encoded notions

While doing my Mechanics homework set tonight, I thought back to the last problem set and had a revelation.

When I look at a problem and I think, “Gee that’s an integral” it makes me happy (this set). When I look at a problem and I think “Gee that’s a differential equation” it makes me sad (the last set).

Professor Franklin told us something in the Fall Theory class about most physicists: they fall into two categories.

1. They see everything as an integral.
2. They see everything as a differential equation.

I’m definitely the former.

The word, in the end, is the only system of encoding thoughts.
Neal Stephenson

Spoken and written language are constrained by the one-dimensionality of the construct of a sentence. Our mouths can make but one sound at a time, so our writings and our minds feel constrained to this physical limitation.

There are times when we have moments of parallelity, where the mind processes more than one item in the active foreground. Most times this does not happen with words, but concepts, abstractions, and calculations. Language trains the mind to flow like water from a pipe instead of like ripples on a pond. Words help to shape the mind, but they do not govern it completely.

There is a construct which can be used to route around this linearity of language: the metaphor. Metaphors are speech tokens and phrases that represent more than what is uttered. They carry weight, associations, connections, memories, subtexts, and implications. They are linked to a collective body of common knowledge. In this regard, they are multi-dimensional words. Metaphors are as close to a mind-to-mind connection as words can allow.

In Computer Science there is a distinction between varying types of programming languages: low-level vs. high-level languages.

LLL, like assembler and machine language, translate the symbols and tokens of the code directly into CPU instructions. There is no ambiguity, since to program in LLL require the programmer to verbosely detail the operation of the program being written. Because of this severe need for detail, these types of programs are difficult and time-consuming to create. In contrast, HLL abstract away much of the nitty-gritty need for detail, and provide a more powerful interface to the system. Tokens and syntax in the code allow for programs to be written with far less code, and less time.

Words and metaphors are like this. Words are the atomic units that lie at the heart of spoken and written communication. Given enough words, one can convey anything that their mind can construct, though sometimes this expression is frustrating and difficult to perform. In the translation from thoughts into words, there is a loss of precision and meaning that cannot be avoided.

Metaphors are more forgiving, allowing you to transmit volumes of information without condensing it into the forced form of sentences. With metaphors, however, one must let go of the thought that they have control of how the metaphors are interpreted by the recipient. The listener must derive the meaning of your words for themselves.

To gain meaning and subtext, one must relinquish specificity.

In the end, however, a stream of methphors is still composed of words, and retains aspects of its one-dimensionality.

A true system of mind-to-mind communication would require that the brain be allowed to wander and express itself in other media besides aurally, like the bubble from Signal to Noise.

I often stop to think about why I am a physics major. The obvious answer someone might offer is that I enjoy physics. They would be wrong.

I don’t think it is ever wise to study something professionally that you enjoy, because your passion for it will tend to dwindle as your list of reasons to despise it grows. University learning does teach you how to think outside of the box, but within the confines of a specific track of study, the trend is to lose originality and become cookie-cutter students.

I don’t want that to happen to me.

Don’t get me wrong, I don’t hate physics—I chose it because it was the least painful area of science that I could accept for 4-5 years of study. Biology, Chemistry, Mathematics, and Computer Science are some of the other areas that I could have turned to, but I have my reasons.

Biology is icky. It’s to closely bound to the world around us, with a lot of What?s and not enough Why?s. The labs are wet, and the classes are nothing but memorization and nomenclature.

Chemistry is not so icky, but it exists in a grey area between Biology and Physics. You can get a sense of something much more interesting behind the scenes controlling the reactions and interactions between the substances. You can start to see inklings of the answers to the Why?s of Biology, but that’s as far as it goes; there’s still memorization and nomenclature, but on the plus side, there are quantitative problems to be solved as well.

Mathematics is a fine subject: very useful across all of the sciences. When it isn’t tied to any one particular system of reality, it feels a bit too abstract. Mathematicians get too worked-up over proofs, validity, and truth (I realize that this is the point behind the subject). It feels too unstable—there’s no room for a slight margin of error in a proof, or else the entire glass house shatters.

Computer Science feels like experimental Mathematics, if that’s even possible. Algorithms are developed and studied. Computer Science is the study of the time evolution of information. I am very intrigued with this type of work, but majoring in this area seems to lead in one of three directions:

1. Teaching Computer Science.
2. Becoming a code-monkey (someone who codes for a living, but hardly ever gets any input into the design process, which is the most interesting part of programming).
3. Writing some insanely cool program and then reaping fame and fortune off of it for the rest of your life—which is highly improbable.

Physics feels like the top of the food-chain in Science. It rests atop of Chemistry and is best friends with Mathematics and Computer Science. With physics, you can feel bits of the answers to all types of Why?s. You have to understand programming well enough to develop simulations of real-world systems, and then have the mathematical background to understand the numerical interpretation of the resulting data. Earning a Physics degree is synonymous with earning a degree in Problem Solving. After graduating, you can downsample yourself to fit well into a different area of study, such as engineering.

Essentially, by becoming a physics major, I’ve delayed the important decision of “What do I want to be when I grow up?” until after I graduate. It also gives me time to investigate some aspects of a topic that I’m only beginning to understand. It’s a realm of study where I feel comfortable and almost happy—like a child at play. It lies at the root of the following topics: complexity theory, chaos, interacting systems, dynamic systems, information theory, AI, language, and cryptography:

Patterns

More on this later.