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.
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.
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.
Slaves to Armok: God of Blood
Chapter II: Dwarf Fortress
Dwarf Fortress is a procedurally generated game made by brothers Tarn and Zach Adams released in 2006. Development is supported by crowdsourced donations. To play, the player needs to generate a new world through parameters ranging from resources to the length of the world's history. I imagine that the algorithm used to generate the world runs during the duration of the game. It likely randomly generates an elevation then picks areas to have biomes, then randomly picks what lives there and so on.
Graphics are simple text symbols, but they can be replaced with custom tilesets.
I admire the potential immersion that a player, they can literally do anything! The brothers' love for generating random worlds is visible.
The effective complexity of this piece is closer to disorder because the game world is constantly changing due to random events
The video that introduced me to Dwarf Fortress (view at your own discretion NSFW):
AARON is an AI developed by Harold Cohen to create original paintings. Cohen has been developing the same algorithm since 1973. There's a lot of things I find fascinating about this project. The algorithm is really an evolving thing, with a lot of complex parts. It's a little difficult to find information, but it seems that AARON has some simple imperative rules as well as some learning functions. It's amazing how much the algorithm has changed since its inception. At first, it just did line drawings, then color, then more and more abstract shapes. Its most recent works look like a different artist than the early works:
AARON's paintings, I think, are actually more simple than many generative algorithms, and that's something that I find impressive. They are concise.
Inigo Quilez's Happy Jumping (2019) is an incredible project for a few reasons. The project itself is a raymarched shader of a cute character jumping around on a bouncy floor in a surreal landscape. One can easily talk about the impressive technical skill behind this piece. The jumping is believable, the floor bounce is wonderful. What I find admirable about this piece is how whimsical the work is, given the context of the piece. ShaderToy is a host to many technically skilled individuals, a few of which have masters and PhDs in math or physics. This piece breaks a mold of the usual geometric study and invites silliness to an otherwise mathematically focused group. The algorithm behind this piece are available for all to see, but the main backbone is the raymarching algorithm, and movement is done with noise. Inigo has proven that he caneasilydophotorealism, but lately his sensibility from has time at Pixar has been prevalent in his work. Though infinitely generated, the piece will always be the happy creature jumping along.
Extrapolating on the "tulip fever" craze in 17th century Netherlands, Anna Ridler's installation,"Mosiac Virus," uses AI technology to develop stunning videos of synthetic tulips. In the real tulip industry, collectors value the flowers for uniqueness, viruses and mutations. Riddler highlights these prized features with her generated tulips; their unique mutations growing, blooming and adapting to the rise and fall of the bitcoin market. The project began with her searching for, photographing, and categorizing ten thousand tulips by hand, which became its own installation seen here. She then fed a generative adversarial network (GAN) these ten thousand photographs as a training set for her own creations. I find this supplementary installation equally, or perhaps even more, important than the project itself. When ownership of generative art is challenged, I question whether work generated with the photos or paintings of others can truly be considered theirs. With this training set however, Ridler creates wholy individual work that calls attention to the human labor that goes into creating generative, "autonomous" art. I feel that her work manages to capture the stunning nuances of a biological organism that organically encapsulates effective complexity.
This week I chose to focus on Leonardo Solaas's work shown at "La emergencia de la imagen" ("The Emergency of Image"), the final show for the Production Marathon Lab 2016 at the Spain Cultural Center in Buenos Aires. Working together with a group of 16 participants, the artist used generative systems to create random and minuscule porcelain sculptures, each with a unique and recognizable body, personality, and/or function. I couldn't find information on whether or not these sculptures were 3D printed or hand-modeled after being generatively designed, but either way I love how a mass society of small beings can be produced with technology.
This is an image featuring a series of lake houses that have been generated by a procedure "all the way from silhouette to final texturing*"
Seeing this blew my mind, in retrospect it makes sense that in movies and video games objects would have some generative component to their creation, but I had really never considered this to be something that could be done with so much, I suppose, artistry.
These generated houses have such a beautiful, whimsical, and hand touched and imagined form, texture, and feel. Realizing that this kind of world building can take place in a generative programmed structure is eye opening. The algorithm itself likely works as a generative 3D model with specific constraints and elements such as window, door, walls, roof etc that are matched with specific types of (also generated) textures. This all balances the order and disorder beautifully. The creators artistic sensibly comes through in the style of the houses/the constraints and textures and the natural forms that this algorithm follows.