Synthesizing Naturalized Language Patterns to Simulate Meaningful Thresholds of Purpose for Interchangeable Labour in Specialized Knowledge Economies

Tonight in the Department of English and Film Studies at the University of Alberta, Professor Harvey Quamen delivered this year’s F.M. Salter Lecture on Language: “Written in Code: Computers as Language Machines.” During question period, humanists and computer scientists alike raised concerns about the implications of Quamen’s titular thesis in regards to the distinction between natural vs. programming languages (e.g., “Can a computer use metaphors?”) and human vs. computer agency. I read these comments as defensive, even anxious—humanists not wanting to concede agency or the traditional territory of human(istic) inquiry, creativity, and thought (language) to a non-human “tool” (e.g., in response to one commenter’s use of the word “make” in regards to computer language, another audience member insisted that humans “create”), and computer scientists likewise exhibited disciplinary anxiety about Quamen’s proposal that we change our metaphors about the computer from one of number-crunching to one of linguistic creativity. I’m surprised (yet, because I’ve witnessed a lot of academia, not surprised) that some humanists, even in view of the emergence in recent decades of such human-decentering perspectives as actor-network theory, object-oriented ontology, ecocriticism, and animal studies, have implicitly or explicitly still not accepted that the author and the human are historically unstable politically constructed concepts and that there is little “natural” about the metaphorically-invested manipulations of sonic material by one particular primate species that deceives itself with that very material by naturalizing its particular anthropocentric use of it as “natural.” The reality of distributed agency, humans’ non-centredness, and hence humans’ non-proprietary hold over “natural” language, should not even be questions anymore. The same people who raised these objections found their ways home using cell phones and GPS while reading articles that may have been written by robots, on computers whose programs may have been written by other programs.

It is to our own detriment to deny the reality that language is no longer ours alone to command (as if it ever was!—this is the faulty ideological assumption that interpellates us as subjects: the assumption that, in Lacanian terms, when the Big Other of the symbolic order speaks through us, “I” speaks). As Federica Frabetti and Tara McPherson both argue in separate articles, we as tool-modular organisms cannot treat our technologies as neutral; they affect the way we think, the actions we take, the organization of our culture. McPherson discusses modularization as a paradigm of programming and software design complicit with racism and the capitalistic logic of the “factory” (150) and, especially nowadays, of the university:

“The intense narrowing of our academic specialties over the past fifty years can actually be seen as an effect of or as complicit with the logics of modularity and the relational database. Just as the relational database works by normalizing data—that is, by stripping it of meaningful, idiosyncratic context, creating a system of interchangeable equivalencies—our own scholarly practices tend to exist in relatively hermetically sealed boxes or nodes.” (154)

The defensive disciplinary displays at tonight’s lecture illustrated precisely this weakness of specialization. I’m not about to stage another disciplinary display by saying interdisciplinarity gets us out of the box (to the relational database, it too can be just another category); rather, I only want to discuss further the implications of being boxed in.

Several of my blog posts thus far in response to the digital reading paradigm shift in the humanities have dealt with the question of randomness and probability, and I am about to embark on trying to get a computer to read one hundred trillion poems in order to further probe these issues, which radically challenge humanistic epistemological and methodological assumptions—what interpretive weight does one reading of one text bear, if it’s just one of one hundred trillion possibilities? Why read that way instead of n other ways? Why not, even, using code, build conditionals and adjustable parameters into one’s reading?

The challenge raised by such questions is due similarly to what McPherson calls (in regards to academic specialization) a “system of interchangeable equivalencies” (154): the risk that the knowledge we produce is arbitrary and interchangeable. While it’s true that most individual contributions to scholarship or otherwise are going to be relatively small, and it’s difficult to predict how such individual contributions can collectively add up over time to change a culture, it’s also true that extreme specialization is also the logic of the assembly line and corporate programmer collectives (you design one molecule of one cog of one wheel and will never gain security access to what purpose it serves in the giant mysterious machine being built) and that interchangeability is also the logic of monetary capital (the universal equivalent). From the perspective of capital, our contributions are arbitrary and interchangeable; but the danger now is how, because of the epistemological shift toward the macroscalar that we are facing, the interests of capital align with that shift.

