CUNY Graduate Center / Data & Society
Cloudera Fast Forward Labs
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The successes of technology companies that rely on data to drive their business hints at the potential of data science and machine learning (DS/ML) to reshape the corporate world. However, despite the headway made by a few notable titans (e.g., Google, Amazon, Apple) and upstarts, the advances that are advertised around DS/ML have yet to be realized on a broader basis. The authors examine the tension between the spectacular image of DS/ML and the realities of applying the latest DS/ML techniques to solve industry problems. The authors discern two distinct ways, or modes, of thinking about DS/ML woven into current marketing and hype. One mode focuses on the spectacular capabilities of DS/ML. It expresses itself through one-off, easy-to-grasp marketable projects, such as DeepMind’s AlphaGo (Zero). The other mode focuses on DS/ML’s potential to transform industry. Hampered by an emphasis on tremendous but as of yet unrealized potential, it markets itself through comparison, in particular the introduction and adoption of electricity. To the former, data is a mere ingredient, a current, but not a necessary, requirement for the training of smart machines. To the latter, data is a fundamental enabler, a digital, always-giving resource. The authors draw on their own experiences as a data scientist and cultural anthropologist working within industry to study the impact of these modes of thinking on the adoption of DS/ML and the realization of its promise. They discuss one client engagement to highlight the consequences of each mode, and the challenges of communicating across modes.
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INTRODUCTION
In a midtown Manhattan conference room, the audience is nodding along to the presenter’s slides. Artificial intelligence seems so accomplished and yet so straightforward, from Google DeepMind’s Go-playing AI agent AlphaGo (and successors) and Carnegie Mellon’s poker-playing Liberatus AI to Sunspring, a short film based on a movie script, replete with dialogue and stage directions, that was written by a neural network. Let two computers play Go against each other and let them learn from their mistakes until they get better than human Go grandmasters. Feed a neural network with movie scripts until it writes one of its own. The artificial intelligence industry has long been adept at foregrounding the “magic” of AI systems (Elish & boyd 2017). On that day, the audience in the conference room was comprised of employees from an entertainment media company who were identified prior to the event as key stakeholders in how the company collected, analyzed, and utilized data across the many lines of their business. They were interested in using data science and machine learning (DS/ML) for their organization and had sought the help of DS/ML experts to do so. Specifically, they were interested in a “data strategy”, a set of project, people, and process recommendations designed to help them harness the potential of DS/ML. The day began with a recognition of the many magical things that data science and machine learning (DS/ML) is capable of. Over the course of the day, conversations shifted towards practical applications of DS/ML and the conditions that allow DS/ML to succeed within organizations.
This paper grapples with the ways in which the contrasting narratives that surround the development of data science, machine learning, and artificial intelligence present different, at times seemingly opposed, paths forward as enterprises develop strategies and make tactical decisions around these emerging technologies. We identify and examine two contrasting narratives for the emergence and development of these technologies. In analyzing the myths and metaphors that attend to the discursive production of DS/ML, we follow Sturken and Thomas (2004), who observe that “metaphors about computers and the Internet are constitutive; they determine how these technologies are used, how they are understood and imagined, and the impact they have on contemporary society”. So too do these metaphors determine how businesses strategize around DS/ML.
STORIES AND MYTH-MAKING
The history of technological development is populated with spectacular demonstrations designed to hasten the development of and increase public demand for new products. The spectacular demonstrations of electrical lightning at World Fairs, Centennials, and other grand exhibitions of the late 19th century were designed to increase consumer adoption of the light bulb and serve as a (literally) shining proponent of the potential uses of electricity (Nye, 1994). Similarly, prominent players within DS/ML build and promote spectacular demonstration projects. These demonstration projects take a range of forms; they highlight an emerging capability (e.g., the capability of generating new text; natural language generation) or are engaging in ways that generate press coverage (as when a machine defeats a human expert). These demonstrations serve a range of functions; they establish their producers as serious players in the industry, they promote existing products and services offered under the same brand, they burnish the resumes of those who work on them. Primarily, they perpetuate excitement, and investment, in DS/ML.
