In the 1997 essay “Protected Mode,” the late media theorist Friedrich Kittler, with nostalgia for “the good old times” when using computers meant interacting with them in a way that made it impossible to ignore the reality of their basic hardware, expressed his disapproval of the user-friendliness of commercial software. In contrast to the true underlying operations of digital machines themselves, he asserted, commercial software hides from view the reality of computers’ operations determined at the level of material technological frameworks. “The higher and more effortless the programming languages,” he wrote, “the more insurmountable the gap between those languages and a hardware that still continues to do all of the work” (157). The problem with software, for Kittler, is that it seems to put the user in control when, in fact, what it really does is reduce the user’s agency by obscuring the user interface’s basis in hardware. Put in different terms, it performs an illusory reversal of the relationship between infrastructural and superstructural elements. One can only imagine that Kittler would be dismayed by the current state of digital media technology’s development in general and by the trend among technology startup companies toward increased reliance on cloud computing in the form of “infrastructure as a service.” At the same, I think this gestures toward a certain problem in the anthropological study of digital technologies.
In “Coding Places: Software Practice in a South American City” Yuri Takhteyev depicts a group of developers from Rio de Janeiro working on software projects with global aspirations. His ethnography, conducted in the span of three years, provides rich detail and insight into the practice of creating a programming language, Lua, and struggling to form local and global communities. In his narrative, Takhteyev sets off with a task that is particularly akin to anthropological studies of globalization: to specify socioeconomic and political forces shaping localities and creating instances of production and circulation of transnational scope. We asked him a few questions related to the book and his research on the topics of globalization, computing expertise, and politics of information technology. Enjoy!
I’m Beth. I study people who study earthquakes and people who work to minimize the damage that earthquakes cause.
That’s my short introduction; the line I use with nearly everyone to describe my research. I do fieldwork in the offices, conference rooms, labs, and workshops of earthquake-prone Mexico, where cutting-edge research and technical problem solving is happening (not to mention pitched battles over what “cutting edge research and problem solving” could mean in the first place). « Read the rest of this entry »
In the first part of this article, I wrote about how two major events shaped research in self-driving cars: the DARPA Grand Challenges and Google’s Self-driving Car (hereafter: SDC) project. In this post, I will talk about my surprise at the unfulfilled yet pervasive promises of machine learning in SDC research.
Self-driving cars (aka driverless cars or autonomous vehicles) are among the most visible faces of Artificial Intelligence (AI) today. In continuation with Shreeharsh Kelkar’s excellent post on Artificial Intelligence last month, I would like to pick up his lead and complicate yet another story of AI – the story of the relationship between software developers and machine learning algorithms. For this purpose, I will use my ongoing field work among members of an academic research group as an example. The work of this particular research group centers around an experimental vehicle – a self-driving car (hereafter: SDC).
During my field work I experienced a couple of surprises which challenged my earlier assumptions about the research on SDCs. That is, I previously assumed that the many promises invested in machine learning would lead to its extensive use in the experimental vehicles. However, this is not the case. I started to wonder why the researchers refrain from using machine learning even though they are officially members of an AI department. After introducing you to the field of SDC research in this first part, I will provide you with three tentative answers in an upcoming second post.
Ah, the Game Developers Conference (GDC)… I started my field research in 2004 at a relatively small but growing game studio: Vicarious Visions. Since that time I’ve been researching game development and game developers. That’s a long time to study such an amorphous, variable and shifting thing/community/world/culture. I’ve ranged from AAA developers to hobbyists to serious game development teams. I haven’t made it to every GDC in that time; travel has always been highly subject to the aleatory. But I have been watching, listening and taking notes from afar even when I haven’t been there myself. What follows is a meta-note, on my collection of meta-notes, which will make this pretty meta-meta.
If you are lost in the middle of the woods, you have a problem.
Assuming that you’ve ended up in this predicament without any navigational aids or food, you’ll have to start walking. Any direction is better than none: if you stay put, you’ll starve. And, once you pick a direction, you better keep going: if you keep changing your mind, you’ll just go in circles, and you’ll also starve. Your problem, aside from the lack of food and abundance of predators, is in deciding which way to go: if all your options look the same, how are you supposed to decide? « Read the rest of this entry »
A decade ago I did an ethnographic study of the little known, but highly promising, field of quantum information physics. I immersed myself in the work and world of physics: reading the field’s latest papers, attending the important conferences, interviewing the thought leaders, and taking thick notes on everything I saw and heard. After three years, when my notebook and brain were both filled with all cultural data they could carry, I wrote up my results, filed my doctoral dissertation, and sat back to wait for the scientific breakthroughs that I thought were soon coming, for the global information technology reboot that the field was on the verge of creating. I waited …and then I waited a bit more.
This is the second half of my conversation with Dominic Boyer about the emergence of “infrastructure” as both ethnographic focus and analytic within anthropology. You can read the first part of the interview here!
Ian Lowrie: I’d like to circle back to the question of how infrastructure is related to politics and liberalism. There’s a recent article by Kim Fortun calling for a revitalized, engaged anthropology of not just infrastructure, but infrastructural expertise, in the context of precisely the degradation of the most visible aspects of our infrastructure. At the same time, I think we also see strong, robust development of other types of infrastructures. Things like technical arrangements, financial instruments, logistical services, the computational and digital. I wonder if part of what makes the urge to expand the concept of infrastructure to include things other than things like roads and sewers is a political urge.
Dominic Boyer: I think it is, and I think you’re right to point out that the story of infrastructure in the neoliberal heyday is not simply about abandonment. It’s a story of selective investment, and also of abandonment [laughs]. This is also the era in which informatic infrastructures, for example, develop. The Internet is one, but also the specialized information infrastructures that allowed finance to exert global realtime power that far exceeds the capacities of most governments to effectively regulate it. And that becomes a pivotal part of the story of the rebalancing of powers, I think, during the same time period. So the neoliberal era saw some remarkable infrastructural achievements in certain areas, whereas at the same time you might find your roads and your sewers decaying, which is interestingly often-times the focus of infrastructure studies. Most seem focused on what I would describe as basic biopolitical infrastructures and their fragmentation. A lot of research is, more or less latently, interrogating the aftermath of neoliberalism, specifically through the lens of biopolitical infrastructural decay. But you could tell a different story if you looked at different infrastructures. And maybe that’s a story that still needs to be told. « Read the rest of this entry »