I'm not talking about dating websites or love-bots. Sorry to disappoint.
As a computer science major, my background is a little unorthodox. My elementary and middle school (and even high school) years were full of music, art, and writing. In tenth grade, I wanted to be a French teacher; in ninth, a writer for The New Yorker; eighth, a comic book artist. I fantasized about the fruits of my imagination changing the lives of all who saw my work. My novel worldview and acute sense of empathy would take the world by storm, speaking to people as more than the generic second person. My voice would be fresh, and I would be heard.
Creativity has always been rewarded in my family, even at the cost of a higher grade. It was never about the grade itself, but what we learned. Rubrics were a necessary evil, viewed as braces on the brain if followed too closely. It eventually became a game to satisfy all the requirements without addressing them directly. (Read: I did what I wanted and turned it in.) This practice led to some pretty fun essays in elementary school, as I was still perfecting the art of bullshit, garnering mixed reactions from my teachers. Many encouraged what they saw as originality, but there were some who called it "failure to understand the assignment." (Quite frankly, I think how often I talked or read in class influenced such feedback.)
On the other end of the spectrum, there was math. It was clear from early on that no amount of explanation makes a wrong answer right. Thankfully, it made such sense that I never had to fake it. The unfortunate part, however, was that I couldn't play my little game. Precocity was displayed, then, in the form of speed. Fifty multiplication problems and six minutes to do them? How about three minutes and a chance to read? Such arrogance had to be earned, though. Hours of commitment to rote memorization of times tables and mechanization of processes like long division were worth the ability to traverse the room in a game of "Around the World."
For a long time, though, math was just something on the side, to be taken for granted. Sure, I enjoyed it, but the fun part was in figuring out the trick in order to solve my problems quickly enough to have time to draw pictures of anime-style medieval princesses. But I never felt as fulfilled in displaying my mastery of the order of operations as I did in bringing Mary Gold and her compatriots to life in our short story assignments. Certainly there was more to the richness of communication than the uninspired methodology of simplifying expressions.
That all changed when I started studying French. There were the rules of well-formed phrases to follow, very mathematical in nature, and then there was the infinite number of ways to express a thought, once those rules were understood. This new way of thinking intrigued me, and it was very exciting, recognizing that I was learning to think anew, as opposed referencing French keys mapped to English values.
Practicality eventually won out over creative zeal as I tentatively decided to pursue a technical path instead of one in the humanities. It's easier to get by in the world as a techie who can write than a dreamer who can do math. I don't mean to sound like I gave up that which made me happy in favor of security. I would have serious nerd-outs after watching movies like "The Matrix" and "I, Robot," and math actually got fun after geometry. (I remember being in the car with my mom one time and mentally drawing an acceleration graph while watching her speedometer, just for lols.) More importantly, though, I didn't feel that I was forsaking my right brain. Video game companies need programmers who understand the creative process of telling a playable story, and international corporations want translation engines built with acute consciousness of the nuances of the human mind. Right?
Unfortunately, that's not a rhetorical question; I don't have an answer as of yet. Right now, as a computer science major, I'm hoping to focus on computational linguistics as a concentration. I have no experience in the field; I'm going on a hunch I have that it's the kind of thing I'm into. All the interesting philosophical questions of linguistics with the power and profitability of computers? Sure, why not? In the meantime, I've been getting a rather romantic, qualitative understanding of language and thought and computability from Douglas Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid. One chapter in particular captures a train of thought very eloquently communicates my understanding of the key to developing human language technologies.
The pre-chapter dialogue, titled "English French German Suite," contains Lewis Carroll's "Jabberwocky," written in English, French, and German. Now, I don't know German, but in comparing the first two, it is clear that the translations are not word-to-word mappings. (For instance, the original version is told in past tense, while the French version switches to present.) In the accompanying chapter, "Minds and Thoughts," Hofstadter discusses the nuances of translation. Acknowledging the obvious differences in the poem, he argues that a good translation means not just conveying the words understandably, but ensuring that the reader responds in the same way. That means that the literary constructs and devices used in the poem come through in the same way, independent of the language. Bringing in the concept of semantic networks, complex graphs of words and ideas such that the triggering of one node sets off dozens of neighbors, he claims that we appreciate literature in a predictable way due to a commonality in semantic networks among speakers of a particular language. I'll leave the rest of the chapter for anyone who's interested to read.
At the risk of geeking out all alone, doesn't that sound cool? This chapter has been on my mind since I read it last month, and I want an understanding that goes deeper than the high-level smattering of ideas that Hofstadter throws about for all of twenty pages. More than that, though, I want such understanding to be relevant. As magical as computer science has been to me in my first year at CMU, this approach to thinking appeals to me more than anything I have seen thus far. From what I heard today in discussions with some of the computational linguists at work, it's more about the computer system, the algorithms, and the number crunching. That frightens me a bit. I love math, truly, but it is a sad day when raw data obscures the beauty of giving words to the world around us. On the other hand, maybe I don't have to hope that many computational linguists share and appreciate Hofstadter's ideas. Maybe it's time the rubric-averse elementary schooler in me appreciates a few standards to stabilize a somewhat subjective field. All I can do at this point is to learn as much as I can and keep thinking my thoughts. I'm waiting for it all to come together beautifully. I'm working to make all my wildest dreams come true. I'm trying to realize a fantasy. I just hope I'm making the right choice.
