The process of recoding the Molnár piece was much harder than I expected. I spent more than a handful of hours staring at the piece and wondering whether the runs were of the same algorithm, and if not, what had stayed the same. I fell short in a few ways. I had more straggler lines than I wanted (some were in the actual image, though for me it was a bug). Working with multiple holes proved harder than I thought. I don't think my piece has the same directionality as Molnár's piece either. I also tried to implement rectangular holes, but it didn't end up panning out. I have especially learned to appreciate the gaps, as the more I researched and worked, the more I noticed that was far from replication.
I chose George Nees's work "Texture of Gravel" or "Schottertextur" where he wrote a program in ALGOL and introduced random variables in the program causing orderly cubes to fall into a more chaotic arrangement. Nees's interest in the relationship between order and disorder is clearly portrayed here in this work. This work inspires me because I am interested in chaos within order/ order within chaos in a system and how that looks or feels. This works effective complexity is that it is simple, easy to describe and predict through the program in which he wrote for it. Running it multiple times would give it different outcomes but all predictably achieve the same general composition of the orderly squares descending into disorder.
Robbie Barrat created house plant generated sketches using their electrical signals and an arduino. I like how it references nature and generates a aesthetic, unique, and simple sketch. After you attach the electrodes on the leaves, you use voltage readers to read/graph the voltage. "Use template.pde as a base program for your artwork; while in the draw loop, you can get the value of the plant anytime through the variable plantValue (which is a float)." There isn't enough examples of this piece to understand the effective complexity. I think that it is has balanced disorder from the uniqueness of the plants, and I assume that other plants will have vaguely related looking sketches based on the same algorithm.
Question 1A. Something you like which exhibits effective complexity. Where does your selection sit between total order and total randomness.
Snake scales are closer to total order. A snake morph is a genetic mutation that makes a snake look different than commonly expected (visual indicators). Some morphs can be predicted since most follow the punnet square rule (ie dominant, recessive, het). However, not all morphs are consistent/can be predicted since they can be spread out across different genes and we don't know which gene causes which morph. Hence, there are many combinations (python has like 100+ morphs). Some morphs can have repeated patterns, random blobs, scaleless snakes.
The blacktailed cribo is a simple example, but other snakes have many many morphs that can be hard to predict. Most morphs are discovered by people breeding snakes, so a lot of morphs have not been discovered yet.
false water cobra
hypomelanistic false water cobra
lavender false water cobra
Question 1B. Problem of Authenticity.
The argument is that if the computer expresses itself that the artist can not anticipate, does it's randomness still qualify for the artist's expression? I believe that it is still the artist's expression. The artist created the code and system for the computer to execute.I see computers as a tool for randomization and computation (like how paint, pens, etc are tools).
1A. A honeycomb exhibits the idea of effective complexity as this is a simple system that contains high degrees of order and disorder. The beehive is a pretty complex hierarchical system and the honeycomb itself as a structure is very orderly, however I think there is a degree of disorder in the hive. I would place the honeycomb closer to the "total order" side of the spectrum (but not completely) and a little directed to the total randomness side.
1B. The Problem of Locality, Code, and Malleability is an interesting one to me as Galanter questions where art resides onto its logical status. The argument about generative art is that some people feel as though it is like any other artwork that has an object or event while others criticize the object or event and see the generative system as the art. Personally, I feel as though the generative system is the art itself because the most interesting/ more important part about generative art is how it was made to become the "object" or "event" rather than what the object becomes.
Nature and biological life are systems that serve as exemplars to effective complexity, and so dandelions were what first came to my mind as one of my favorite instances of such. On the spectrum, I believe dandelions fall within an equal split between relative order and randomnesses, which is why I feel particularly drawn these weeds, as I am fascinated with the balance and similar distribution of both. Typically, dandelions can be associated with having extreme "randomness" -- pick up the flower, blow on it, and have the seeds scatter haphazardly over the field by dancing, lifting, and dipping from the wind. However, once each individual seed settles, there is a method to which biological processes will take place from there -- depending on the fertility of land, the seed will take root, germinate, bloom into flowers, and repurposed into white pappis with seeds at the end. Additionally, dandelions are capable of asexual reproduction, resulting in many identical flowers. Wind and/or other factors in nature then chaotically disperses of the seeds, and the cycle continues.
1B) The problem of Meaning: Can and should generative art be about more than generative systems?
Generative art serves as a medium to help maximize the possibilities and skillsets of an artist. However, to this, arises the issue of whether or not the emphasis should be placed on the "generative" or "art" aspect. Some projects call for attention to be drawn to the multiple iterative art pieces as the final product (with little regards to the process in which went about creating them; a top down approach), whereas others highlight the system of creating generative art (with little regards to the byproduct itself; a bottom up approach). Although there lies value in both approaches, I find myself personally aligning with the values of bottom up. Typically, when I finalize my mind on exactly what an end desirable should exhibit, I find myself more "comfortable", in the sense that I have a working goal in mind and am more so simply seeking the bridges to connect me to that. Whereas, when I work from a bottom up approach, I find it more rewarding to "seek truth to process as being inartistically beautiful", which not only celebrate creation as an activity, but also allows me to maintain an open mindset, and ultimately, design and create emotionally durable experiences.
