2b:null 2c:["$","title",null,{"children":"Untitled | katho | Observable"}] 31:T50f,// The Nature of Code // Daniel Shiffman // http://natureofcode.com // Interactive Selection // http://www.genarts.com/karl/papers/siggraph91.html //I think this seems totally fine //Constructor (makes a random DNA) class DNA { constructor(sketch, newgenes) { this.p5 = sketch; // DNA is random floating point values between 0 and 1 (!!) // The genetic sequence let len = 20; // Arbitrary length if (newgenes) { this.genes = newgenes; } else { this.genes = new Array(len); for (let i = 0; i < this.genes.length; i++) { this.genes[i] = this.p5.random(0, 1); } } } // Crossover // Creates new DNA sequence from two (this & crossover(partner) { let child = new Array(this.genes.length); let crossover = this.p5.floor(this.p5.random(this.genes.length)); for (let i = 0; i < this.genes.length; i++) { if (i > crossover) child[i] = this.genes[i]; else child[i] = partner.genes[i]; } let newgenes = new DNA(this.p5, child); return newgenes; } // Based on a mutation probability, picks a new random character in array spots mutate(m) { for (let i = 0; i < this.genes.length; i++) { if (this.p5.random(1) < m) { this.genes[i] = this.p5.random(0, 1); } } } }32:T440,p4 = md`To me, there's a certain depth to this problem — it has taken on a quality of the uncanny. Being "translated into a problem of coding," has something of the quality of staring into the void and it looking back. To what extent can we be translated into a problem of coding? As in the classical map-territory relation (which is wonderfully illustrated by Neil Gaiman in the introduction to *Fragile Things*^[5](#fn4)) — the "map" of reality, or the model that exists in a computer, can't represent that reality completely accurately, or it would be so detailed and complex as to be useless. It's only fairly recently that computational or algorithmic decision making systems have become wide-spread^[6](#fn5). We're only just being confronted with the idea of looking at computational models of our world, ourselves, and our behavior, and having those models look back at us — now, in helping us make decisions, the models concretely shape our reality, and can be more important to its shape than the "fluid social worlds" Haraway talks about.`33:Teaf,// The Nature of Code // Daniel Shiffman // http://natureofcode.com // Interactive Selection // http://www.genarts.com/karl/papers/siggraph91.html // A class to describe a population of faces // this hasn't changed very much from example to example // Create the population class Grouping { constructor(sketch, m, num) { this.p5 = sketch; this.mutationRate = m; // Mutation rate this.population = []; // array to hold the current population this.matingPool = []; this.generations = 0; // Number of generations for (let i = 0; i < num; i++) { let D = new DNA(this.p5); //make the first one the ground switch(i){ case 0: this.population[i] = new Feature(this.p5, D, 50 + i * 75, 60, "background"); break; case 1: this.population[i] = new Feature(this.p5, D, 50 + i * 75, 60, "river"); break; default: this.population[i] = new Feature(this.p5, D, 50 + i * 75, 60, "tree"); } } } // Display all faces display() { for (let i = 0; i < this.population.length; i++) { this.population[i].display(); } } // Are we rolling over any of the faces? rollover(mx, my) { for (let i = 0; i < this.population.length; i++) { this.population[i].rollover(mx, my); } } // Generate a mating pool selection() { // Clear the ArrayList this.matingPool = []; // Calculate total fitness of whole population let maxFitness = this.getMaxFitness(); // Calculate fitness for each member of the population (scaled to value between 0 and 1) // Based on fitness, each member will get added to the mating pool a certain number of times // A higher fitness = more entries to mating pool = more likely to be picked as a parent // A lower fitness = fewer entries to mating pool = less likely to be picked as a parent for (let i = 0; i < this.population.length; i++) { let fitnessNormal = this.p5.map(this.population[i].getFitness(), 0, maxFitness, 0, 1); let n = this.p5.floor(fitnessNormal * 100); // Arbitrary multiplier for (let j = 0; j < n; j++) { this.matingPool.push(this.population[i]); } } } // Making the next generation reproduction() { console.log("reproduction"); // Refill the population with children from the mating pool for (let i = 0; i < this.population.length; i++) { // Sping the wheel of fortune to pick two parents let m = this.p5.floor(this.p5.random(this.matingPool.length)); let d = this.p5.floor(this.p5.random(this.matingPool.length)); // Pick two parents let mom = this.matingPool[m]; let dad = this.matingPool[d]; // Get their genes let