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Surveillance of human subjects is how data-intensive companies obtain much of their data, yet surveillance increasingly meets with social and regulatory resistance. Data-intensive companies are thus seeking other ways to meet their data needs. This article explores one of these: the creation of synthetic data, or data produced artificially as an alternative to real-world data. I show that capital is already heavily invested in synthetic data. I argue that its appeal goes beyond circumventing surveillance to accord with a structural tendency within capitalism toward the autonomization of the circuit of capital. By severing data from human subjectivity, synthetic data contributes to the automation of the production of automation technologies like machine learning. A shift from surveillance to synthesis, I argue, has epistemological, ontological, and political economic consequences for a society increasingly structured around data-intensive capital. [ABSTRACT FROM AUTHOR]

As a nascent field within the academy, the contours, attributes, and bounties of data science are still indeterminate and contested. We studied how participants in an initiative to establish data science at a large American research university defined data science and articulated their relationships to the field. We discuss two contrasting visions for data science among our research participants. One vision is a transdisciplinary view portraying data science as a phenomenon with transcendent, appropriative, and impositional qualities that sits apart from academic domains. Another view of data science—one that was far more prevalent among our research subjects—casts data science as grounded, relational, and adaptive, emerging from crosspollination of numerous academic domains. We argue that this latter formulation represents a more quotidian reality of data science and positions the field as an extradiscipline, defined as a field that exists to facilitate the exchange of knowledge, skills, tools, and methods from an indeterminate and fluctuating set of disciplinary perspectives while conserving the boundaries of those disciplines. We argue that the dueling transdisciplinary and extradisciplinary visions for data science have important implications for how the field will mature, and that the extradiscipline concept opens novel directions for studying academic knowledge production in STS, contributing additional precision to the literature on disciplinarity and its permutations. [ABSTRACT FROM AUTHOR]

The thriving contemporary form of artificial intelligence (AI) called machine learning is often represented sensationally in popular media as a semi-mystical technology. Machine learning systems are frequently ascribed anthropomorphic capacities for learning, emoting and reasoning which, it is suggested, might lead to the alleviation of humanity's woes. One critical reaction to such sensational proclamations has been to focus on the mundane reality of contemporary machine learning as mere inductive prediction based on statistical generalizations, albeit with surprisingly powerful abilities (Pasquinelli 2017). While the deflationist reaction is a necessary reply to sensationalist agitation, adequate comprehension of modern AI cannot be achieved while neglecting its material and social context. One does not have to subscribe wholeheartedly to the social construction of technology thesis1 to allow that the development and evolution of technologies are influenced by social factors. For AI, the most important aspect of the current social context is arguably capital, which increasingly dominates AI research and production. One former computer science professor describes a "giant sucking sound of [AI] academics going into industry" (Metz 2017). This paper introduces capital's theory of AI as utility and initiates a discussion on its social consequences. First, I discuss utilities and their infrastructures and introduce a few critical thoughts on the topic. Second, I situate modern AI by way of a brief history. Third, I detail capital's view of AI as a utility and the technical details underpinning it. Fourth, I sketch how AI as a utility frames a social problematic beyond the important issues of algorithmic bias and the automation of work. I do so by extrapolating from one consequence of AI as a utility which multiple capitalist firms predict: the curation of human subjectivities. [ABSTRACT FROM AUTHOR]

There exists a real dearth of literature available to Anglophones dealing with philosophical connections between transhumanism and Marxism. This is surprising, given the existence of works on just this relation in the other major European languages and the fact that 47per cent of people surveyed in the 2007 Interests and Beliefs Survey of the Members of the World Transhumanist Association identified as "left,"though not strictly Marxist(Hughes 2008). Rather than seeking to explain this dearth here, I aim to contribute to its being filled in by identifying three fundamental areas of similarity between transhumanism and Marxism. These are: the importance of material conditions and particularly, technological advancement, for revolution;conceptions of human nature;and conceptions of nature in general. While it is true that both Marxism and (especially) transhumanism are broad fields that encompass diverse positions, even working with somewhat generalized characterizations of the two reveals interesting parallels and dissimilarities fruitful for future work. This comparison also shows that transhumanism and Marxism can learn important lessons from one anotherthat are complementary to their respective projects. I suggest that Marxists can learn from transhumanists two lessons: that some "natural" forces may become reified forces and the extent to which the productive apparatus is now relevant to revolution. Transhumanists, on the other hand, can learn from Marxist theory the essentially social nature of the human being and the ramifications this has for the transformation of the human condition and for the forms of social organization compatible with transhumanist aims. Transhumanists can also benefit from considering the relevance of Marx's theory of alienation to their goals of technological advancement. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor commenting on an article related to violence against environmental health professionals.