I’m going to offer this mercurial fella a position as the mascot for Interstice. Go Monsters Go!
On the importance of irritation in the creation of meaning
“Only Barthes, among the men, was at ease with incarnating a site that cannot be designated, a matte faubourg, without qualities.”
It started with this strange opaque phrase capturing my attention in “In Defense of Nuance,” Wayne Koestenbaum’s foreword to Barthes’ A Lover’s Discourse (1978). It’s a phrase that only a fraction of readers could be expected to grasp. The words “a matte faubourg” were meaningless to me; a semantic collapse, a gap. However I did not drift over them but instead I stumbled, felt irritated, paused, mused, googled, mused some more…
Most people loathe what is often seen as the overly complex language of academic and critical texts, and roll their eyes at what is seen as the intention of contemporary art to irritate through cleverness or shock. At its worst, the fruits of modern discourse are alienation, ironic detachment, and a stratified system of insiders and outsiders.
But is this the only function of such disruptions, or can their impact contribute something deeper to human life? Can the art of skilled and meaningful disruptions be developed? And where does responsibility for this occur? Is it the job of the consumer of culture to pause and educate themselves in order to engage more meaningfully with disruptive language or imagery? Or is it the role of the author of the work to produce skillful, creative disruptions?
…A matte faubourg.
It is in fact a symbol that represents itself, a gap, detour, an empty site, an unrendered image, titleless and isolated.
In that moment, the text became a poem, linguistic friction that invited me into the play of nuance, beyond the symbols of meaning (the content) and into an effect of meaning (the play).
“A matte faubourg” frustrates a reader bent on overt meaning, but overt meaning may not be the ultimate function of a text. When I unwittingly read “a matte faubourg” I did not experience a metaphor, I experienced a matte faubourg directly, I danced with it, I experienced being pulled into that non-space.
Apophatic Acts of Unsaying
This “meaning event” – the momentary union of predicated meaning and direct experiential meaning – is at the heart of an apophatic discourse. Normally, language betrays direct experience, for words create distance, slippage. Language delimits objects and entities, but if the true subject of discourse is not static, non-object and non-thing, how can language be accurate? Author Michael Sells proposes that rather than foreclose on this problem with either non-saying (e.g. Zen Buddhism), or an analysis of the borders of the sayable and the unsayable (e.g. scientific method), one can actively engage the irresolvability of the problem by harnessing its infinite regress.
Unlike a discourse constructed out of finite assertions, apophasis (Greek: “un-saying”) is a propositionally unstable and dynamic discourse in which no single statement rests its own as true or false or even as meaningful. It is not the content of the sayings that is significant. The essence of the practice is that any propositional statement requires an undoing, a destabilizing revision, and it is the tension between proposed meaning and collapsed meaning that becomes important. Meaning events emerge from this tension, but each event is momentary, and must be “continually re-earned by ever new linguistic acts of unsaying.” Therefore apophasis is not asserted but performed.
Moral Machines is an interesting piece by Gary Marcus in the New Yorker, exploring the increasing confrontation between automated technology and moral decision making. That confrontation is the site of an important dialogue about the complexity of morality and human behavior.
One front has been the driverless car – now functional as a machine, but dysfunctional within our current ethical-cultural-legal framework. The driverless car confronts ethical frameworks based on personal responsibility. Although it may be dramatically safer statistically for me to ride in an automated vehicle, I would be reluctant to give up my sense of personal control to a machine. I would rather take a 1:100 statistical risk of crashing due to my own error, than a 1:1000 statistical risk of crashing due to a program malfunction – because on a gut level I believe that personal control is equivalent to safety. Beyond my personal reluctance to give up perceived control, it challenges the existing social systems that reinforce personal responsibility – licensing, insurance, laws, justice.
The article reveals another side to the confrontation, the necessity for the machines themselves to have moral reasoning coded into their operations. Marcus gives the example:
“Your car is speeding along a bridge at fifty miles per hour when errant school bus carrying forty innocent children crosses its path. Should your car swerve, possibly risking the life of its owner (you), in order to save the children, or keep going, putting all forty kids at risk? If the decision must be made in milliseconds, the computer will have to make the call.
It’s the Trolly Problem, no longer a thought experiment but a real world decision, left to a computer.
Marcus is lucid about the fundamental issue with this problem: morality is complex, dynamic, relational and evolving, while the codes that run programs are brutal and rule based.
‘The thought that haunts me the most is that human ethics themselves are only a work-in-progress. We still confront situations for which we don’t have well-developed codes (e.g., in the case of assisted suicide) and need not look far into the past to find cases where our own codes were dubious, or worse (e.g., laws that permitted slavery and segregation).”
This perhaps always has been a problem that moral reasoning struggles with. How can complex truths can be reflected in institutional frameworks? It is a great social taboo to let complex, dynamic, relational and evolving systems be the mess that they are. Institutionalization equals validity. But a judge needs a jury…
We might find that jury in the emerging field of machine learning. Generally, machine learning involves a computer program being able to learn without being given an explicit program. By providing the system with a lot of training data, it “learns” by recognizing and responding to patterns, rather than by applying a given set of rules to execute. This enables the program to engage meaningfully with tasks that contain a lot more complexity than we have been able to capture in a set of codes. Voice and image recognition software are examples of this. It is very difficult to tell a computer how to determine an outline of an object or recognize the word “fork” in 70 different accents. But, given a ton of raw data (via Youtube images and Googlevoice audio respectively), programs have learned how to make these differentiations with more complexity than a programmer could write.
“We are going to have to slow down, reorient and regulate the proliferation of monsters by representing their existence officially.”ii
In the above quote, Bruno Latour muses on the tension between two ways of relating to information, which he calls “purification” and “translation.” Purification is the separation and specialization of knowledge. It distills, reduces and creates partitions, separating knowledge into distinct and exclusive realms.
Translation is hybrid and continuous knowledge, stitching together disparate fields and perspectives into networks.
Purification is the acknowledged project of modernity, and it is what generally passes for knowledge in the contemporary world. We go to scientists and mathematicians for facts, priests and philosophers for morality, artists and sociologists for a critical examination of the discourse itself. Generally, the more purified information is, the more “true” it feels to the modern person.
Hybrid knowledge on the other hand feels “uncanny, unthinkable, unseemly”: think creationist museums, a doctor doing energy healing, a politician admitting uncertainty. It is taboo to cross the lines, we are uneasy with these monsters.
I’ve always been a fan of monsters.