I wrote this email two nights ago, a little after midnight:
woke up ~ 5 am today. it was early and the sun had not risen. i contemplated going back to sleep and worked on my urop instead. then i took a shower, got an early lunch, and took the 11am bus to wellesley. i caught up on my email and read some articles and flipped through the first chapter of the python natural language processing textbook. then i hung out with toons, ate dinner with toons, sang a concert with toons, and ate ice cream with toons. it was a rare occasion where i didn’t feel drained by social interaction.
today i feel like my life is worthwhile; like i’m doing good work that i can be proud of, and like i’m contributing a positive thing to people’s lives. this is a good feeling and i’d like it to stick around.
another ending begins. people shifting, people leaving, commemorating nostalgia. i saw a tumblr quote today: “if you want to learn what someone fears losing, watch what they photograph”
The concert we sang was a farewell concert for our graduating seniors. Last night we celebrated a good friend’s twenty-first birthday. Today I’m reading danah boyd’s reflection on John Perry Barlow’s 1996 Declaration of the Independence of Cyberspace. I am also thinking about this week’s topic in CMS.701, echo chambers and filter bubbles. Wikipedia says:
A filter bubble is a result of a personalized search in which a website algorithm selectively guesses what information a user would like to see based on information about the user (such as location, past click behavior and search history) and, as a result, users become separated from information that disagrees with their viewpoints, effectively isolating them in their own cultural or ideological bubbles.
Wikipedia took me to the article on collaborative filtering, a term used to describe a class of recommender systems (think: “people similar to you also liked these movies…”). There, I learned about gray sheep:
Gray sheep refers to the users whose opinions do not consistently agree or disagree with any group of people and thus do not benefit from collaborative filtering. Black sheep are the opposite group whose idiosyncratic tastes make recommendations nearly impossible. Although this is a failure of the recommender system, non-electronic recommenders also have great problems in these cases, so black sheep is an acceptable failure.
Now I am reading Natasha’s post on spring and resilience:
A spring can be pressed or pulled but returns to its former shape when released. I do not want to return to my former shape. I have changed in the past four years, mostly for the better, and I’m a little afraid to leave. I know I won’t un-grow but I’m still apprehensive about leaving this place that has changed me so much. I’m not attached to the place, but I’m attached to the people, and the intellectual energy, and the endless spring of opportunity. There is also something about being in school, where all your work is on your own ‘becoming,’ that I think I’ll miss. I am resolved to enjoy it as much as I can this last month. I am so grateful for all of this.
I’d like to think MIT makes us into gray sheep (or at least somewhat grayer sheep). For sure it changes us in complicated and difficult-to-explain ways. During CPW, many visiting prefrosh asked me questions about what I liked and disliked about MIT; what majors and opportunities are offered; what I would have done differently; whether I think I made the right choice to come here. I, like many of my friends, struggled to distill our experiences into helpful words of wisdom.
Now those friends, newly gray, are turning twenty-one, they are graduating, they are singing their last songs with us. I have a strange nostalgia for the future. It is impossible to explain.