Rethinking Communication, Technology, and Ourselves
Edited By Andrea L. Guzman
From virtual assistants to social robots, people are increasingly interacting with intelligent and highly communicative technologies throughout their daily lives. This shift from communicating with people to communicating with people and machines challenges how scholars have theorized and studied communication. Human-Machine Communication: Rethinking Communication, Technology, and Ourselves addresses this transition in how people communicate and who, or what, they communicate with and the implications of this evolution for communication research. Geared toward scholars interested in people’s interactions with technology, this book serves as an introduction to human-machine communication (HMC) as a specific area of study within communication (encompassing human-computer interaction, human-robot interaction, and human-agent interaction) and to the research possibilities of HMC. This collection includes papers presented as part of a scholarly conference on HMC, along with invited works from noted researchers. Topics include defining HMC, theoretical approaches to HMC, applications of HMC, and the larger implications of HMC for self and society. The research presented here focuses on people’s interactions with multiple technologies (artificial intelligence, algorithms, and robots) used within different contexts (home, workplace, education, journalism, and healthcare) from a variety of epistemological and methodological approaches (empirical, rhetorical, and critical/cultural). Overall, Human-Machine Communication provides readers with an understanding of HMC in a way that supports and promotes further scholarly inquiry in a growing area of communication research.
11. Ars Ex Machina: Rethinking Responsibility in the Age of Creative Machines (David J. Gunkel)
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11. Ars Ex Machina: Rethinking Responsibility in the Age of Creative Machines
DAVID J. GUNKEL
In May 2015, National Public Radio (NPR) staged a rather informative competition of (hu)man versus machine. In this 21st century remake of that legendary race between John Henry and steam power, NPR reporter Scott Horsley went up against Automated Insights’s Wordsmith, a natural language generation (NLG) algorithm designed to analyze patterns in big data and turn them into human readable narratives. The rules of the game were simple: “Both contenders waited for Denny’s, the diner company, to come out with an earnings report. Once that was released, the stopwatch started. Both wrote a short radio story and got graded on speed and style” (Smith, 2015). Wordsmith crossed the finish line in just two minutes with an accurate but rather utilitarian composition. Horsley’s submission took longer to write—a full seven minutes—but was judged to be a more stylistic presentation of the data. What this little experiment demonstrated is not what one might expect. It did not show that the machine is somehow better than or even just as good as the human reporter. Instead it revealed how these programs are just good enough to begin seriously challenging human capabilities and displacing this kind of labor. In fact, when Wired magazine asked Kristian Hammond, co-founder of Narrative Science (Automated Insights’s main competitor in the NLG market), to predict the percentage of news articles...
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