Core Post: Algorithms & Their Others

“What is generally asked of an algorithm is that it produce a correct output and use resources efficiently” (Bucher 23) or “‘to produce a desired outcome’” (Kitchin in Bucher 21), “encoded procedures for transforming input data into a desired output, based on specified calculations” (Gillespie 1). I am curious the relationship of desire to algorithm and procedure, how the propositions and inputs and systems are set up willing the outcomes into ‘right’ and ‘wrong’ echoing in the repeated emphasis on the ‘correct’ answer in these definitions, by which these technologists likely mean the answer that solves the problem they’ve proposed. However, this discipline’s methodological orientation toward problems and correct solutions may predispose them toward particular types of solutions that feel or seem correct based on criteria that come with their own constraints and biases—we might look to the feedback loops of Gillespie’s items 1) “patterns of inclusion,” 3) “evaluation of relevance,” and 5) “entanglement with practice: how users reshape their practices to suit the algorithms.”

What would it mean to consider ‘solutions’ that come out of different desires? Already in these feedback loops of algorithmic thinking, how can we begin to imagine these? Is it as binary as all that either-or? Here’s where I take the algorithm for a walk.

Desire lines are unpredicted trajectories across distance, unanticipated data. If desire is an artifact of distance, if longing a length, an impulse to move toward that implies and requires space. One approach to desire is to quench it, and the procedural is an effort to foreclose, to solve. “Machine learning algorithms reduce this uncertainty by making predictions about the likelihoods of outcomes. Put differently, machine learning is about strengthening the probability of some event happening, based on evolving information” (Bucher 28). I am interesting in this Xeno’s paradox of uncertainty, probabilities, distances that cannot entirely foreclose or finish. Where Bucher says, “an algorithm as ‘a strategy or plan of action’ [...] always in becoming since events are not static but unfolding” (paraphrasing Chun 28), this version suggests a process-oriented algorithm—almost an A Thousand Plateausean mood through which to reframe: becoming-fill-in-the-blank. I went back to Chun’s original, and unfortunately I think Bucher misreads... Chun was critiquing such thinking, ends reflecting back to justify means. But I’ll leave the thought, because what if we were to imagine—in addition to what Bucher argues, that “algorithms have the ability performatively to change the way events unfold or, at the very least, change their interpretation” (28)—that the algorithm itself is changed, always imperfect and in progress? This is much closer to how machine learning is working these days anyway, and makes me think of how Luciana Parisi (2013) talks about anticipatory systems in Contagious Architecture, systems that rather than being merely interactive, “sense and anticipate (or productively prerespond to) changes in atmospheric pressures, moods, sounds, images, colors, and movements, incomputable data have infected the general ecology of media systems” (25). To anticipate, to respond to another’s desires, one must be able to adapt, and are there queer potentials and desires possible in such systems? Could they be imagined?

How do we contrast this question of different desires and works-in-progress with something like “power that works from below [... that] becomes indistinguishable from life itself by sifting into ‘the capillaries of society’” (Lash in Bucher 34)? How do we know different desires within proceduralized systems predicting us under our skins, algorithmic power as “immanent life force”? But are we willing to grant it that much power, or is that only to desire the algorithm itself instead—to make it the fetish object instead of a means of investigation or access?

Links for Presentation/Discussion

Simplification: Nick Montfort, @one_algorithm

A bot (poem) that simplifies to the point of boredom the mystique of algorithms into a procedural, almost bureaucratic walkthrough of its permutations until it quits after having exhausted them all. 

Complexification: Jer Thorpe, You Say Data, I Say System

“The next time you read a story with the word data in the headline, swap it out with data system. When you see a data visualization, think of it instead as a data system visualization. If the government proposes new policies around personal data, think about them instead as policies about people, and the data systems which they inhabit.” 

Ancient Babylonean Algorithms: The Earliest Programs

“Babylonian Programming: [...] they represented each formula by a set-by-step list of rules for its evaluation, i.e. by an algorithm for computing that formula. In effect, they worked with a 'machine language' representation of formulas instead of a symbolic language.” 

Each algorithm closes with “This is the procedure.”

Sarah Ciston, Uncertainty Index

How does the body digest the computational and conform to it? Conversely, how does the algorithmic procedure (fail to) capture the non-binary, doubtful, less-than-ideal human interface? How do Gillespie’s “cycles of anticipation” converge into grotesque portraits of subjectivity that culled from “creepvenient” interfaces?