Tag: Google

Note from the Field: Charting Territories without Maps

The Lao People’s Democratic Republic (Laos) does not have postal codes, street addresses, or mail delivery. Streets rarely have codified names. Since I started doing fieldwork in Laos in 2012, I have been fascinated by the ingenious maps that people make to navigate a country without codes. Every day, people make-do by making their own maps. Map making technologies (like GPS, digital mapping software, graph paper) are also important tools for my informants in the bomb clearance sector, where I do much of my fieldwork. Here, as well, people learn to make do by making their own maps. The present writing, however, is the first time that I have consciously tried to chart the source of my fascination. (read more...)

What Drives Research in Self-driving Cars? (Part 2: Surprisingly not Machine Learning)

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. (read more...)

What Drives Research in Self-driving Cars? (Part 1: Two Major Events)

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. (read more...)

Killing Comments: Back to the Future with Web 1.0

Two new strategies for dealing with online comments have set the interwebs a-buzzing. The first is the decision by Popular Science to shut off comments on articles on their website, arguing that they are bad for science. The second is Google’s announcement that it will significantly modify YouTube’s comment system by featuring more “relevant” comments up front, and providing new tools to moderate comments. While some people expect these decisions to usher in a new public sphere, others see them as harbingers of a return to the age of “Web 1.0” (if you’ll forgive that term), which still holds the connotation of highly-restricted forms of online participation. According to Popular Science, although many insightful comments are posted, studies show that people experience more negativity toward certain announcements about science after seeing rude—even if substantively unrelated—attacks. In fact, “even a fractious minority wields enough power to skew a reader’s perception of (read more...)