Face Trade is a vending machine of sorts -- cash in a portrait mugshot of yourself (taken on the spot at site), in return for receiving a computer generated face drawing. Your mugshot that Face Trade receives will then be permanently stored in the Ethereum Blockchain, therefore suggesting the exchange of a "semi-permanent" face-swap. The Face Trade project is comprised of a printer, thermal printer, buttons, lcd screens, speakers, cameras, flash, MDF, steel, paint, computer, and website. There is no information as to how Moka has decided the algorithm to which produces the unique generated portraits, and it is also not explicitly stated if there is a a feedback mechanism to which the mugshots help generate the unique portraits. However, I would think that there would be some sort of initial face detection code to pinpoint key components of a face (two eyes, a nose, a mouth, etc.), and then a library to which these faces would be generated from. From this, I suspect that there could be a machine learning element in which new mugshots retrieved could play a significant role in generating new eyes, noses, and so on and so forth.
I enjoy this project because of the union of inputting a personal stake and receiving an unique surprise. Moka "often trades control in favor of surprise" because of his belief of computation as an expressive tool. The effective complexity of this project is 50% balanced order and 50% disorder - the user has half of the power to generate the end deliverable; they have the complete choice to input whether or not they want to "cash in" and the deliverable (an unique portrait), however, they have no say as to how their mugshot will be used thereafter and what their unique portrait will look like.
There is an overall flow to the image (having majority lines flowing either upwards/downwards, or flowing left/right
Most lines are either touching one another or intersecting
Lines near areas of negative space can be found to not touching other lines
Many areas have repetitive lines "patterns" of some sort, with slight change of angle from one line to the next.
Negative spaces between normal touching lines have relative similar area.
There are random patches of absence of lines ("interruptions")
Negative spaces "interruptions" are no more than 30% of the space.
There are almost "columns" / "rows"; each line going down and across seem to have the same center point
Amount of lines in each "column" / "row" ranges from roughly 45-55 lines.
Process: Originally, I had wrote three main functions (one to generate the lines themselves, one to generate a grid for which these lines would be placed, and one for calculating random holes based on noise(), to which I would then use in regards to the grid + line functions. I had run into several issues with this way, as I struggled manipulating each individual line to rotate in more "random" ways - it came down to either rotating all the lines at once, or rotating the entire canvas. I then decided to build off of Dan Shiffman's Coding Challenge #24, to which the manipulation of identical length lines were achieved through creation of a vector variable (p5.vector.fromAngle), to which he was then able to manipulate solely the vector itself by calling a rotation directly onto the vector, rather than the entire canvas. Then, through a noise function, I was able to achieve allotting "holes", or gaps in the canvas.
Although Dan Shiffman's way was very neat in achieving a series of segmented lines, I would have been more satisfied if I were to have the time to debug my own separate three functions. I believe that I would be able to achieve similar results if I could translate the way I drew lines by calling a vector, and then directly calling onto the vector to call for a rotation.
If effective complexity is to be understand as a system moving in a structured yet unpredictable way, the slime mold can be a fine example. The slime mold is a single celled organism that will continuously expand itself until it reaches food nearby, at which point it will contract until it exists in the shortest path between itself and food. It has no control over the food position, but will follow the same logical procedure when it finds the food.
I think about The Problem Of Uniqueness quite often. Instagram is now full of simple effects on typography, or looping gifs of shape illusions. Even more complex generative art tends to follow much of the same structure. I do think that there are moments with which artists can express uniqueness, even in a space where it seems everyone is playing the same hand. Take for instance the generative artist Shane, who has donemanyplays on the idea of a tunnel, or turned a truchet pattern into a structure, or sculpture.
Quantum Fluctuations by Markos Kay is a generative abstract animation created through a simulation of quantum physical interactions when protons collide. It's an intricate visualization of interactions that are usually observed through very indirect means.
I love how the work is clearly digital, comfortable with allowing the viewer to see digital artifacts, but at the same time, the actions going on are so complex and dynamic that the underlying logic seems very real and tangible.
I'm guessing Kay achieves the piece by tying various types of animations to different events in the particle simulation, and then movement of the particles shapes those animations. He clearly cares a lot about the physical phenomena underlying the visual experience, so the generative algorithm is probably determined heavily by the particle simulation.
Kay creates effective complexity by devising extremely diverse animations to occur at different particle events, then the randomness of the simulation acting on the order of the chosen animations creates the complexity.
