulders of other people’s language–papers, books, discussion boards, social media chatter, and utterances in whatever additional ways collected and compiled. Not that utterances have shoulders. But they do, at their genesis, stem from beings in contexts, and although the writing itself is a technology that rebodies utterances, LLMs as an extractable reserve and pseudo-sense-making melange yet further extend that rebodiment. To invent with the assistance of artificial intelligence is to compose in a way uniquely hybridized and synthetic. Language games, in this case, work by different but non-obvious rules. AIlinguals, or users of LLMs to write, suspend the pact and engage in pactless speech.

It isn’t so much the case that pactless speech of this machine-assisted sort is destined to be disappointing, underwhelming, detached from terrestrial contexts, or otherwise experientially vapid. I can’t say I am in a hurry to devote any time to reading AI writing, other than comes with the shallowest of headlines glancing. And now that we’re solidly a year and a half into this “summer” (or buzzy hot streak) of AI, it continues to hold true that most everyday people are still puzzling over what, exactly, is assisting when a writer enlists the assistance of AI. AI is as often as not fumbling along with poor customer service chat help, with returning Amazon orders, and with perfunctory Web MD advice (“Have you tried sipping chamomile tea for your sore throat, Derek?”). It is helping to offer safe-playing might-rain-but-might-not weather forecasts. Looks up; no rain. And in this sense, it still functions, albeit within my admittedly small and mostly rural lifeworld, innocuously.

In a section called “5. Creatures as Machines,” Wendell Berry puzzles out a series of questions that, though they appeared in Life Is A Miracle, which was published in 2000, might just as well have been about ChatGPT:

Is there such a thing as a mind which is merely a brain which is a machine? Would one have a mind if one had no body, or no body except for a brain (whether or not it is a machine)–if one had no sense organs, no hands, no ability to move or speak, no sensory pains or pleasures, no appetites, no bodily needs? If we grant (for the sake of argument) that such may be theoretically possible, we must concede at the same time it is not imaginable, and for the most literal of reasons: Such a mind could contain no image. (47)

Such a mind could contain no image. AIlingualism propagates pactless speech; its intelligence can generate but not contain an image. Its memory is contrived (or dependent upon contrivance), not organic, fleshly, or pulsed neurologically. This is the greatest and gravest indicator of all: still, it better than holds on. AI is ascendant, picking up steam. What can this mirror about the world we’ve built, grinding along with its paradoxically gainful backsliding, AIlingual utterances–today–amounting to no more and no less than the throat clearings, ahem ahem, of commercial science and militarism. Of all the possible energias to put to language, to sacrifice our tongues to, these? Ahem ahem ahem.

Are the Artificials Expressive? 🐂

/ dmueller

Stepping into AI discussions since November 2022 has felt to me like stepping into a mixed gravity bounce house, enthusiasts bounding miles-high right next to cautionaries clinging clutch-knuckled to whatever handles avail themselves of the seeming-eternal humanistic basics.

Me, I’m just doing what I can to check the conversations, keep walk-jog sideline pace, or possibly bounce high enough for an occasional dunk-thought, sort of like those tenth grade lunch breaks when the gymnastics spring boards were theatrically repurposed so that everyone who wanted one could have an attempt at reaching the rim. Just a touch! I hope that’s not too much mixing, from bounce house to springboard-boosted basketball, considering I am over here trying to make a point about artificial intelligence, large language model “writing,” and the scoops of words masquerading as discourse from ChatGPT.

I was listening to a podcast—Ezra Klein, I think—while driving to Virginia from Michigan on August 2, and although the podcast wasn’t about AI, per se, the discussion of First Amendment law and free speech got me puzzling through a question about whether AI-generated prose is legally expressive. I am not the first; I am also not a lawyer. But. To illustrate, consider this: a local politician is running for a seat on the Board of Supervisors. Not being much of a speech writer, they tap GPT4 on its non-shoulder, prompting it to return for them an applause raising statement about democratic values. The AI returns a lukewarm soup of a statement, and it just so happens to include in it a damaging and slanderous falsehood about another local official. Litigious gloves are off. Legal teams are enlisted. And the candidate mea culpas with the grandest of agentic shifts: “GPT4 made me say it!”

It reads to me as one of the most ground floor conditions, a lower order stases: Is AI expressive? Is ChatGPT responsible, legally or otherwise, for its so-called writing?

If no, then follows a corresponding set of questions about what writing qua “content generation” actually boils down to. Humans are, arguably and correspondingly, small(er) language models (SLMs). Certainly this doesn’t mean that an SLM can’t every so often augment their repertoire of inventional, compositional, and interpretive range with a sidekick LLM, a backdrop behemoth spitting possibly everything ever. But my hunch is that the SLM should be cautious about surrendering its language to this other phenomenon overmuch, or all-out ventriloquizing the LLM as though its expressions will be satisfactory, sufficient, or both, just because it is big.

Writing, as a verb, doesn’t shield itself especially well from contending, sometimes mismatched, activities. In fact, three decades of writing studies scholarly activity has worked mightily to expand writing, sparing writing its alphabetic-linear reduction, and pluralizing it loftily with overtures of multimodality. Much of this has been good and necessary and warranted, but there has been a trade-off. The trade-off is the you can fit a whole lot of yes-that-too under the baggiest of umbrellas, and then along came the LLMs. I wouldn’t argue that anyone should revert to exclusive or narrow-banded definitions of writing, tempting as it might be (e.g., only a pencil-holding activity, or a thing that happens when a human hand makes a keystroke). But I would say that the lines have blurred between “content generation” and “writing” in ways that are not always helpful for demarcating reasonably distinctive activities and in ways that risk promoting shortcut mindsets when writing is presumed to be ready-made, extractive, and infinitely/generically scoopable from an allegedly ever-improving LLM.

Collin recently referred me to Alan Jacobs’ recent entry, “on technologies and trust,” which aptly sketches the position that we wouldn’t ever think of enticing prospective students to cooking school only to tell them that everything they learn will be derived from HelloFresh boxes. A similar logic extends to graphic designers from templated fallbacks. While the masticated options might be appealing to the uninitiated, they are not quite the same as learning by practicing when that practice entails selection, decision, trial and error, and so on.

I am not convinced that LLMs are expressive, and I want to work on making evaluative sense of AI more forwardly in these terms.

A final illustration: In April an HVAC technician visited the house for routine maintenance on the heat pump leading into the air conditioning season. Before leaving, he started to tell me about how he used to manage a big game preserve in Tennessee, though it closed, and so he changed careers. He then went on to tell me about his daughter who was taking an interest in cattle AI because she had a friend who was working with ranchers in Texas; the friend was finding cattle AI quite lucrative, he explained.

It took me a while to figure out that large-scale livestock procreation, too, has an artificial alternative; that’s “cattle AI,” for us non-ranchers. I think about this often as a checkpoint in conversations about AI and content generation. Might be, cattle AI is for cows what ChatGPT is for writing–artificial, expedient, not to be mistaken for the other embodied, developmentally-dependent, organic-contextual (more than mechanistic) act.