A few years ago, Matt Might’s “illustrated guide to a Ph.D.” started circulating as a meme, telling a pretty story about what a Ph.D’s cog-molecule will do: if a circle is the sum of all human knowledge, then when you become an expert in one area, you will push that circle’s radius ever so tinily forward—but push it you will! Progress will be made!

Scott Weingart deconstructs the picture this way, though: as that circle grows larger with time, then “every new dissertation expanding our radius…increas[es] the distance to our neighbors” such that “the inevitable growth of knowledge results in an equally inevitable isolation. / This is the culmination of super-specialization: a world where the gulf between disciplines is impossible to traverse, filled with language barriers, value differences, and intellectual incommensurabilities” (np), or in McPherson’s words, “the divide-and-conquer mentality that the most dangerous aspects of modularity underwrite” (153).

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It’s not just a question of (inter)disciplinary community, though, but of epistemology and politics. If what we take for “knowledge” can just as easily be duplicated by a machine (whether this machine involves human components such as “programmers” and “CEOs” or not) capable of analyzing billions of patterns of human speech and discourse in order to calculate what degree of patternicity must be simulated in order to dupe such-and-such pattern-seeking human into thinking of such discourse as “meaningful” or “truthful”, then what knowledge producers like PhDs may be “producing” is less importantly “knowledge” than a form of labour that, if it has any knowledge-value at all, will, more likely than not, further serve the interests of the Big Data machines that crowd-source such individual sequestered research in order to make Bigger claims with it. If the Big Data epistemological paradigm expands that “circle of knowledge” such as to render isolated individual efforts and claims increasingly obsolete, then it will succeed in subordinating those individuals themselves to a (relatively, according to the Big Data pardigm) meaningless pattern-seeking behaviour in fact serving as merely a sample-size-increasing node within a larger machine.

I recognize the cynicism of such a perspective; however, it is something that admitting our lack of control of and exclusive right to language, in combination with the current shift in epistemology, should make us pause and think (or maybe, to think less).

As McPherson writes, “Our screens are cover stories, disguising deeply divided forms of both machine and human labor. We focus exclusively on them increasingly to our peril” (152).

When there are so many Netflix or other movies you can watch (“research”, if you prefer) – even filtering for good ones within a genre you like – then does the particular flashing on the screen (and all its “generic” variations, its postmodernly indifferent interchanging of tropes) matter so much anymore as merely the fact that the content causes the screen to flash with enough patternicity to keep your pattern-seeking  mind’s butt at the edge of its seat (but still, crucially, in its seat)?

Non-Digital Works Cited

Frabetti, Federica. “Have the Humanities Always Been Digital? For an Understanding of the ‘Digital Humanities’ in the Context of Originary Technicity.” Understanding Digital Humanities. Ed. David M. Berry. 161-171. New York: Palgrave MacMillan, 2012. Print.

McPherson, Tara. “Why Are the Digital Humanities so White? or Thinking the Histories of Race and Computation.” Debates in the Digital Humanities. Ed. Matthew K. Gold. Minneapolis: University of Minnesota Press, 2012. 139-160. Print.

Synthesizing Naturalized Language Patterns to Simulate Meaningful Thresholds of Purpose for Interchangeable Labour in Specialized Knowledge Economies

On Exactitude and Messiness in (Big) Science (Data)

The stories of Jorge Luis Borges, who tellingly spent most of his life as a librarian, read like thought experiments (Stephen Ramsay would approve: “an impossible fantasy constructed for the purpose of divining the possibilities of the real” [23]), and seem to crop up again and again when discussing matters of information, representation, and hypertextuality. As I’m reading Viktor Mayer-Schönberger and Kenneth Cukier’s Big Data, the Borges story that comes to mind is “On Exactitude in Science,” a paragraph-long fable you can quickly read if you haven’t. Jean Baudrillard uses the story to open Simulacra and Simulation, an appropriately fake copy of which appears in the Waichowskis’ The Matrix (where Neo keeps a cracked disc he sells on the black market), which also borrows from Baudrillard’s summary the phrase “Desert of the Real,” subsequently used by Slavoj Zizek in Welcome to the Desert of the Real. In his widely cited article “Database as a Genre of New Media,” Lev Manovich reworks the story for databases:

“Jorge Luis Borges’s story about a map which was equal in size to the territory it represented became re-written as the story about indexes and the data they index. But now the map has become larger than the territory. Sometimes, much larger. Pornographic Web sites exposed the logic of the Web to its extreme by constantly re-using the same photographs from other pornographic Web sites. Only rare sites featured the original content. On any given date, the same few dozen images would appear on thousands of sites. Thus, the same data would give rise to more indexes than the number of data elements themselves.” (181)

My opinions on Manovich have shifted somewhat since discussing the shortfalls of his “database and narrative are natural enemies” (181) formula in light of responses surrounding Ed Folsom’s “Database as Genre,” but Mayer-Schönberger and Cukier’s Big Data compels me to wonder how Borges’s story would be “re-written” for Big Data.

