The Modular Myth

The myth of the modular addition of capabilities contributes in concrete ways to the the emergence of “technology” as a “hazardous concept” (Marx 1997) that refuses interaction with anyone besides experts (conditions to not matter). The hazardous concept, in Leo Marx’s analysis, is that of technology as an “singular noun” capable of acting as an agent in history. In his telling, it is the technology that affects people’s lives and reshapes the possibilities for human existence, not the field of individual actors who comprise the sociotechnical system in which technology is embedded. AlphaGo (Zero), and other spectacular demonstrations, mark the unfolding of DS/ML as a succession of particular inventions, recapitulating in miniature the sweeping narratives of human progress that are marked by key inventions — stone tools, fire, the wheel, gunpowder, semiconductors, perceptrons, reinforcement learning — that have played active roles in human history. Marx goes on to point out that narrow conceptions of technology as constituted by discrete objects like the steel plow or the steam engine are “merely one part of a complex social and institutional matrix”, that is entailed by large scale technosocial system. This understanding informs an understanding of technology as a constitutive force that shapes society as a whole, but particularly reshapes the institutions, including corporations, that are intimately bound up with developing, employing, and deploying new technologies. In the context of this paper, technology may sometimes be seen as an active force in the constitution of the corporation.

The Modular Myth and the Technological Sublime

In American Technological Sublime, David Nye (1996) discusses spectacular presentations of technology as participating in an experience of the sublime. The sublime, in this context, is not a “self-conscious aesthetic theory” but rather a “cultural practice of certain historical subjects” that continually produces “new sources of popular wonder and amazement”, in Nye’s analysis. The modular myths of DS/ML development gain their mythological status from the technological sublime, and considering these spectacles as such through the lens Nye provides is instructive. The sublime, in it’s Kantian, Enlightenment-era sense has both a ‘mathematical’ and ‘dynamic’ aspect. The mathematical sublime pertains to an experience of scale that produces wonder in a human subject. The Grand Canyon, the vastness of space, and the Great Wall of China all exist at scales dwarfing the normal realm of human experience, and produce, according to Kant, a sense of the mathematical sublime. The dynamic sublime is more closely associated with a sense of terror, as when a crowd gathers for a skyscraper demolition or to watch a passing storm from a safe distance.

The spectacular, modular myths of DS/ML participate in both these forms of the sublime, and indeed are key to understanding these cultural objects as spectacles. The scales at which an algorithm may run are constantly foregrounded in promotional materials, as in a documentary about the defeat of Go champion Lee Sedol by AlphaGo, which tells us that “a game of Go has more possible configurations than there are atoms in the universe”. The number of petaflops a computer is capable of, the number of cores and GPUs brought to bear on a computational problem, the nearly infinite permutations of possible outcomes, are all made clear to an audience in order to produce a sense of the mathematical sublime. There is terror in these spectacles, too. Even setting aside the many terrifying scenarios of an “AI Apocalypse” in which machines actually attack humanity (see Dowd 2017), in many ways DS/ML mythmaking points towards a world that doesn’t need human subjects at all, self-driving cars, efficiently optimized factories, and flawless recommendation systems sketch out a world in which the human is mostly incidental. Like Niagra Falls, it will keep churning, oblivious to our existence, and that such a world is possible induces a sense of the dynamic sublime.

While these Kantian forms of the sublime are certainly at play in the modular myths of DS/ML, they are also legible as an iteration of the electrical sublime that Nye presents, in which spectacles moved beyond the realm of the natural world, and were developed specifically for celebrations of industry, nationalism, and amusement. These modular myths, as spectacle, make invisible technologies visible. Electricity was made visible through lighting displays, just as AlphaGo (Zero) makes algorithms and data streams visible, as events that pit a human master of a game against a computer: AlphaGo defeated Lee Sedol in front of a human audience.

The Myth of the “New Electricity”

A crucial point Andrew Ng’s “VP of Electricity” metaphor makes is about the complexity of emerging technologies and the necessity of expertise to adequately grapple with that complexity. Because of the siloed nature of divisions in modern corporations (Rumelt 1974), expertise is not easily distributed across an organization. Supporting DS/ML expertise in any one part of a company will not necessarily translate to other parts of that company, unless they are empowered to make changes beyond their own division. And as the DS/ML experts will not be able to influence business practices outside their own division, it becomes difficult if not impossible to transform those practices in ways that integrate well with the DS/ML projects they work on. By placing a DS/ML expert at the executive level, or by explicitly designing processes for distributing that form of expertise across existing divisions, the complexities of the emerging technology can be addressed in a coordinated, rather than piecemeal, fashion. An expert in DS/ML can approach these capabilities as resource-driven, capable of using data to transform existing products and processes in ways that a modular, bolt-on approach cannot.