momgenes = mom.getDNA(); let dadgenes = dad.getDNA(); // Mate their genes let child = momgenes.crossover(dadgenes); // Mutate their genes child.mutate(this.mutationRate); // Fill the new population with the new child switch(i){ case 0: this.population[i] = new Feature(this.p5, child, 50 + i * 75, 60, "background"); break; case 1: this.population[i] = new Feature(this.p5, child, 50 + i * 75, 60, "river"); break; default: this.population[i] = new Feature(this.p5, child, 50 + i * 75, 60, "tree"); } } this.generations++; } getGenerations() { return this.generations; } // Find highest fitness for the population getMaxFitness() { let record = 0; for (let i = 0; i < this.population.length; i++) { if (this.population[i].getFitness() > record) { record = this.population[i].getFitness(); } } return record; } }34:T4c7,p9=md`Google's efforts to create image recognition software using neural networks result in systems which can be run "backwards" to generate images too — you can use them to amplify features in images of noise to get a particular classification result^[10](#fn9). Using these systems creatively leads to [fascinating and surreal imagery](https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB) that almost seems like the electric sheep of proverbial Android dreams^[11](#fn10). Looking at the images you wonder both if the computer is as creative as you are. I start to wonder if finding patterns of eyes and dogs and buildings that aren't actually there in the things I see and think are what creativity has been all along.

![image](https://lh3.googleusercontent.com/pw/ACtC-3cnSmNNjwlPugSZ0My6jrdY06B_yp4sPOD47BLs_WaeWkCn8ICJzjw4duDJwSnQAzgw2_eDLF3RWH5ZA_O7Q-AwyEiXCtaweBhmr4FT3ZhDhrd-iVx9DOcWEzxQ-UOXUrkcefbLox32sftSkVdB6w_HQA=w640-h301-no?authuser=0)Images from [Google Deep Dream](https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html ).

`35:T86d,p10 = md`

![image](https://lh3.googleusercontent.com/pw/ACtC-3ffehjyqFrkLBeEA9ac9SS494QMO7cJGZ2uH6EQsTYToRpEcs78B2jtqDF0Rcsk-xHPCuiK4bofw5_iEWgtF2AiEqX8aaOrbuX8PkTwiK8dm95F7SXEZfBTbMDizNM8snOlZxxZr1jHfUlwxV8z7-cP1w=s307-no?authuser=0)This image was downloaded from [thispersondoesnotexist.com](https://thispersondoesnotexist.com/) at 5:02pm on January 23, 2021.

When you visit Phillip Wang's website [thispersondoesnotexist.com](https://thispersondoesnotexist.com/)^[12](#fn11), you view an image of a human face. The face is not a photograph, but was generated by a neural network — one trained to generate images against a second intended to recognize generated images which compete with each other^[13](#fn12). So as they improve, they create more and more convincing images, and the faces seem more and more to whisper anecdotes in your ear. Sometimes, there are uncanny details that might make the generated images a little bit less convincing — backgrounds that don't quite make sense, accessories that aren't quite right. Looking at them, I think about the way I can imagine lives for these people, who were generated based on my request. I start to wonder how different I am, especially how different I would be to a computer system. It would be easy to generate the data that represents me, rather than collecting it. A system made with [RiTa.js](https://rednoise.org/rita/) or [OpenAI](https://openai.com/blog/gpt-2-1-5b-release/) could incorporate the data into answers from a person who is, for computational purposes, me. So, computers can generate a data set to represent a person who is, as far as algorithmic decision making is concerned, as real as I am. Then is it important that I exist in a biological body and have a squishy brain, out here interacting with other organisms who do as well? Even if I think that distinction is important, does my opinion matter, if the computer making important decisions about me can't tell the difference? Within this system, do I exist in a way that is important beyond computational purposes?`36:T5ad,p11 = md`If algorithms can "dream," what other behaviour can pop up at the intersection of mathematics and decision making by agents who can have only the context we provide? Kottke's discussion of [How AI Agents Cheat](https://kottke.org/18/11/how-ai-agents-cheat) gives us a glimpse into models^[14](#fn13) that vary from whimsical — digital creatures which generate free energy in their simulated world by clapping their body parts^[15](#fn14) — to the somewhat terrifying — digital creatures who, because giving birth had no cost in the system, evolved to mate, produce children, and eat them^[16](#fn15). Examples for which the practical applications are a bit more clear include a mushroom classifier that learned that poisonous and non-poisonous mushroom images were supplied in an alternating order^[17](#fn16). Reading them all in a list, all I can think about is the fact that within the computer, there isn't any inherent difference between evolving digital creatures, and indicating which image is of a poisonous or edible mushroom. Each are questions answered by responding to a set of input data, finding patterns within those sets. The system can't distinguish a pattern introduced by how data is gathered or presented from one that exists in the reality the data set represents. And, to the system, the stakes of the two problems are the same — getting the answer wrong.