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This work of generative Internet art presents an essay to readers that reads like an essay written by a graduate student that has done nothing but read Postmodern theory for years. The result might be brilliant, nonsensical— perhaps both— but it exists on a different reality as the rest of the world’s and is likely to have little impact on anything. You might as well pump all that high theory into a machine and put together a little program to produce some semi-random output from that lexicon and then see if readers will read the results at face value.
For this piece to have any function at all, requires a mind that is eager to project meaning onto experience. If we expect an experience to be meaningless, our minds certainly do not bother to piece together the chaos of clues that make the world comprehensible. With Chomsky’s famous pseudo-sentence “Colorless green ideas sleep furiously.” for example, we undergo an initial attempt to identify a meaningful message. Convincing the mind to choose at the crossroads between potential comprehensibility and inevitable noise is an important task.
This is a crossroads readers of poetry reach when they come across a particularly challenging poem. In many cases, they will make the interpretive leaps because they hope the poet’s intentional hand (whether real or imagined) will catch them and lend them support in their interpretation— either that or because their professor asked them to and they know they have him or her as a safety net. But what is the point in reading the output of this “behavioral art?”
Part of it is figuring out the intention behind the algorithm. What point does JudsoN want to make with a work that generates endless theory papers, with keywords, sections, figures, captions, references, and a title? Is this work a critique of this kind of writing?
But more interesting (to me) is to examine the artistry in the program— one we can’t access but we can intuit, and even reverse-engineer. And the only way we can do so is by reading multiple iterations, carefully, to find patterns, repetition, variations, places where the algorithm fails or suceeds in producing grammatical or sensible sentences. This is old-school data mining: carefully focused human intelligence scrutinizing a text to find conceptual gems. Gloriously absurd or lucid phrases that excite the intellect, even if by accident.
The search for meaning that drove the New Critics teach generations of scholars and students to perform close readings of texts, was completely subverted by Post-structuralist theory, which showed that all meaning is constructed and can therefore be deconstructed. We might have come full circle with generative works like “Essay,” carefully reading that which we know is meaningless, because we might find something worth ascribing meaning to.