HUMANMACHINE
HUMANMACHINE is an ongoing research that explores human-made generative art through chaos and serendipity. By anthropomorphising machine logic, ambiguity is introduced into simple computational algorithms, and design rituals that train Artmachines to think like a computer and build new aesthetic pathways into generative art. The algorithms used in this process vary across abstract to precise, small to large rulesets, and emotional to rational sentiments, amongst other variables, until significant chaos is generated.
//MACHINE no. 1
skin;
blackWhiteMesh;
blackGradient;
redGradient;
blueGradient;
void setup() {
size(1920, 1920);
background(black);
}
void draw() {
distort(blackWhiteMesh(random, random, black, white)) {
if(blackWhiteMesh = liquid) {
distort=0;
}
}
distort(blackGradient(random, random, black, white)) {
if(blackWhiteGradient = soft) {
distort=0;
}
}
distort(blueGradient(random, random, black, white)) {
if(blueGradient = effervecent) {
distort=0;
}
}
distort(redGradient(random, random, red, white)) {
if(redGradient = liquid) {
distort=0;
}
}
distort(blueGradient(random, random, blue, white)) {
if(blueGradient = liquid) {
distort=0;
}
}
distort(skin(random, random, image(skin))) {
if(skin = hard) {
distort=0;
}
}
distort(redGradient(random, random, red, white)) {
if(redGradient = soft) {
distort=0;
}
}
distort(blackWhiteMesh(random, random, black, white)) {
if(blackWhiteMesh = liquid) {
distort=0;
}
}
distort(blueGradient(random, random, black, white)) {
if(blueGradient = effervecent) {
distort=0;
}
}
draw vortex () {
If (glitch = true) {
vortex = 0;
}
}
}HUMANMACHINE explores the artistic potential of human-made generative art through error, chaos and serendipity. The goal is to investigate this subgenre as a disruptor of the conventional human-machine duality. By inviting errors into a generative process, I open up the possibility of uncovering the human subconscious and intuition that reveal itself in visual form. Producing a library of art objects determined by simple computational algorithms, I explore “ambiguity” as an interface of the human psyche while tackling the following questions:
How do humans interpret visual algorithms differently than machines?
How can we capture human intuition through error, chaos and serendipity in generative art?
How does machine precision limit esoteric creativity?
This research was initially inspired by the Theory of Shape Grammars by Stiny and Gips, which organises shapes in a linguistic structure as a tool for form generation. I use this as a bridge between coding environments like Processing and the works of conceptual artists such as Sol LeWitt and Frank Stella. By anthropomorphising machine logic, I recruit HUMANMACHINEs that contribute unique “ambiguities” to the principles of visual reconstruction, are trained to think like machines and build aesthetic pathways into generative art.
The algorithms used in the process are simple enough to be accessible to humans, yet will vary across abstract to precise, small to large rulesets, emotional to rational languages, amongst other variables, until significant error is produced.
These outputs will therefore be presented with their algorithms to reveal the contrast between one’s intellectual interpretation of the algorithms and that of the HUMANMACHINES. This research also opens up the possibility of a participatory practice, where multiple HUMANMACHINES perform the same algorithms with different error sets. In this case, inter-subject variability in skill, experience, and interpretation will be the determining factor.
In a reversal of roles, machines become the processors of information and humans become mindless bodies that solely generate tasks. Through rigidity or absentmindedness, as well as pure misinterpretations, the HUMANMACHINES produce accurate outcomes mixed with mistakes that introduce a culture of freedom, a sense of humour, a peek into the subconscious and the process as performance into generative art. The generation of the algorithms becomes a performance and is a part of the outcome. The contribution of this research is the critique of the predictability of machine art. I’m also planning to create a human-centric esolang that optimises the HUMANMACHINE outcomes, which can be useful for basic design education.
