Author Archives: Ian Lowrie

I'm currently a doctoral student in the sociocultural program at Rice University, and the editor of Platypus, the CASTAC blog. I work on data science and computational neuroscience in Russia and the United States.

Weekly Round-up | February 10th, 2017

This week’s round-up is a bit more focused, with threads on Mars colonization, automation, and artificial intelligence. As always, we also ask you to write or find great stuff for us to share in next week’s round-up: you can send suggestions, advance-fee scams, or Venmo requests to editor@castac.org. (read more...)

Weekly Round-up | February 3rd, 2017

This week’s round-up brings us stories on climate change, robot overlords, copyright, and video games. As always, we also ask you to keep an eye out for interesting digital tidbits that we should include in next week’s round-up: you can send them, along with any hate mail, compliments, or cat pictures, to editor@castac.org. (read more...)

The More Things Change…

Things are more than a little unsettled, lately. The past ten days since the Inauguration have been a maelstrom of activity, leaving many of us feeling profoundly uncertain about our political, technological, and scholarly futures. Of course, we haven’t been passive. Whatever else it has been, the rise of Trumpism has been an occasion for a great deal of anthropological activity. Anthropologists from around the world have been hard at work attending to the emergence of this phenomenon as both scholars and citizens. If our activities at each of these levels have seemed somewhat disconnected, somewhat divorced from one another, it is perhaps a testament to the profound challenge to our inherited sensibilities, our disciplinary and political commonplaces, represented by the transformations we are witnessing. I think, however, that this is in some respects a constitutive feature of our discipline; anthropology has long been haunted by a tension between its ethical commitment to engagement and its methodological commitment to untimeliness. (read more...)

Weekly Round-up | January 27th, 2017

Stories on data archaeology, global medical infrastructures, mushrooms, and open-access futures weekly round out this week’s weekly round-up of cool stuff from around the web. Remember, if you stumble across or create any blog posts, open access publications, or objets d’internet art that you think might fit here, just shoot a link to editor@castac.org. Help break us out of our habitual media itineraries and parochial corners of the internet! (read more...)

Weekly Round-up | January 20th, 2017

Starting today, we’ll be posting a weekly round-up of cool stuff from around the web that the editorial collective thinks might be interesting to readers of the blog: posts from other blogs, news stories, art objects, internet ephemera. If you stumble across anything that you think might fit here, just shoot a link to editor@castac.org. Help break us out of our habitual media itineraries and parochial corners of the internet! (read more...)

Data: Raw, Cooked, Shared

(Almost) everyone makes data. People browsing the internet or buying stuff generally do so without knowing much about the data that their activities generate, or even knowing that they are doing so. Scientists, though, are supposed to be a little more conscientious about the data they collect, produce, share and borrow (at least in their professional capacities). They’re lately supposed to be, among other things, data managers. This is largely the product of the funding and institutional environments; program officers, science managers, and university administrators increasingly demand rationalized, comprehensive data management plans (DMPs) from researchers. In many cases, such as those from the NSF, these demands include requirements to store data for a specific period of time—often five or ten years beyond completion of the project—and to make such data publicly available. For some scientists, this is just a formalization of existing disciplinary best practices. For many, though, and for anthropologists who study them, these injunctions raise critical epistemological questions about the nature of data, and by implication, of contemporary scientific inquiry—anthropology included. (read more...)

Let’s Think about the University: Anthropology, Data Science, and the Function of Critique

There have been surprisingly few sustained, ethnographic studies of the university that aim to understand it as an institution devoted at once to the production of knowledge and technologies, the circulation of those products, and the cultivation of particular types of subjects. Ethnographers have largely worked at it piecemeal, with admittedly excellent work from both the anthropology of education and of science carving out various areas of inquiry: classrooms, laboratories, admissions offices, student groups, start-up incubators. To my mind, it seems that the lack of a synthetic approach to the knowledge work going on in the university might be due to the disappointing fact that these two camps within anthropology don’t talk to each other very much. In part, this is a result of their different goals, positions within the ecology of anthropological knowledge production, possible sources of research funding, and available career paths both within and without academia; yet, despite the sociological intelligibility of this lack of communication, it remains intellectually unfortunate. As the business of research and education becomes increasingly corporatized, increasingly shaped by wider forms of rationality that rely upon quantification, standardization, and the devolution of responsibility to the individual, it becomes correspondingly urgent to develop a rigorous, holistic understanding of the university as such. This has only been underscored by my fieldwork among Russian data scientists, who are themselves involved in the ongoing reorganization of higher education here. That is to say, the neoliberal university qua institution, with its own internal forms of organization and expertise as well as its place within the broader political economy, deserves to be the object of a newly shared inquiry. The current shape of the university has profound implications for the professional lives of anthropologists of both science and education, and similarly thorough-going epistemological consequences for their ongoing, ultimately complementary attempts to understand how contemporary people make knowledge. I’m working through the latter half of this proposition in my current research project. Data science has emerged as a key site of intervention into the educational system in Russia; elites from both industry and academy are working together to modernize and re-purpose Russia’s formidable pedagogical infrastructure in pure mathematics and theoretical computer science to train a new generation of algorithmists, developers, and programmers in both the practical skills and professional attitudes that they see as necessary for the creation of a truly Russian knowledge economy. The result has been both the creation of a number of hybrid, industrial-academic institutions and wide-ranging modifications to curriculum and requirements at more traditional institutions. These changes are occurring within a broader context of profound reforms to post-graduate education1 and the science system more generally.2 (read more...)

Political Economy and the Internet of Things

According to Cisco, the number of things – smart phones, cars, delivery vehicles, smoke detectors, outflow sensors, electricity meters – connected to the internet surpassed the number of people connected to the internet in 2008. Projections for the coming decade vary, but corporate researchers at firms like Cisco, Intel, IBM and Siemens are betting big on the exponential growth of networked sensors and microcomputing devices. These companies are working in loose concert to shepherd this emergent swarm of networked things into a truly infrastructural data-collecting system. They see in the so-called “Internet of Things” the consummation of promise held forth to the corporate world by big data analytics; comprehensive, actionable, real-time data about production and consumption, allowing for ever more agile and sophisticated extraction of value from human activity. (read more...)