Mayer-Schönberger and Cukier argue Big Data is defined by three features: there’s “More” data, it’s “Messy,” and it privileges “Correlation” (what) over causation (why). I bring up Borges’ “On Exactitude in Science” in response to the second feature: “a willingness to embrace data’s real-world messiness rather than privilege exactitude” (19). Ironically, the authors are consistenly inexact about this exactitude-messiness distinction. To illustrate, they compare three different scenarios, two non-Big Data, and one Big Data, “to measure the temperature in a vineyard” (34).

Scenario 1 (Non-BD): “If we have only one temperature sensor for the whole plot of land, we must make sure it’s accurate and working at all times: no messiness allowed” (34).

This scenario is misleading. No system is 100% efficient, no matter how accurate or consistently operational. If you have one temperature sensor for a “whole plot of land,” your reading’s accuracy relative to the whole is never going to be 100% accurate, and will vary depending on the size of the plot, whose temperature is not going to be consistent throughout. This is the same mistake people make when they listen to “a 20% chance of showers” forecast. It actually means there is a 20% average probability of rain in all the subregions analyzed. So in one neighbourhood of your city there could be a 8% chance of rain, and 80% in another, but there is no way of knowing from the shorthand recited to you by newscasters. Moreover, the kind of sensor used is going to matter; Wikipedia lists six different kinds of “temperature sensors”; the authors don’t even specify if we’re dealing with a mechanical instrument or an electrical one. This additional information matters; the authors shouldn’t be using such a hypothetical scenario, especially when they’re trying to inform us about differences in information yield across methodologies.

Scenario 2 (non-BD): “In contrast, if we have a sensor for every one of the hundreds of vines, we can use cheaper, less sophisticated sensors (as long as they do not introduce a systematic bias). Chances are that at some points a few sensors may report incorrect data, creating a less exact, or ‘messier,’ dataset than the one from a single precise sensor. Any particular reading may be incorrect, but the aggregate of many readings will provide a more comprehensive picture. Because this dataset consists of more data points, it offers far greater value that likely offsets its messiness” (34).

First, the authors imply that because the sensors are less sophisticated, more of them will report incorrect data. The only reason we did not see this phenomenon in Scenario 1, then, is because we only had one sensor, which, relative to the other elements in its set of one, was 100% accurate. However, as discussed, in reality no sensor is 100% accurate. Therefore this bit about incorrect data amounts to only a general statistical observation, that a large set of objects is likely to include variations—something to which the “single precise sensor,” as one sensor among a group of many other possible choices of sensor, is no less subject; the authors just didn’t think this was the case because they were blind to the blindspots of that sensor’s precision which they meaninglessly decided to assess self-relatively as 100%. By adding (relative to Scenario 1) two variables at once, more sensors AND less sophisticated sensors, the authors needlessly confuse a relative loss in sensory quality with the constant loss of sensory quality (noise, blindspots) inherent to any information system. This produces the illusion that the resulting data is “less exact” or “messier” only because of a loss of quality of the sensors, rather than also (and nonetheless) because more information (from more sensors) now needs to be made sense of. As a result, the “incorrect[ness]” of “[a]ny particular reading” is overstated as an absolute value instead of as a value relative to all other readings in the dataset, and the “aggregate of many readings” is overstated as “more comprehensive” without defining “comprehensive” (they seem to use it as a synonym along with “precise” and “accurate”) and therefore without also specifying the increased level of abstraction (again, the constant of noise) resulting from increased aggregation: we are no longer dealing with one data point, absolutely stated, but a measure of central tendency abstracted from multiple and relative data points—a measure, therefore, no less subject to blindspots however more “comprehensive.” This results in meaningless or messy statements like the biased “as long as they do not introduce a systematic bias” (their lesser degree of sophistication would itself already constitute a systematic bias!), and “greater value…likely offsets its messiness.”

