Posts under /Matter Category
Back when I was just starting graduate school, I remember already feeling as if I understood the components needed for great scientific research: knowledge of a domain, the ability to implement a system or execute an experiment, and a creative insight about a phenomenon worth studying. While the domain knowledge and ability to execute seemed like pre-requisites for doing science at all, the capacity for creativity seemed to the element that separated a great scientist from the good. Since I felt like I was good at identifying creative research, I hoped that once I immersed myself in academia and started gaining domain knowledge and engineering skill, the creative ideas would come to me. Now, almost a year into my PhD program, I feel like I have learned a great deal, but I am left with the question: Where are all those good ideas?
These slides are from a presentation I gave in Sep Kamvar’s Computational Methods in Data Mining (old website link here). In the presentation, I presented TurKit, a programming framework created by Greg Little and others at MIT that allows for programmatic iteration over tasks in Mechanical Turk. Essentially, that means that instead of the familiar paradigm of sending out a bunch of HITs and waiting for the responses, TurKit will ping AMT for answers and these answers can be used in future HITs. This allows for the use of an “improve and vote” loop, where Turkers continually improve on and validate the work of other Turkers. They had some impressive results in the paper, getting fairly high quality responses to a wide range of tasks (including image labeling, handwriting recognition, and brainstorming) for under $0.50.
The buzz around Q&A startup Quora has been building steadily over the past couple of months. I measure this not only by the number of Follow messages received concerning people randomly sampled from my Facebook connections which are now flooding my inbox, but also by the heated debate that is developing about the site’s usefulness, much of which is chronicled in this TechCrunch article about the “Quora Backlash Backlash”.
This quarter, I’ve been taking two classes: Data Visualization, taught by Jeff Heer (my rotation advisor for this quarter), and Social and Information Network Analysis, taught by Jure Leskovec (my rotation advisor for next quarter). If you’re interested in either of these two topics, follow those links, as an extensive set of course materials (including class projects and suggested readings) have been posted. For a mid-quarter assignment, I worked with Diana MacLean on a project related to visualizing social network patterns. In this project, we chose to examine methods for visualizing cross-posting behaviors of users of MedHelp, an Online Health Community (OHC).
I’m embedding a presentation I gave at a recent “Data Lunch” about how to analyze responses to Likert items. As I am not a stats expert in any respect, I learned a number of things while putting this together – one of the most important is that Likert isn’t actually pronounced “Like-ert”, it’s pronounced “Lick-ert”, which is still tough for me to remember to say. Anyways, hope you enjoy, I’ll include some summary below as well.
I’m currently reading Albert-László Barabási’s second book, Bursts. Though the book is primarily about predicting human behavior in the future, the book is peppered with interesting anecdotes about historical figures (i.e. from the past). One such figure mentioned prominently is Siméon-Denis Poisson, the 19th-century French mathematician. A element which may seem trivial out of context but is rather crucial in the book is Barabási’s description of Poisson’s organizational habits (a sort of 19th-century French GTD):
Yesterday, I attended a pretty interesting PARC Forum where the speakers were three members of the Wikimedia Foundation. For those, that don’t know, Wikipedia is actually part of a larger group of projects (including Wiktionary, Wikiquotes, Wikiversity, etc.) which are all under the umbrella of the Wikimedia foundation, but the talks primarily focused on Wikipedia and how the foundation leverages the community of editors and developers to help build the content and tools that make the site work. PARC will have the video up in a couple days if you want to watch, and you can find the presentation here, but I’m presenting a short summary of some of the interesting tidbits and points here, organized by speaker:
I decided to try a little Mechanical Turk study to see if I could spot some differences between tags generated by experts and those generated by novices. I had each Turker read 1 of 5 web pages (on the topic of “enterprise 2.0 mashups”) and enter 5 tags which they thought would be useful for bookmarking the page (either for themselves or others). I also asked them to rate how familiar they were with the subject matter (“Not at All”, “Slightly Familiar”, “Somewhat Familiar”, and “I am an Expert”)…
I’ve been interested a bit in looking at how to help people find high-quality information on the web – recently, I have been exploring how to help people make better credibility judgments about the information they find. One paper I was reading, “Statement Map: Assisting Information Credibility Analysis by Visualizing Arguments” by Koji Murakami and others at the Nara Institute of Science and Technology in Japan, uses as a motivating example the recent movement against vaccinations for children, specifically the MMR (Measles, Mumps, and Rubella), as the result of fears that these vaccines could cause autism.
So, in a seemingly inevitable, but nonetheless surprising move, Google has purchased Aardvark for $50 million. My last blog post was about Aardvark’s recent paper describing their social search engine, which included allusions to the research paper which was responsible for the creation of Google, so the announcement seems timely.