` 37:T21fe,// The Nature of Code // Daniel Shiffman // http://natureofcode.com // Interactive Selection // http://www.genarts.com/karl/papers/siggraph91.html // The class for our "face", contains DNA sequence, fitness value, position on screen // Fitness Function f(t) = t (where t is "time" mouse rolls over face) // Create a new face class Feature { constructor(sketch, dna_, x_, y_, type) {//could send number to go more generative locations. this.p5 = sketch; this.type = type; //console.log(type); this.rolloverOn = false; // Are we rolling over this face? this.dna = dna_; // Face's DNA //this.x = x_; // Position on screen this.x = this.dna.genes[10]*this.p5.width; this.y = this.dna.genes[11]*this.p5.height; if (this.x>xriveroffset-50 && this.x going to need this of course. //this.p5.noStroke(); switch(this.type){ case "background": //console.log("draw a background"); this.p5.background(c); //fill in the background colour, think that's the only thing I'm using. Maybe I should like avg them all? //will need to change the mousover thing for the background. IE add the bounding box properly. // Display fitness value //need to fix this too this.p5.textAlign(this.p5.CENTER); if (this.rolloverOn) this.p5.fill(0); else this.p5.fill(0.25); this.p5.text('' + this.p5.floor(this.fitness), 100, 100); break; case "river": //Draw the river this.drawRiver(genes, c, eyecolor, mouthColor); break; case "tree": //console.log("draw the tree"); //why are the colours not working? this.drawTree(genes, c, this.x, this.y, eyecolor, mouthColor); break; default: } this.p5.pop(); this.p5.push(); this.p5.translate(this.x, this.y); //--> going to need this of course. this.p5.pop(); } drawRiver(genes, c, eyecolor, mouthColor) { //console.log("draw the river"); this.c = c; this.eyecolor = eyecolor; this.mouthColor = mouthColor; this.genes = genes; //this.p5.stroke(0, 255, 0); //this.p5.strokeWeight(4); //this.p5.rect(20, 20, 60, 60); //This is Not Working. this.p5.push(); this.p5.translate(xriveroffset,0); this.p5.stroke(eyecolor); this.p5.fill(this.mouthColor); this.p5.strokeWeight(10); this.p5.beginShape(); //console.log("beginShape"); //generate some of these based on the genes? let leftpoints = []; let rightpoints = []; let yl = 1; let yr = 1; let lowestx = 0; let highestx = 0; for(let i = 0; i<5; i++){ //these are totally random //they are bouncing, I should like add a new number to it each time for the y let lxs = this.p5.int(genes[i]*xmult-Math.random()*genes[10]*5); let rxs = this.p5.int(genes[i+1]*xmult+xwidth+Math.random()*genes[10]*5); yl = yl+this.p5.int(genes[i+2]*ymult+ydist-Math.random()*genes[10]*5); yr = yr+this.p5.int(genes[i+3]*ymult+ydist+Math.random()*genes[10]*5); leftpoints.push({x:lxs, y:yl}); rightpoints.push({x:rxs, y:yr}); } //shunt this to a function //do the top of the river. If this is not curvy, add another at the same point that's a curve? let x1 = genes[6]*xmult-Math.random()*genes[10]*5; lowestx = x1; let x2 = genes[7]*xmult+xwidth+Math.random()*genes[10]*5; highestx = x2; this.p5.curveVertex(x1, -ydist/2); // first left-bank point. this.p5.curveVertex(x2, -ydist/2); // first right-bank point. //go down the right side for(let i = 0; ihighestx)? rightpoints[i].x : highestx); } // do the bottom of the river let rx = genes[8]*xmult+xwidth+Math.random(); highestx = ((rx>highestx)? rx : highestx); let lx = genes[9]*xmult-Math.random(); lowestx = ((lx=0; i--){ this.p5.curveVertex(leftpoints[i].x, leftpoints[i].y); lowestx = ((leftpoints[i].x { // The Nature of Code // Daniel Shiffman // http://natureofcode.com // Interactive Selection // http://www.genarts.com/karl/papers/siggraph91.html //vars for placing elements const xmult = 30; const ymult = 30; const xwidth = 200; const ydist = 55; let population; let info; let button; let cwidth = 950; let cheight = 400; sketchface.setup = function() { sketchface.createCanvas(cwidth, cheight); sketchface.colorMode(sketchface.RGB, 1.0, 1.0, 1.0, 1.0); let popmax = 10; let mutationRate = 0.05; // A pretty high mutation rate here, our population is rather small we need to enforce variety // Create a population