As a computer science major, my background is a little unorthodox. My elementary and middle school (and even high school) years were full of music, art, and writing. In tenth grade, I wanted to be a French teacher; in ninth, a writer for The New Yorker; eighth, a comic book artist. I fantasized about the fruits of my imagination changing the lives of all who saw my work. My novel worldview and acute sense of empathy would take the world by storm, speaking to people as more than the generic second person. My voice would be fresh, and I would be heard.
Creativity has always been rewarded in my family, even at the cost of a higher grade. It was never about the grade itself, but what we learned. Rubrics were a necessary evil, viewed as braces on the brain if followed too closely. It eventually became a game to satisfy all the requirements without addressing them directly. (Read: I did what I wanted and turned it in.) This practice led to some pretty fun essays in elementary school, as I was still perfecting the art of bullshit, garnering mixed reactions from my teachers. Many encouraged what they saw as originality, but there were some who called it "failure to understand the assignment." (Quite frankly, I think how often I talked or read in class influenced such feedback.)
On the other end of the spectrum, there was math. It was clear from early on that no amount of explanation makes a wrong answer right. Thankfully, it made such sense that I never had to fake it. The unfortunate part, however, was that I couldn't play my little game. Precocity was displayed, then, in the form of speed. Fifty multiplication problems and six minutes to do them? How about three minutes and a chance to read? Such arrogance had to be earned, though. Hours of commitment to rote memorization of times tables and mechanization of processes like long division were worth the ability to traverse the room in a game of "Around the World."
For a long time, though, math was just something on the side, to be taken for granted. Sure, I enjoyed it, but the fun part was in figuring out the trick in order to solve my problems quickly enough to have time to draw pictures of anime-style medieval princesses. But I never felt as fulfilled in displaying my mastery of the order of operations as I did in bringing Mary Gold and her compatriots to life in our short story assignments. Certainly there was more to the richness of communication than the uninspired methodology of simplifying expressions.
That all changed when I started studying French. There were the rules of well-formed phrases to follow, very mathematical in nature, and then there was the infinite number of ways to express a thought, once those rules were understood. This new way of thinking intrigued me, and it was very exciting, recognizing that I was learning to think anew, as opposed referencing French keys mapped to English values.
Practicality eventually won out over creative zeal as I tentatively decided to pursue a technical path instead of one in the humanities. It's easier to get by in the world as a techie who can write than a dreamer who can do math. I don't mean to sound like I gave up that which made me happy in favor of security. I would have serious nerd-outs after watching movies like "The Matrix" and "I, Robot," and math actually got fun after geometry. (I remember being in the car with my mom one time and mentally drawing an acceleration graph while watching her speedometer, just for lols.) More importantly, though, I didn't feel that I was forsaking my right brain. Video game companies need programmers who understand the creative process of telling a playable story, and international corporations want translation engines built with acute consciousness of the nuances of the human mind. Right?
Unfortunately, that's not a rhetorical question; I don't have an answer as of yet. Right now, as a computer science major, I'm hoping to focus on computational linguistics as a concentration. I have no experience in the field; I'm going on a hunch I have that it's the kind of thing I'm into. All the interesting philosophical questions of linguistics with the power and profitability of computers? Sure, why not? In the meantime, I've been getting a rather romantic, qualitative understanding of language and thought and computability from Douglas Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid. One chapter in particular captures a train of thought very eloquently communicates my understanding of the key to developing human language technologies.
The pre-chapter dialogue, titled "English French German Suite," contains Lewis Carroll's "Jabberwocky," written in English, French, and German. Now, I don't know German, but in comparing the first two, it is clear that the translations are not word-to-word mappings. (For instance, the original version is told in past tense, while the French version switches to present.) In the accompanying chapter, "Minds and Thoughts," Hofstadter discusses the nuances of translation. Acknowledging the obvious differences in the poem, he argues that a good translation means not just conveying the words understandably, but ensuring that the reader responds in the same way. That means that the literary constructs and devices used in the poem come through in the same way, independent of the language. Bringing in the concept of semantic networks, complex graphs of words and ideas such that the triggering of one node sets off dozens of neighbors, he claims that we appreciate literature in a predictable way due to a commonality in semantic networks among speakers of a particular language. I'll leave the rest of the chapter for anyone who's interested to read.
At the risk of geeking out all alone, doesn't that sound cool? This chapter has been on my mind since I read it last month, and I want an understanding that goes deeper than the high-level smattering of ideas that Hofstadter throws about for all of twenty pages. More than that, though, I want such understanding to be relevant. As magical as computer science has been to me in my first year at CMU, this approach to thinking appeals to me more than anything I have seen thus far. From what I heard today in discussions with some of the computational linguists at work, it's more about the computer system, the algorithms, and the number crunching. That frightens me a bit. I love math, truly, but it is a sad day when raw data obscures the beauty of giving words to the world around us. On the other hand, maybe I don't have to hope that many computational linguists share and appreciate Hofstadter's ideas. Maybe it's time the rubric-averse elementary schooler in me appreciates a few standards to stabilize a somewhat subjective field. All I can do at this point is to learn as much as I can and keep thinking my thoughts. I'm waiting for it all to come together beautifully. I'm working to make all my wildest dreams come true. I'm trying to realize a fantasy. I just hope I'm making the right choice.
