Regex and the Visual Context of Content

In my attempts at dinking around with regular expressions this week, I decided to use the Harry Potter books as my source text. Without any potential spoiler given away, the person “R. A. B.” becomes an important mysterious character at the end of book six. In attempts to see if Rowling had already mentioned this character, I did an analysis of each book looking for three words side by side, first starting with R, then A, then B. These were my results and my code.

I also thought it would be fun to grab all the dialog from a book, so I did that with the fourth book in the series. Code and Results.

In the reading for this week, I was reminded of my days in graphic design school and all the emphasis and important typography plays into the interpretation and desire of audiences to read the text. Its funny actually, my mind is constantly jumping back and forth over these issues.

So often I see text scandalized for the sake of a “wow” factor. It was my job for many years, and I begin to crave nothing more than the raw data… the raw strings. Give me the meat. If I like the taste by itself, then perhaps its worth my attention in the oversaturated media world we are in. I began designing websites with little to no visual wow factor, and information and content design became my primary ways of “designing” a set of information. Put the information in the right place, make it easy for people to get to it. Its one step away from the “Information wants to be Free” idea. Its the notion that I will put it in a zoo to make it appear its free… in its contained space.

But there’s no doubt that certain pieces of text need visual representation as a priority of their life in the public view. Darick Chamberlin’s Cigarette Boy, the last example in our reading, is a perfect example. The information itself needs an appropriate wrapper, and was successfully delivered in this piece.

You know, just like everything, its a balance. Its a lot like cooking steak. You can spend all day soaking a steak in amazing spices, and sprinkle and beat in others while its on the grill, but its still going to taste sub-par if its a bad piece of steak to begin with. You can season grissle to perfection, it just smacks the person eating it in the face, one way or another.

Start with a nice, tender, lean piece of steak (content), and the cooking will take care of itself.

Rethinking Algorithms

Throughout the readings this week in Programming A-Z, I’m reminded of some of my winter break reading. Specifically, the distinction between an alrorithm and a heuristic. In my attempt to understand some of Mac Low’s work, I began to see the distinction between input and output.

Input is an algorithm. People may optimize it, but it is a clear and consice problem which an algorithm can get right every time. Read the file size, allocate space, copy that information into that space, and package that space in a way that is easily useable.

Output on the other hand, I find to be more heuristical in nature. What we as programmers, and/or the computers we use decide to do with the text is not steadfast and the same every time. What makes programming valuable is the ability to do tasks that are outside of time and space for a normal human being, and furthermore, the ability to do something we as humans would not be able to do without it. Approaching these challenges as a heuristic allows us the freedom to know we aren’t creating a perfect system, but rather a nuanced system that will render interesting and thought provoking results.

As for this week’s exploration of text, I chose to make a simple aggregator that collects all of the words from an input source of a certain size, and outputs that for interpretation. The results are a bit quirky, but bring up interesting issues. The bigger the word you search for, the more often that word is of higher vocabulary, bringing class systems to mind as well as other nuances. The smaller the text, the more that remains, therefore arguably, the more understandable the text is.

Source is here.
Example with three letter words aggregated here.

New Orleans - Initial Thoughts

Its been more than four months after hurricane katrina. Its a wonderfully haunting, strange, and difficult place to be right now, no matter who you are.

My initial thoughts are scattered: My first view while flying into the city, is that of an extremely long bridge that runs across the lake north of New Orleans (the lake which caused most of the damage after levees failed). I can only imagine how insane it would be to be in the middle of that bridge as winds picked up, as water levels raised. It was a strange introduction into the atmosphere water plays in this city.

In terms of water, New Orleans is like Paris and Chicago intermixed. The Mississippi weaves through one side of the city, on the southside. The lake creates the physical boundary to the north. Industrial canals weave into the city from the lake at random intervals.

As I flew over, blue splotches littered the periphery, and soon I was able to focus in on the tarps that lined seemingly every other building on every street of the city.

