‘Read Later’ tabs: Google Glass, the Secretary Effect & Big Data


Looking to purchase a Google Glass of your own? Better hurry, you only have until January 19th! Google is sending the wearable project underground, in the capable hands of Tony Fadell (former Apple guy + current head of Nest Labs) and will be selling to companies/developers only for app development.   Bought it? If not, guess you will have to wait it out with me for Glass version 2 to roll out later this year.

The Atlantic gives us another reason to detest group projects in ‘Group projects and the Secretary Effect’:

Left to their own devices, kids usually divide the labor for group projects. The way they divide that labor matters, and is the root of a pet theory of mine, based on anecdotes and a little bit of research that goes like this: More often than not, a girl winds up in one particular role every time. The secretary. Maybe her teacher calls it the “recorder” or the “data collector” or the “stenographer.” But whatever it is, she’s writing everything down. She’s the organized one, the one with the good handwriting, the one who cares about actually filling out the worksheet. The ones who get to be creative, who get to goof off and riff ideas and not worry about the form or the specific assignment tasks? They’re mostly boys.


I end up reading and writing a lot about technology during my workday and recently I found myself delving deeper into the syntax and terminology behind the vague mass that is ‘Big Data’. As I tweeted my theories, questions, and sheer delight at unlocking the actual meanings behind the I.T. world-exclusive jargon, a  Computer Science Samaritan realized that I had just dipped my toes in the discipline. They shared this article to help clear up some of the ambiguity:

Nowadays, I think the reason I hear this question so often is that some of the most salient properties of big data, as it’s commonly construed, make people very uncomfortable.

In other words, unlike the data sets arising in physics, the data sets that typically fall under the big data umbrella are about people — their attributes, their preferences, their actions, and their interactions. That is to say, these are social data sets that document people’s behaviors in their everyday lives.

The second reason is highlighted by this quote from Michael Jordan, a machine learning researcher, taken from a recent talk of his:

The issue is not just size—we’ve always had big data sets — the issue is granularity.

In other words, not only do these data sets document social phenomena, they do so at the granularity of individual people and their activities.