with a target phrase, mutation rate, and population max population = new Grouping(sketchface, mutationRate, popmax); // A simple button class button = sketchface.createButton("evolve new generation"); button.mousePressed(sketchface.nextGen); button.position(cwidth-175, cheight-40); button.id("sketch-button"); info = sketchface.createDiv(''); info.position(cwidth-150, cheight-70); info.id("info-div"); } sketchface.draw = function() { sketchface.background(1); // Display the faces population.display(); population.rollover(sketchface.mouseX, sketchface.mouseY); info.html("Generation #:" + population.getGenerations()); } // If the button is clicked, evolve next generation sketchface.nextGen = function() { console.log("new generation"); population.selection();//this seems OK population.reproduction(); } });39:[] 2f:{"id":"0aac865a11ab36c1","slug":null,"title":"","thumbnail":"f0d9e40aa9dfa3b222d876ae7d9c0d390d80a2fea9fb41ed328361d6aa905ba0","access_level":"unlisted","create_time":"$D2021-01-22T05:30:33.733Z","update_time":"$D2021-03-17T17:29:46.978Z","view_time":null,"first_public_time":"$D2021-01-25T16:33:54.243Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"2af8c9116115837f","login":"katho","name":"","avatar_url":"https://avatars-next.observableusercontent.com/923b127d6c8235a3e600f24af6f53c306a7dc34d42ae80c071db72770e6b0c40","tier":"starter_2024"},"version":792,"publish_version":792,"role":null,"authored":false,"authors":[{"type":"individual","id":"2247c34bd87165c7","login":"katho","name":"","avatar_url":"https://avatars-next.observableusercontent.com/923b127d6c8235a3e600f24af6f53c306a7dc34d42ae80c071db72770e6b0c40","tier":null}],"members":[{"type":"individual","id":"2247c34bd87165c7","login":"katho","name":"","avatar_url":"https://avatars-next.observableusercontent.com/923b127d6c8235a3e600f24af6f53c306a7dc34d42ae80c071db72770e6b0c40","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"","cells":[{"id":359,"value":"p1 = md`\n*This is a bit of a musing in code and text about how code shapes the world we live in. As we go, I put together some code to generate a little world. You can see that world, and influence it to become more to your liking via a genetic algorithm.*`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":415,"value":"p2 = md`There's a line from Donna Haraway's *A Cyborg Manifesto*^[1](#fn1) that I think about a lot. It's the idea that communications systems are part of a move towards \"the translation of the world into a problem of coding.\"^[2](#fn2)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":427,"value":"p3 = md`In the text itself, she states this in the context of a discussion of the way that the fluid social worlds must be formalized to become input data for communication science and command and control systems. Haraway goes on to say that this move is \"a search for the common language in which all resistance to instrumental control disappears and all heterogeneity could be submitted to disassembly, reassembly, investment, and exchange.\"^[3](#fn3)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":485,"value":"p5a = md`In this context, DNA is a useful metaphor — DNA is often conceptualized as the physical \"code\" for our bodies^[4](#fn4-1), assembled through chemical processes that are analogous to the computational ones we use to process information. (You can click to the left of the \\`DNA = class\\` text to view the code, if you wish.)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":514,"value":"p6 = md`In my own thinking about these issues, I've decided to call this state of affairs — that is, when a computer's model becomes more important to decision outcomes than the complexities of reality — \"computational ontologies\".\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":518,"value":"p7 = md`There are many social impacts the use of computational ontologies. They can become problematic when their use undermines the responsivity and inclusiveness of our societies. Their use can introduce or perpetuatesocial and economic inequality -- and can even do so on auto-pilot. For more about these wider and acute social issues, I refer readers to books like Cathy O'Neil's *Weapons of Math Destruction*^[7](#fn6), Frank Pasquale's *The Black Box Society*^[8](#fn7) and Safiya Noble's *Algorithms of Oppression*^[9](#fn8). These social issues arise from a combination of technical and social issues related to how we make decisions, the decisions we want computers to make or help with, the information we can collect, the way we use that data to create decision-making systems, and the existing biases in the world which form patterns computers can find and intensify. `\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"p8=md`Here, I'd like to focus more on some of the strangenesses that can come to the fore when we're looking into the heart of the algorithm. Then, I'd like to think about how we're reflected back.