Year: 2018
Type: conference-paper
Abstract: 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.
Authors: EMANUEL MOSS, FRIEDERIKE SCHÜÜR
Institutions: The Graduate Center, CUNY, Cloudera (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2912917607