encoded notions

(After watching Bruce Sterling talk about tags and “theory objects” and after attending a presentation about the history of symbols last year, I penned parts of this in a notebook. I recently stumbled across my incomplete essay and fleshed it out more fully.)

What is a word but a collection of symbols?

The implementation of those symbols doesn’t matter as much as the abstraction itself. It’s a fundamental issue about identity. The ‘wordness’ of a word isn’t in its construction, but denoted more by its meaning. The cyclical synaptic impulses the abstraction evokes in the brain of those who hear, read, speak, or think it.

A set of words and grammar and syntax forms a language. A language is inherently a social object; it has no utility if it has only one speaker. Language is a medium for information transfer from one person to another. The speakers of a language are what give context to its words. After the writers leave their work, the only remnant of meaning left for words is encoded in the many contexts in which they are found.

All a reader needs to read a piece of writing is a basic subset of knowledge about the language used to write the piece. With a proper subset, a reader can bootstrap the rest of the abstractions in their minds by the interplay of the words they know, the words they don’t know, and the context that binds them together.

But what happens when a reader lacks any subset at all? Does the sentence still have meaning? Is a sentence only useful if someone is around that can read it? In such a situation, the sentence may be neutered, but it will still retain the contextual information that relates the words to each other.

This is the kind of situation that arises when you think about writing programs that read, write, or translate human languages. A program doesn’t have the same sort of general abstraction manipulating faculties that come with the average human brain. If you give a computer several thousand pictures of cats, describe the behavior of cats, and give it descriptions of the structure of cats will it know what a cat is? What differentiates pure information from knowledge?

When you think about learning a foreign language by immersion, nouns are by far the easiest thing to learn. If someone points to a cat and says “chat” and you can tell that perhaps chat is the french word for cat. Verbs are slightly harder because there is a temporal element to them. If someone jumps once, stops, and says “sautez” you might be able to understand that jumping is related to “sautez” and that they expect you to jump in response. If someone points at a stationary cat and says “le chat respire” do they mean that the cat is living? sitting? dying? breathing? It only spirals out of control from here.

It would seem that using machines that cannot know the true meaning of any word to translate languages would be impossible if they rely solely on context, but this is not the case. Lacking any other information, a word is approximately defined by the consensus of its usage. Statistically speaking, if you examine a fairly large subset of a word’s entire usage history there must be some value to all of that context.

Metaphors are also to be found hiding in the context between words. Metaphor is a way to toy with the malleable nature of language. It’s a way to activate a complex thought in a reader where there is no word linked to that thought. A metaphor is the swiss-army-knife-and-duct-tape-MacGyver solution to a lack of lexical materials. Metaphors can offer closer insight into the real meaning of words than the cold phrases found in dictionaries. Captured in a metaphor is a whisper of the organic nature of the brain.

Thoughts pushed through a fine semantic mesh produce sentences. Sentences are woven with a language’s abundant supply of words. Words are symbols that represent something intangible and yet exist as objects themselves.

If every human lived a solitary existence, without the need for communication, what would their internal dialogue consist of? What would color the form of their thoughts?

I have come to the conclusion that I am not a physics person.

For a long while, I thought that I really was a physics person, because I found the subject interesting. It offered me something to chase after: a solution for how the world works.

I worked my way through the various genres of physics: Classical Mechanics, Quantum Mechanics, Electromagnetism, and others. Each one shed more light on bits and pieces of how the universe evolves from one moment to the next. I found the most truth in Quantum Mechanics. From that I learned that the universe is governed by probabilities and micro-fluctuations. Things are here and they are not. Light is both a particle and a probability wave. Most of everything is made of vast sums of nothingness. In a single word, Quantum Mechanics was cool.

I went on to study the same courses all over again in graduate school, but from a more advanced perspective. In the first year I finished the four core courses that form the foundation upon which the rest of my graduate career would rest, but something felt out of balance. There was a vacancy in me and I was ignoring something that I could not immediately name. Sure the classes seemed interesting, but there was a growing sense of disconnect between what I was learning and how I saw it connect to my reality.