On the ground, the view is quite a bit more fantastic in a humbling, destructive sense of the word. Multiple communities have been struck differently, but similarities of iconography remain. For one, in water hit areas, every building contains “ring around the tub” marks, in some communities up to eight feet high, where the water remained stagnant for days and days after the storm. Cars are stacked on top of fences or lay uneasily against trees depending on circumstances, each of them marked with a foggy complexion and in certain cases showing signs of vandalism and/or dismantling.

Each building also bears another mark, of human origin. Spray painted symbols mark each and every building in effected areas, giving the date seach and rescue was able to check the home, how many bodies were found dead in the resident, how many animals were found, as well as additional information needed quickly by helping hands directly after the storm. The woman driving us through the community she lived in months earlier points to a home, mentions that someone had died in that home who was of note in the community. I can barely make out the rescue information on the front door of the home, as I realize they’ve attempted to remove the tattoo that had served as a reminder of their loss.

Tattoos remain everywhere reminding everyone of the loss. In some of the areas hit worst by the breaking levees, entire homes are uprooted and cars appear to be climbing trees, in an awesome spectacle of the power of water.

Most of the day was spent with a man named Bob, and his small one story home in a parish community on the north side of New Orleans. His home is in between two burst levees, but was put in harm primarily through one of the industrial canals which resides three or so blocks east of his property. Bob stayed in his home during the hurricane, and when noticing water entering the home, moved up into the attic where he stayed three days until finally rescued. His story only begins after being found in his home, but that story is for another day. Bob is a coach, or was, at a local school in his neighborhood, and spends his off months as a dance instructor on a cruiseline ship. He seems rather at ease, considering the state of his home. Currently, its a skeleton of a home, as three quarters of the sheetrock have been pulled off the walls and lay in a large heap outside on the street, along with a waterlogged couch and matress we deposited there as well. Bob, like many other residents in neighborhoods near levees, really doesn’t know what to do with his home. Two significant problems are holding back many home owners over a large percentage of the city.

1. Power
There’s no power.

Over four months after the storm, and power is still out. Much of the sub-stations that route electricity into these areas were owned by small power companies, and went backrupt after the storm. The deregulation of power comes with consequences, as local coffers are tapped dry or improperly allocated to fund this problem, and the federal government (to this point) is not going to step in.

As we explored the city this evening, we found ourself in the Ninth Ward, a sub-community on the northeast side of the city. This residential area is completely powerless, and left me almost speechless as we drove through. It was as if I had entered a strange post-apocolyptic film, but was still watch from behind a fast dollying camera. Randomly mixed within a mass of deserted homes, one would be lit with laterns or generator-powered string lights. One family played cards out in the high 60 degree evening as we rolled past. If seeing entire sub-developments destroyed by water damage doesn’t attack the senses in the daytime, perhaps this is just the nightime tour you need. It did the trick for me.

2. Rebuilding the Levees
Are the levees going to be fixed and properly upgraded to prevent such problems in the future? No one in these communities really wants to start rebuilding until they know, which is most likely one of the key reasons the federal government won’t step in to help restore power to these communities (although speculation on my part). Its a real issue. Many people have left and have given up on their properties, and many more are soon to follow unless some big decisions are made.

Its a sad state of affairs, and a long road ahead, but in our expedition through the Ninth Ward this evening, we finally found the destination we had been seeking. Every day, under a small, homemade metal dome structure able to seat around 400, a small group of men and women living in next door tents serves meals to anyone hungry: home owners, volunteers, etc, they are all welcome. As we sat with our meals, I felt the simple sense of community I had been hoping to see. People are laughing, talking about plumbing, and smacking their food while chatting with their friends and fellow man.

