That’s a Wrap!

This past Wednesday, I left my probability final with a feeling of freedom. The following day, I slept in until 11:30 and drifted around for the rest of the day, picking at various tasks in between time-wasting activities. Yesterday, the only two notable activities were talking to Evan on the phone and going to play Friday Night Magic at a shop in San Jose. And today, I’m pretty sure I’ve done nothing.

Thinking about my activities, this might not be so different than a slow weekend during the school year. Thinking about how I’ve felt, it’s been so much better because there’s no pressure to do anything. Instead of the constant need to catch up on reading or start a problem set easy, I can spend an hour or two watching a movie or playing video games and not feel like I’m sacrificing anything. That’s a sign the year is over.

I’ve learned a lot. I’ve learned that there’s actually structure in philosophy papers. I’ve learned enough C++ that I’m no longer bluffing when I help someone on an assignment. I’ve learned how to play racquetball. I’ve learned about the difference between bebop and cool jazz. I’ve learned that polarized sunglasses make the world look funny. I’ve learned that I don’t understand euclidean domains. I’ve learned how dorm events are run.

One of the most important things I’ve learned is what my major is actually about. When asked what symbolic systems is, I still tell people that it’s my way of doing computer science with philosophy and psychology requirements instead of engineering requirements. And in some sense, that’s still true. Over this year, though, I’ve taken 4 philosophy classes (2 logic, 2 not), a modern algebra class, and a computer science theory class. Something I’ve seen through these classes is that there are principles behind how the world works, and there are ways of understanding that, and depending on how you understand it, everything you might think you know might be a horrible lie or simplification. At the end of this quarter, my prof for “Mind, Matter, and Meaning” told us that he decided to go into math and philosophy instead of natural science because he was fascinated by how the worlds work. Knowing how the world works is cool, but in some sense, the laws of physics and biology are arbitrary. It’s fantastic that someone has the answer, but there’s no reason why the world actually works that way.

In that class, we had talked a lot about logically possible and naturally possible worlds, hoping that possibilities and necessities in those worlds would help us understand why our world works like it does. It was quite a throwback to my previous philosophy classes. In my moral philosophy class, we talked about Kant’s Formula of Universal Law, where we imagine world and attempt to come to various types of contradictions. And that could be understood within the framework of modal logic, which we talked about in my 2nd logic class. In that, we looked at the connection between worlds, where we connected truths between various worlds.

It seems that the common theme between all of this is reducing complex systems into an underlying structure that can make things easier to understand. In my first logic class, we talked about how to use propositional and first-order logic. This seemed like the end of the story until the 2nd class, where we reduced these systems to even fewer axioms and showed how the systems were built and proved why they work. Which was the same thing that we did in my CS theory class, covering finite automata and turing machines as simpler forms of what all computers are today. And was the same in that algebra class, where we learned all these wonderful properties of the integers and natural numbers that I had used so much.

So when I think back to last summer, when Kurt Godel and Alan Turing came up in all my reading, that no longer seems surprising, because that’s apparently what I’m interested in. After this quarter, I’ll have to throw in Saul Kripke as another person who’s coming up in all of my classes as being very important to developing and understanding these symbolic systems.

When I came to Stanford, I was fairly certain that I was going to major in symbolic systems, so when people asked me if I knew what I wanted to do, that was a yes. I knew that the set of classes that make up the core looked interesting. After having taken most of the core now, I’m at the point where I think I now know what my major actually is, and why I think the classes are great. That, of course, doesn’t give me any certainty on what I want to do after this, but at least I’ll know what it is that I know.

3 thoughts on “That’s a Wrap!”

  1. I’m glad that you’ve found your way in symbolic systems.

    I remember when we talked about your college options — 2 years ago? — I found an interesting challenge in trying to explain research areas and courses in which I’m immersed (at the level of graduate studies) to a high school student who is not yet prepared to discern all of the shades of grey.

    You’ve moved farther up the theoretical scale than I had in my undergraduate studies, with all of the courses on philosophy. I’ve picked up the philosophy along the way in an practical way, i.e. I needed to figure out the philosophy when science failed.

    I presume that you now know about the field sufficiently that you can figure out what you’ll like in the next few years of studies. (After that, there’s always the possibility of graduate school!)

  2. So, are you not worried about getting a job with a practical degree?

    I mean no offense when I ask this, but I am curious as to what jobs you can get with a degree in symbolic systems?

  3. Not at all. My backup plan is to be a software engineer. The SymSys core actually requires 4 of the 6 classes in the computer science core (the rest of which I’ll finish out of interest), and a concentration in AI requires around 5 upper-level AI courses listed in the cs department. So as far as programming goes, I think I’ll come out about as solid as a CS major.

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