AlphaGo Zero and the Modular Myth
AlphaGo (Silver et al. 2016) and its successor AlphaGo Zero (Silver et al. 2017) are algorithmic systems built by Google’s DeepMind that spectacularly defeated reigning human world champions of the board game Go. Go is a complex game, with millions of possible moves and billions of possible board configurations. In their release notes of AlphaGo, DeepMind foregrounded the complexity of the game itself and the remarkable achievement of building an agent that can learn to cope with that complexity from human gameplay data. In announcing AlphaGo Zero, DeepMind’s promotional materials foregrounded the ability of the algorithmic system to learn from self-play: AlphaGo Zero learns on its own by playing against itself. In the process, it learns strategies that resemble strategies of human Go players, as well as a few novel others (Silver et al., 2017). The accomplishments of AlphaGo and AlphaGo Zero appear as evidence that AlphaGo must be very intelligent since Go is commonly understood as a complex game that only the most intelligent humans can learn to play well. And while AlphaGo still needed some human help, in form of human game playing data, AlphaGo Zero freed itself from this requirement, this dependence on human expertise and labor.
The Conditions of Success for AlphaGo (Zero)
Under scrutiny, we discover that, as Andrej Karpathy put it, “AlphaGo is a narrow AI system that can play Go and that’s it” (Karpathy 2017); the success of AlphaGo is grounded in several conditions or “conveniences” of the game Go (see Table 1).
Table 1. Conveniences of Go (adapted from Andrej Karpathy [2017])
| Deterministic | The rules of Go describe possible game states without any randomness or noise. |
| Fully Observed | Each participant knows everything about the current state of the game simply by looking at the board. |
| Allows only discrete actions | There are a quantifiable number of different moves that are possible without gradations between these moves. |
| Is simulatable | It is easy to simulate a game of Go and this simulation will be identical to the game itself. |
| Is short | Each game of Go lasts approximately 200 moves. |
| Has a clear outcome | There is a clear definition of what constitutes a ‘win’ or ‘loss’. |
| Is well-documented | There are hundreds of examples of human gameplay to supercharge the initial knowledge that AlphaGo begins learning from (AlphaGo Zero, of course, freed itself from this condition). |
Few ‘real-world’ problems, problems that one is likely to encounter in industry, share these conveniences with the game Go. Real-world problems are full of imperfect information, vaguely defined in terms of a success metric, rare enough that trainable examples are hard to come by, or they involve continuous phenomena rather than discrete moves that allow for gradations between possible states. Arguably, most real-world problems are more complex than the game of Go (see also, Elish & boyd 2017); in DeepMind’s promotional material and the paper detailing the algorithms that power AlphaGo (Silver et al., 2016) and AlphaGo Zero (Silver et al., 2017) complexity is defined in terms of combinatorics, the number of possible board configurations, a narrow definition of complexity. Furthermore, most real-world problems are only simulatable through deliberate decisions about what is and is not part of a system that do not come close enough to approximating reality to be good representations of the problem at hand. The weather does not affect the outcome of a game of Go, yet it is likely to be relevant for algorithms that steer self-driving cars; most real-world problems tie into dynamics part of the world that require us to make decisions about what is relevant and what is not when we model the system. As Karpathy concludes his analysis of AlphaGo (and AlphaGo Zero), it demonstrates not so much the power of DS/ML, but rather shrewdness in choosing a tractable yet impressive problem, as well as the power of Google to devote its resources to create a system that can tackle such a difficult, if singular, problem (Karpathy 2017). AlphaGo and AlphaGo Zero are only two of the more recent spectacular projects, of course, that demonstrate the dramatic promise of DS/ML. Carnegie Mellon’s poker-playing Liberatus AI beat humans in games of Texas Hold ‘Em, for example.