I think that vacancy was due to my not co-enrolling in computer science courses.

I spent many elective credit-hours as an undergraduate taking various CS courses “for fun”. I can recall being delighted to learn many of the things I was taught in those classes. At the time I even wrote something that made reference to this physics/CS dichotomy:

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 look at that rambling now and see that I was desperately trying to justify why I was a physics major and not studying computer science officially.

Just because you’re good at something doesn’t mean that you should do it for the rest of your life.

I was an excellent physics student. I understood the explanations of very challenging physics concepts and mental models. It wasn’t too terribly taxing for me and I didn’t have to study much above and beyond the assigned problem sets.

I don’t know when it happened or what directly caused it, but somewhere in that fuzzy temporal space between my undergraduate and graduate career I passed a break-point. The draw that physics had once held for me in the what-else-can-I-learn-about-the-world department started to decay. It’s true that the more you know the more you realize how little you actually know. Subconsciously I was aware of the fact that I was not going to personally unravel all of the secrets of the universe like my optimistic high-school self had wanted. After that idea fell away, it was like tearing off a warm blanket in the dead of winter—eventually you start to notice just how damn cold it is.

I’m a computer scientist pretending to be a physicist. I don’t follow the physics community. I get bored at physics colloquia. I put off physics homework and don’t enjoy it when I finally get around to slogging through it. I didn’t join any physics communities. I’ve only read about three published physics papers. In contrast, I do follow the tech community and I know some of the names of the Important People in it. I can program in about a dozen different programming languages. I’ve coded lots of programs for personal use and at least two for other people to use. I’ve read several published computer science papers for fun. Plus, I even attended a geek conference in some small capacity.

Two years ago I decided to put off answering a question:

Fresh on the horizon: another 4-6 years of delaying the universal question from childhood: “What do you want to do when you grow up?”

I am through dodging this question. When I grow up I want to program computer systems. I want to solve complex problems by building virtual tools. I want to tame vast oceans of data with deft hands and elegant solutions.

I also want to prepare myself for “The Coming Age of Magic”. Computer Science is the modern alchemy. In the past, alchemy was the art and skill of being able to change something common into something special [def]. These days computers are everywhere, and only a programmer can make them work wonders.

To further these newly stated goals, I have transferred from the PhD physics program to the Masters computer science program at the same school. The physical transition may be trivial, but the mental transition is very strange to make. I have always viewed CS classes as a sort of naughty indulgence, as some men might similarly refer to the catalogs they receive that reveal the secrets of a certain Ms. Victoria. I’ve known about this academic transition since mid-November, but it has taken me almost two full months to invert the contents of my brain, relegating physics to the background for a change and dragging my hackish talents to the fore. In a way, it’s like telling a 6 year-old that they can get paid to eat ice cream all day long. Something about that seems…unreal.

Despite some atypical wintry weather causing delays, the new semester will begin for me very soon. Hopefully that vacancy I felt with physics will fade into the past.

Anyway, this little tale of woe is all just a roundabout way of getting to my Laws of Computational Metaphysics. I used to have one, now I have two. I’m sure someone has stated these laws before, but here’s my formulation:

1. Information that resides only on a single hard drive doesn’t exist. This one is the most important, since this one bites both geeks and non-computer geeks all the time. (Computer geeks: raise your hand if you’re older than 22 and you’ve never lost data.) Among non-computer geeks, only very very very smart people like my kid sister and my mother can be made to understand this problem. So for everyone else my default advice is not, “Get yourself a good backup system,” but, “Don’t store anything important on the computer, ever.”
2. Permalinks that contain an anchor don’t exist. Law #2 has a narrower scope, but I think that amongst the web nerd set, it’s underappreciated.

Source: Laws of Computational Metaphysics (goer.org)