Poetry and the digital simulacrum of mind [poetry as digital touchstone]

I'm bumping this to the top of the queue on general principle.

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Carmine Starnino, Poetry & Digital personhood, The New Criterion, April 2022.

Starnino starts out by talking about Racter, a well-known computer generator of poem simulacra from the 1980s, and then moves on to GPT-3: “...GPT-3 isn’t a better Racter. It’s a godlike Racter.” Then mixes in a little of this and that and observes:

The Turing Test, after all, has shown that readers have a weakness for rhetoric, grand gestures, and feelingful murk—all of which algorithms easily mimic. If this is what we mean when we say AI will one day rival human poets, then it will surely win, and indeed may already have.

Yes! Our willingness to read meaning to any quasi-intelligible lump of language makes it easy for computers to crank out simulacra of poetic profundity. That’s a low bar to cross.

Starnino goes on:

But there’s another kind of poetry AI will have to beat—poetry as an art of brilliant accuracies, of reality re-described in ways that bind sound to perception. And here AI’s deficiencies are brutally exposed. Because to compete at this imitation game, a machine has to show that, by micro-adjustments of effect, it can draw our senses to the highest pitch of expression. It will need to be able to match Les Murray’s depiction of beans as “minute green dolphins at suck,” or Peter van Toorn’s realization that flying dragonflies have “a great rattle of rice in their wings,” or Elizabeth Bishop’s noting a fish’s “coarse white flesh/ packed in like feathers,” or how, for Seamus Heaney, love was “like a tinsmith’s scoop/ sunk past its gleam/ in the meal-bin.” To play at this level, a machine has to imbue words with the most intimate associations and, turning inward, confess hardships, regret irrevocable choices, ponder its ultimate demise. It has to hit the same mark Robert Frost does at the end of his sonnet “Design,” when, watching a spider readying itself to eat a moth, he asks what the grim scene reveals about nature—and if there is any moral code to such predation. “What but design of darkness to appall?—/ If design govern in a thing so small.” The word “appall” here logs the shock perfectly. In its French root, the word means to make white. It also contains “pall”: a sheet, laid over a coffin, usually of white linen. Thus Frost’s diction hones our cognition, schooling us to see the world in a fresh way.

Yes.

Starnino goes on to remind us of Eliza:

Released in 1966 by the MIT professor Joseph Weizenbaum, ELIZA was the world’s first chatbot. Designed to impersonate a therapist, it would reflect back a user’s statements with open-ended questions and prepared responses (“My mother never loved me” would trigger “please go on” or “tell me more.”) Weizenbaum’s goal was to explore a computer’s capacity for conversation. Instead, he was alarmed by how completely users were taken in by ELIZA’s shallow repartee; his own secretary once insisted he leave the room so she could talk to the program in private. Credulity even extended to graduate students who had watched him build ELIZA from scratch. Sherry Turkle, a social scientist and Weizenbaum’s colleague, called it “the Eliza effect,” which she defined as “human complicity in a digital fantasy.” We can see this effect in the love-struck language Racter’s programmers used to describe the moment their creation came to life.

A bit later he notes:

It’s no coincidence that each time a new threshold is smashed, poetry is soon offered up as evidence of the breakthrough. The most profound exercise of full human consciousness, poetry has long been coveted as a benchmark for silicon-based minds, the ultimate proof of concept. Its principles were not only present at the founding of artificial intelligence as a field—the 1956 conference that set out to design machines able to “use language, form abstractions and concepts”—but every step in eroding the line between robots and people has been marked by a poetry generator. When the famed futurist Ray Kurzweil wanted to sell the public on the idea of a thinking machine in the late 1980s, he began by inventing a “Cybernetic Poet.” In fact, you can even argue that the pursuit of machine poetry has driven entire sectors of AI, helping push the limits of what language models can now do.

Hmmmm. I can’t help but think that, to the extent that that is true, my 1970s work using computational semantics to analyze a Shakespeare sonnet* deserves a re-reading, and that despite the fact that it is firmly entrenched in the era of symbolic computing.

Citing AI’s recent successes in both GO and Chess, Starnino offers and interesting argument. He notes that when Deep Blue beat Kasparov in 1997 “the IBM supercomputer appeared capable of counterintuitive thought with a baffling move that left Kasparov profoundly unnerved.” Similarly, when AlphaGo be Lee Sedol in Go in 2016 it did so with “a move that so stunned Sedol with its strangeness, he needed fifteen minutes to recover.” AI didn’t beat us in those games by learning to think about them in a human way. Rather:

It beat us because it learned to think in an entirely inhuman way. The scale of AI’s processing power—able to mull millions of strategies and pit itself against those strategies millions of times—found bizarre but superior solutions that centuries of flesh-and-blood play never considered, solutions so removed from normal reasoning as to be alien.

However, poetry

is inexorably linked to how humans think—a kind of undeluded self-questioning that, as T. S. Eliot wrote, helps us become “a little more aware of the deeper, unnamed feelings which form the substratum of our being.” It’s also tied to the need to think this way. A poem’s mental force derives from the set of intentions driving it, intentions that push poets into action. But when GPT-3 gets the call to write a poem, it doesn’t know it’s writing “poetry,” or what “writing” even is. That last part is anything but trivial. Style is a sentient act: you strive for it. My point is that a computer will never replicate what poets do unless it can also replicate why they do it.

There’s more at the link.

* William Benzon, Cognitive Networks and Literary Semantics, MLN 91: 1976, 952-982, https://www.academia.edu/235111/Cognitive_Networks_and_Literary_Semantics.

William Benzon, Lust in Action: An Abstraction, Language and Style 14, 1981, 251-270, https://www.academia.edu/7931834/Lust_in_Action_An_Abstraction.