Scenario 3 (BD) is Scenario 2, plus an increased frequency of sensory readings. Again, loss of quality arising from degree of sensory sophistication (here, they also add in issues of information transmission, which amounts to the same thing, just at a different point in the process), are conflated with issues arising from perspectival limitations, to the suppression of the latter. The authors state that, because of noise, “[t]he information will be a bit less accurate, but its great volume makes it worthwhile to forgo strict exactitude” (34). It is true that a better ratio of information to noise results in a clearer message, but this has nothing to do with forgoing the chimera of “strict exactitude”; it is only the same situation we’ve been in all along, with each scenario. The “strict exactitude” of the first scenario only arises because both “volume” and “exactitude” are taken to equal one. The authors are so noisy in communicating their ideas that they miss their own information theoretic joke: “[t]he information will be a bit less accurate” is more than one bit inaccurate.

They then conclude about Scenarios 2 and 3 as follows: “In the first example, we sacrificed the accuracy of each data point for breadth, and in return we received detail that we otherwise could not have seen. In the second case, we gave up exactitude for frequency, and in return we saw change that we otherwise would have missed” (35). By “breadth,” they mean sample size: more sensors meant more data; but by “frequency,” don’t they also mean sample size? More sensors measuring more frequently means a larger sample (or higher sample rate). It thus seems to me that, contrary to their three special features of Big Data, the only difference between these three scenarios, as they’ve articulated it, is sample size. (In this, I agree with Ted Underwood that Big Data is about scale and scale is relative. So, just as Digital Humanities will one day be called humanities, Big Data will probably just be called Data, once Earth becomes but one data point among many.) And even then, they misunderstand sample size, because in Scenario 1, they think that a sample size of 1 will lead to the most “precise,” “accurate,” or “exact” results (terms which, so interchanged, cease to describe themselves).

Even when, as they elsewhere claim, Big Data measures a population, not a sample (a purview not unique to Big Data), it can only do so with the same biases inherent to any information system. Even if you’re measuring a population, the law of induction dictates that you will never know when you’ve actually obtained a “complete” population (you never will except arbitrarily), that population will only measure a sample of parameters, from a sample of endless possibilities. While the authors at least acknowledge that “messiness is not inherent to big data” but “a function of the imperfection of the tools we use to measure, record, and analyze information” (41), they also make claims like “Moving into a world of big data will require us to change our thinking about the merits of exactitude” (40) (maybe admitting the inexactitude of human methods has shock-value for the popular and business audience he’s writing for…), “Big data transforms figures into something more probabilistic than precise” (35) (statistics in general is a probabilistic science), “Processing big data entails an inevitable loss of information” (45) (information loss is inevitable, period), and “In many areas of technology and society, we are leaning in favor of more and messy over fewer and exact” (42) (fewer and exact are relative terms, and just as messy depending on how they’re used).

But lastly, most problematically, this kind of rhetoric and lack of understanding of the inherent biases of perspective and information systems, leads the authors to grandiose and paradoxical claims about Big Data’s access to “the totality of information,” though through “slackness instead of exactness” (47); that “Big data, with its emphasis on comprehensive datasets and messiness, helps us get closer to reality than did our dependence on small data and accuracy” (48); and that as it “become[s] a regular part of everyday life, we as a society may begin to strive to understand the world from a far larger, more comprehensive perspective than before, a sort of n=all of the mind” (48).

John Law, in an article on messiness called “Making a Mess With Method,” would characterize such viewpoints as only typical of the common-sense realist, who assumes that there is a world “out there” that is definite, singular, and anterior and indepedent to any viewpoint on it (5-6). But in reality, things are messy, and some “things are at least sometimes vague and can only be known vaguely” (5).

This all makes me think that the Big Data version of Borges’ “On Exactitude in Science” may be simply the same story: n=all.

Non-Digital Works Cited

Mayer-Schönberger, Viktor and Kenneth Cukier. Big Data: A Revolution That Will Change How We Live, Work and Think. New York: Mariner, 2014. Print.

Ramsay, Stephen. Reading Machines: Toward an Algorithmic Criticism. Chicago: University of Illinois Press, 2011. Print.

On Exactitude and Messiness in (Big) Science (Data)