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":559,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":605,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"p12=md`Throughout this text, you may have noticed some code paragraphs — like the \\`DNA = class\\` block [at the beginning](p5a). If you'd like to look at the code, you can click to the left of the paragraph to view the code. Throughout the text, I've added in the additional blocks of code needed to generate a digital world. The world will use a genetic algorithm to \"evolve\", using input from you to determing what parts of the world are \"successful\" and should continue to appear. Earlier in this text, I [declared some DNA ](#p5a)for the parts of a generated world. Then I [set up a group to store our world elements](#p4). This next block of code determines how the genes we send to our entities will be turned into things in our world. Again, you can click to the left of the block to see the code if you like.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":643,"value":"p13=md`These examples have been those in the woolly and weird areas of AI. This isn't because there aren't down to earth and concrete examples, like systems which are deciding whom governments can trust^[18](#fn17), recognising faces on streets^[19](#fn185), allocating healthcare^[20](#fn18), and deciding who sees ads for which jobs^[21](#fn19), and who is ultimately offered them^[22](#fn20). Because these systems are the ones that are directly shaping our world into something new, they aren't engineered to show the potential holes in the system — you aren't supposed to think about them and see a person who doesn't exist looking back. But as the faces we can generate become more and more convincing, it does make it easier to ask whether our existence is any more meaningful to a decision system than that of a collection of generated pixels. `\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":644,"value":"p14=md`Thinking about all of these ways that systems create feedback loops with our lived reality, now I invite you to influence the fitness of the generated world you see below. Hover over the parts of it that speak to you, and when you want to look at a new generation, press the button. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"p15 = md`What have you trained your world to become? What patterns do you want to intensify?`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":406,"value":"md`## Colophon\n\nThe code in this piece is based on examples from Chapter 10 of Daniel Schiffman's [*The Nature of Code*](https://natureofcode.com/book/chapter-9-the-evolution-of-code/). His Youtube channel, [*The Coding Train*](https://www.youtube.com/user/shiffman) is a great place to start if you want to try programming like this!\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":340,"value":"footnotes = md`## Footnotes`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"fn1 = md`⁣1. Haraway, D. (1991). A Cyborg Manifesto. In Simians, cyborgs, and women: the reinvention of nature (pp. 292–342). Routledge. https://doi.org/10.1007/s10529-009-0120-4 [⬆](#p2)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":433,"value":"fn2 = md`⁣2. Ibid, 165 [⬆](#p3)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":437,"value":"fn3 = md`⁣3. Ibid, 165 [⬆](#p3)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":718,"value":"fn41 = md`⁣4. Keller, E. F. (2002). Making sense of life explaining biological development with models, metaphors, and machines. Harvard University Press. [⬆](#p5a)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":488,"value":"fn4 = md`⁣5. Gaiman, N. (2010). Fragile things. Hachette UK.[⬆](#p4)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":492,"value":"fn5 = md`⁣6. Danaher, J. (2016). The Threat of Algocracy: Reality, Resistance and Accommodation. Philosophy and Technology, 29(3), 245–268. https://doi.org/10.1007/s13347-015-0211-1 [⬆](#p4)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":529,"value":"fn6 = md`⁣7. O’Neil, C. (2016). Weapons of Math Destruction (Kindle). Crown. [⬆](#p7)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":530,"value":"fn7 = md`⁣8. Pasquale, F. (2015). The Black Box Society. Harvard University Press [⬆](#p7)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":535,"value":"fn8=md`⁣9. Noble, S. U. (2019). Algorithms of Oppression. In Algorithms of Oppression. https://doi.org/10.2307/j.ctt1pwt9w5 [⬆](#p7)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":568,"value":"fn9=md`⁣10. Mordvintsev, A., Olah, C., & Tyka, M. (2015). Research Blog: Inceptionism: Going Deeper into Neural Networks. https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html\n [⬆](#p9)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":569,"value":"fn10=md`⁣11. Dick, P. K., & Reprint of (manifestation): Dick, P. K. (1996). Do androids dream of electric sheep? http://www.worldcat.org/title/do-androids-dream-of-electric-sheep/oclc/34818133 [⬆](#p9)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"fn11=md`⁣12. Metz, B. R. (2019, February 28). These people do not exist. Why websites are churning out fake images of people (and cats) - CNN. CNN Business, 1–7. https://www.cnn.com/2019/02/28/tech/ai-fake-faces/index.html\n [⬆](#p10)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":613,"value":"fn12=md`⁣13. Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4396–4405. https://doi.org/10.1109/CVPR.2019.00453\n [⬆](#p10)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":619,"value":"fn13=md`⁣14. Examples from [this spreadsheet](https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml) linked in Kottke's blog post, which includes links to the papers. [⬆](#p11)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":621,"value":"fn14=md`⁣15. Sims, K. (1994). Evolving virtual creatures. Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1994, 15–22. https://doi.org/10.1145/192161.192167\n [⬆](#p11)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":623,"value":"fn15=md`⁣16. Yaeger, L. (1994). Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context. Santa Fe Institute Studies in the Sciences of Complexity Proceedings, 1, 1–25.\n [⬆](#p11)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":624,"value":"fn16=md`⁣17. Ellefsen, K. O., Mouret, J. B., & Clune, J. (2015). Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills. PLoS Computational Biology, 11(4), 1–24. https://doi.org/10.1371/journal.pcbi.1004128\n [⬆](#p11)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":660,"value":"fn17=md`⁣18. Lee, C. S. (2019). Datafication, dataveillance, and the social credit system as China’s new normal. Online Information Review, 43(6), 952–970. https://doi.org/10.1108/OIR-08-2018-0231\n [⬆](#p13)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":772,"value":"fn185 = md`⁣19. Williams, T. (2015, August 12). Facial Recognition Software Moves from Overseas Wars to Local Police. The New York Times. https://nyti.ms/1NqQtsX`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":661,"value":"fn18=md`⁣20. Lecher, C. (2018, March 21). What happens when an algorithm cuts your healthcare. The Verge. https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy\n [⬆](#p13)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":666,"value":"fn19=md`⁣21. Sweeney, L. (2013). Discrimination in Online Ad Delivery. Queue, 11(3), 10. https://doi.org/10.1145/2460276.2460278\n [⬆](#p13)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":667,"value":"fn20=md`⁣22. Mann, G., & O’Neil, C. (2016). Hiring Algorithms Are Not Neutral. Harvard Business Review, 1–2. https://hbr.org/2016/12/hiring-algorithms-are-not-neutral\n [⬆](#p13)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":397,"value":"md`## Other classes, imports, variable and code`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":723,"value":"mutable fnum = 0","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":725,"value":"inIframe = function () {\n try {\n return window.self !== window.top;\n } catch (e) {\n return true;\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"// The Nature of Code\n// Daniel Shiffman\n// http://natureofcode.com\n\n// Re-implementing java.awt.Rectangle\n// so JS mode works\n\nclass Rectangle {\n constructor(x, y, w, h) {\n this.x = x;\n this.y = y;\n this.width = w;\n this.height = h;\n //console.log(x);\n }\n\n contains(px, py) {\n return (px > this.x && px < this.x + this.width && py > this.y && py < this.y + this.height);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"xmult = 30;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"ymult = 30;\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"xwidth = 200;\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":158,"value":"ydist = 55;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"import {p5} from \"@tmcw/p5\";","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":240,"value":"xriveroffset = 200;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"style = html`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":562,"value":"html` \n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 30:"$2f:body"