AlphaGo’s Deep Play: Technological Breakthrough as Social Drama

Binder, Werner

doi:10.1007/978-3-030-56286-1_6

Cited by 5 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same argument applies to ‘human/machine interfacing’ (Lipp & Dickel, 2022), insofar as its relational genealogy, for it to become operative (e.g., in a technology demonstration), needs to be locally performed and practically sustained (e.g., as an exhibition match). And the argument a fortiori applies to narrative interpretations in terms of ‘social drama’ (Binder, 2021), ‘enchanted determinism’ (Campolo & Crawford, 2020) or ‘technological myths’ (Natale & Ballatore, 2020). 17…”

Section: Discussionmentioning

confidence: 99%

“…For instance, the exhibition match has been invoked as an exemplary case to narrate and reflect upon technological change, as a historical process and/or discursive pattern, rather than to pause on the event’s very occurrence—that is, its staged character and situated performance. Hence, and instead of being described as a technology demonstration, the AlphaGo show has been interpreted as part of a broader ‘social drama’ (Binder, 2021), ‘enchanted determinism’ (Campolo & Crawford, 2020), or ‘shifting narratives’ (Bory, 2019). Often interpretations take a comparative form.…”

Section: Technology Demonstrations: Narrating Change/interfacing Alphagomentioning

confidence: 99%

“…In early March 2016, the exhibition match was livestreamed from Seoul and reportedly watched by over 200 million viewers across Asia, eventuating in 35,000 media reports by March 2017 (Hassabis, 2017). By now, media attention has been complemented with extensive (post-)game commentary, computer Go expertise, and contrasting media analysis, be it by outside observers (e.g., Binder, 2021; Bory, 2019; Curran et al, 2020; Goode, 2018; Mair et al, 2020), the initial commentators (Redmond & Garlock, 2020a, 2020b), or the AlphaGo research team (Silver et al, 2016, 2017). However, despite or precisely because of all the media frenzy and scholarly attention, the exhibition match as a particular kind of technology demonstration—in short, the ‘ AlphaGo show ’ (Redmond & Garlock, 2020a, p. 47)—has not received much scrutiny in science and technology studies (STS).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Interfacing AlphaGo: Embodied play, object agency, and algorithmic drama

Sormani

2023

Soc Stud Sci

View full text Add to dashboard Cite

For decades, playing Go at a professional level has counted among those things that, in Dreyfus’s words, ‘computers still can’t do’. This changed dramatically in early March 2016, at the Four Seasons Hotel in Seoul, South Korea, when AlphaGo, the most sophisticated Go program at the time, beat Lee Sedol, an internationally top-ranked Go professional, by four games to one. A documentary movie has captured and crafted the unfolding drama and, since AlphaGo’s momentous win, the drama has been retold in myriad variations. Yet the exhibition match as a technology demonstration—in short, the ‘ AlphaGo show’—has not received much scrutiny in STS, notwithstanding or precisely because of all the media frenzy, game commentary, and ‘AI’ expertise in its wake. This article therefore revisits the second game’s ‘move 37’, its surprise delivery by AlphaGo on stage, and the subsequent line of commentary by the attending experts, initiated by the news-receipt token ‘ooh’. Drawing upon a reflexive video analysis, the article explicates the Go move’s scenic intelligibility—its embodied delivery as part of the technology demonstration—as the contingent result of intricate ‘human/machine interfacing’. For mainstream media to report on AlphaGo’s ‘superhuman intelligence’, it both relied upon and effaced such interfacing work. In turn, the article describes and discusses how ‘object agency’ and ‘algorithmic drama’ both trade on skillfully embodied play as a pivotal interfacing practice, informing the exhibition match from within its livestream broadcast.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Technology Demonstrations: Narrating Change/interfacing Alphagomentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Interfacing AlphaGo: Embodied play, object agency, and algorithmic drama

Sormani

2023

Soc Stud Sci

View full text Add to dashboard Cite

show abstract

“…Rule-based games not only offer a sweet-spot between formal rules and creative behavior for experimenting on machine learning models (Ensmenger, 2012), but are also dramatic engineering feats that can be exhibited through spectacle and capture the public's imagination in ways that go beyond the mechanization of labor, precisely because the machines are doing something they are not supposed to be doing, or that nobody expected they would be able to do (Binder, 2021). While chess, for example, ultimately proved to be a computationally solvable problem, the fact that a computer beat the best human champion late in the 20 th century does not mean humans have stopped playing chess between each other or against machines.…”

Section: Playing Out Machine Learningmentioning

confidence: 99%

Play it Again

Reigeluth

2023

JHTR

View full text Add to dashboard Cite

Contemporary discourses accompanying the deployment of machine learning tend to fit within a meta-narrative of automation. Whether heralded as inevitable or criticized as reductive, this process of automation tends to be considered as concurrent to technology’s extension in general. By drawing on the social and technical history of machine learning, I would like to suggest that play offers at least one other way of framing machine learning’s development, one that could help foster other expectations and evaluations of machine learning performances. The genealogy of machine learning I will provide draws on certain historical tropes to problematize contemporary debates. I will distinguish two normative paradigms: automation and play. Each one expresses differing, albeit not incompatible values, expectations and objectives when evaluating machine behaviors and their interactions with human ones. Whereas automation pushes us to consider machines as ideally working by themselves, play requires a social and affective engagement whose outcome is always partially unpredictable. It helps account for the relative open-endedness, intractability and recursiveness of machine learning systems embedded in social practices. More specifically, I underline why play provides a sweet spot for thinking about performances such as those we are increasingly seeing in machine learning systems, that combine both rule-following behaviors and forms of improvisation upon those rules. Play includes automaticity as a level of behavior among others. The more general claim I am making is that play gives us a window onto a different history of machine learning and for imagining other forms of social interaction with and through technology.

show abstract

“…[1][2][3][4] The indomitable victory of AlphaGo, the brainchild of Google DeepMind, against the world's reigning Go champion-a game renowned for its astonishing complexity-clearly underscored AI's relentless march toward transcending human computational power. 5 However, the realization of a truly practical AI, seamlessly integrated into human existence, hinges not only on superb computational abilities but also on the development of powerful 'physical embodiments'-to empower AI with robust computational and operational capabilities on a scale ranging from microscopic to macroscopic scales.…”

Section: Introductionmentioning

confidence: 99%