The tendency of the discourse around DS/ML towards narrating the emerging technology as a modular addition of capabilities rather than as resource-driven is highlighted by the way Ng’s story was bent towards a metaphor of AI as “the new electricity” (Eckert 2016). Portraying it as such is a subtle rhetorical move that foregrounds the power of the new technology eliding the challenges that remain in building practices around it whilst pointing to future, as of yet unrealized potential. The power of electricity is readily visible to any audience that hears that “AI is the new electricity”, even if not all listeners connect AI, machine learning, and the data that drives it with the role electricity has played as a public utility (as opposed to the private commodity data currently is). Indeed, the challenges that were present in the early days of electrification, however, have receded to the background. It has become infrastructural, visible only when it fails (Star 1999). According to Ng, DS/ML share its eventual invisibility and great power.

CONCLUSION

The algorithms that are powered by data participate in their own set of metaphors. Some are ‘intelligent’, while others are merely ‘smart’. The use of games like Chess or Go as demonstrations of DS/ML perpetuate this metaphor. Such games have long been proxies for human intelligence (Ensmenger 2012), foregrounding certain human skills like foresight, planning and concentration over others like sensitivity, compromise, or even deception. But the use of these games in AI research remain an abstraction of human cognition that fails to capture the entire gamut of human intelligence. These are distinctly human capabilities that set algorithms on an even playing field with people who may feel more threatened than enhanced by their presence in the workplace. This tension between human and machine becomes more acute when DS/ML is described as superhuman, either in terms of being hyper-rational, hyper-vigilant, or omniscient. In some cases, DS/ML is imbued with capabilities bordering on the clairvoyant, as in breathless headlines like, “Google’s AI Can Predict When A Patient Will Die” (Tangerman 2018). Framing the capabilities of DS/ML as on par with, or even as surpassing, human capabilities places it in competition with the humans who must be full participants in any integration of DS/ML into a company. However, this participation is frequently fraught due to an inadequate consideration of the “affective relationship to the product or system, that is, how someone feels about the technology at stake” (Elish & Hwang 2016).

The metaphors of big data tend to treat data as a resource from which value can be extracted. The metaphors of DS/ML tend to treat machines as somehow more than human, which is to say they have many of the strengths of humans (intelligence, anticipation) but few of the weaknesses (inattention, exhaustion). Both of these sets of metaphors elide the uncertainties inherent in the metaphors they employ. Resource extraction is not a linear processes, it involves the failure of exploratory wells, infrastructural costs to move minerals to markets, and shifting price and demand curves relative the costs of extraction. Neither is human intelligence a completely predictable process, particularly where the development of science and technology are concerned.

In this paper, we have discussed the how the prevailing stories that highlight the emergence of data science and machine learning tend towards an understanding of DS/ML as a modular capability. These stories fail to promote transformative practices that might reshape existing business problems into ones that the emerging capabilities of DS/ML can currently address. To do so would require an attention towards data not as an ingredient, but instead as a means through which other things become possible, but also requires a different set of expert practices than those that are currently incentivized by many technical teams, which was particularly true in the case study laid out above. By understanding expertise as sets of practices that can be encouraged and rewarded, rather than as an object that can be possessed by individuals (Carr 2010), we point the way towards undertaking broad shifts in overall business practices by seeking transformative changes that are not siloed within individual departments, but rather have the opportunity to reshape existing practices broadly in pursuit of interfaces that match the underlying technical capacities of DS/ML with the specific, measurable business needs of an organization.

NOTES

Acknowledgements – The authors would like to thank Cloudera Fast Forward Labs; without their support this work would have been impossible. All conclusions represent the work of the authors, and should not be interpreted to represent the position of Cloudera Fast Forward Labs or any of its employees or officers. The authors would also like to thank Dawn Nafus for her generous notes and Jan Philipp Balthasar Müller for reading a late draft. Emanuel Moss is grateful to the CUNY Graduate Center, Data & Society Research Institute, and the Wenner Gren Foundation for their support.

1. https://aws.amazon.com/ml-solutions-lab/

2. https://cloud.google.com/asl/

3. https://www.elementai.com/

2018 Ethnographic Praxis in Industry Conference Proceedings, pp. 264–280, ISSN 1559-8918