A volunteer pulls out a fiddle, which another pulls his digeredoo out from a corner and plays along (kind of). Its an atmosphere of rest, warmth, and a little despair with hope intermixed in strange increments. Of course, hippies are there (as the easiest way to describe them), mostly as part of the volunteer serving team, but many different types of people sit chewing in the tent, and I felt a little envious of their situation with its simplicity and overt raw humanity. No computers, no silly designing or conceptualizing, just people trying to make it and get back to normal. I’m strangely drawn to the moments like these… the snow days where you spend the entire day with a friend in a snow-ready vehicle, helping others out of ditches and grabbing saving your friends from being stranded at home watching soap operas. This situation here feels like a exponentially compounded version of that same day, stretched out over years and years of unrest and toil. I find myself laughing at how silly and naive I can be, and how much I’m glad that it drives my overall passion for the things I pursue.

Dynamism in Heuristics

Finally read through Pattern in the Stone, by W. Daniel Hillis. Great read for anyone curious in the fundamentals behind computing. When I say fundamentals, I am not referring to how to setup your new printer, or fix that annoying blue screen of death error, but understanding the mystery behind a box living with you, helping you do things that would be otherwise impossible.

One aspect of this book that was truly informative, was Hillis’ look into algorithms vs. heuristics. In the short time I’ve actually begun feeling comfortable with programming, I’ve always explored problems through a “design an algorithm” approach. I’m just beginning to realize, my approach was only allowing me to find half of the solutions to the problem through my lack of a definition of algorithm. It turns out, an algorithm is a method for solving a problem 100% of the time… no errors, no bad judgement. Its a hard and fast way to solve something, and it usually emplores some set of logic for a pragmatic result (if this is bigger than that, do something, etc). In comparison, heuristics are different and require a different way of thinking about the problem. Rather than searching for one undeniable truth or solution to a problem, heuristics are methods of discovery similar to scouts in war. They are sent out, in a quazi-random fashion to explore the landscape, and return with any information they were able to find. This allows random samples of data to be used in comparison, rather than exhaustive research.

Exploring heuristics in networks has potential benefits, especially in the creation of nodes. Over and over, I find myself realizing that the problems I wish to tackle in building networks revolves around the dreaded N*N equation. As a network scales, it becomes ridiculously hard to control, and even harder to compute. Hillis mentions the need for heuristics while describing real world problems that have a hard time finding algorithms. The famous example in this case being the “Traveling Salesman Problem”. Help the salesman find the shortest route from city to city, and you have the N*N conundrum. For every city you add, you add that many more possible combinations of complexity over which is the fastest. Below are some links to this problem, which is one of the “holy grails” in computer science. I would love to explore potential augmentation to nodes in a network, if they are given certain heuristical analysis to perform in and above their normal nodal tasks (sending and receiving). Would this “random samplings” give certain nodes more natural reactions with the network at large? Could the network as a whole be more robust, with potential for natural decentralization? Is this method already in place in modern P2P applications. These are all questions I would like to further study…

TSP - Mathworld Explanation
Traveling Salesman Problem Competition

Artificial Mavens

I’ve been reading through The Tipping Point by Malcom Gladwell. One of many books given as Christmas presents, deciding which one to begin with has been a challenge. Gladwell writes an entertaining and enlightening book about social epidemics, and the simple catalyst that can start it all rolling.

Gladwell takes a chapter to explain three types of people: connectors, mavens, and salesmen. Mavens struck my attention. They are information seekers, and quickly become experts on whatever subject(s) they are interested in. What’s most intriguing about them, is they seek to share this information with others in their immediate network.

Mavens aren’t required to do this, its just a natural instinct they have. Its a natural reaction they have to the information they learn, discover, and seek. In my exploration in networks, and the potential to create a network as a vehicle for artistic expression, I’m curious how Mavens could be created algorithmically. Would they become unique nodes within a preconceived network, or simply coexist with the other nodes without much impact or attention given to themselves. Many of these questions should be explored soon, with simple designs of physical networks that exhibit these artifacts.

I need to remind myself to keep things small, simple, and elegant as I research the effects of these networks, so that I can see the results quickly and continue to find interesting correlations that will further drive this line of questioning.