Month: September 2019

Ellan Suder-LookingOutwards-02

Alfred Hoehn’s Drawing Machine

Creator’s name: Alfred Hoehn

Title of the work above: Drawing Machine Ptolemaios

Year of creation: 2008

I really enjoy the works of Alfred Hoehn, a Swiss artist who constructs large-scale linkage harmonographs. The simple movements of the pendulums create complex images that can look flat/abstract or look almost 3D, depending on how the artist sets the lengths and the diameters of the spinning circles.

The finished product is fun to look at, but the process of watching the image as it’s being drawn is important to the project as well. It’s very mesmerizing and calming –– a few people in the comments section were comparing it to a computer screensaver.

The artist can probably estimate the finished product to an extent, but I like the element of uncertainty. It’s kind of like plugging a random equation and seeing what kind of graph it’ll make.

Caroline Song – Looking Outwards-02

Mitchell Whitelaw specializes in creating generative systems, as well as data-aesthetics. This specific project called Local Colour (made in June 2011), is made out of recycled vegetable boxes, that are both laser-cut and laminated into bowl forms such as the one below. After that, there is a network diagram graphic that is mounted onto the bowls by using a second generative process.

Local Colour, made out of vegetable boxes that have been laser-cut.

I find this project to be interesting because of its use of recycled vegetable boxes to create a bowl, which then showcases a network diagram. Network diagrams can visualize data from places such as social networks, biological systems, and etc. The network diagram being on a bowl is the part that intrigues me because this bridges together the scientific data that network diagrams have, with the artistic form it takes in the shape of a bowl.

I do not know much about the algorithm that generated the work, but I suppose that it took into account the specific curves and edges of the bowl in order to properly infuse the graphic into the form. I also suppose that the algorithm had to somehow take numerical data and translate it into a visual form in order to incorporate it into the bowl.

Whitelaw’s artistic sensibilities are shown in this algorithm very clearly. As I stated above, his algorithm had to take this precise, scientific data and turn it into a graphic that could be presented visually on the laser-cut vegetable boxes. The fact that the data had to go from numbers to graphics alone shows Whitelaw’s artistic side.

12 Tone Serialism

This is less of a specific project but more of a musical movement that occurred in the mid 20th century. Serialism is the systematic composition of music that is completely unbiased in terms of tonal center. Where as most music is written in a key (B major, c minor, etc.), serialistic music equally values each of the 12 notes found in western music.

A key aspect of this form of music is the tone row. Tone rows are mathematically generated lists of the 12 chromatic notes. The only rule to these lists is that each note must appear exactly once. The most famous 12 tone composer was Arnold Schoenberg (1874-1951), he is also credited with the creation of the 12 tone system.

https://www.instructables.com/id/Create-a-Twelve-Tone-melody-with-a-Twelve-Tone-Mat/

This website goes into detail about one way of creating tone rows. While this is not the only way to go about doing it, it is one of the most well known methods.

Example of a tone row using all 12 chromatic notes

While the order of the 12 notes is usually random, the artistry of the composer manifests in how they use the notes presented to them. While most serialistic music sounds extremely jarring to first-time listeners, an incredible amount of thought and care goes into their arrangement.

Composers transform their given notes in many creative ways. Some examples would be transposition, retrograde, and inversions. Often, part of the fun of 12 tone pieces is trying to figure out what the composer did to transform their original tone row into the piece you are hearing.

Example from one of Schoenberg’s 12 tone piano pieces
One of Schoenberg’s orchestral works
(If you have never heard this type of music before it might be pretty surprising)

Steven Fei – Looking Outwards – 02


With the development of algorithms, even artistic ideas can be developed and turned into reality with great efficiency. When designing architecture, I always put a lot of manual adjustings and editings for linework drawings and overal layouts of the rooms. However, an architecture studio Wallgren Arkitekter has created some programs embedded in the architectural parametric modeling tool Grasshopper.

Finch’s automatic production of room layouts

View this post on Instagram

Let’s draw some dwellings with #finch3d

A post shared by Jesper Wallgren (@jesper.wallgren) on

Once the site constraints and several specific requirements such as wall thickness and architecture type are set, the program will automatically create elegant drawings for the plans and sections. Meanwhile, the building models are created with great detail. When the user makes changes to the settings, changes in the drawings are laid out instantly. Such an algorithm provides an insight into modern healthy and safe living, and it also takes advantage of the site to generate the most ideal and economic layout for the rooms. Moreover, when combined with other programs that deal with weather and transportation proximities, the algorithm will also produce a map analyzing which house or suite will be most wanted. Such a new program inspires me to compare my own design with what the machine produces and to see which design factors I have considered or not. In addition, the program greatly facilitates the design efficiency and enables us to think in a wider picture for forms and environment.

Click here to see the introduction of the software

Official website for the software:http://finch3d.com/#

Ammar Hassonjee – Looking Outwards 02

An Interactive Wall That Moves According to Hand Movement

An interesting example of generative art I found online is a project called “Breathing Wall II” built in 2014 by Behnaz Farahi, an architecture professor at USC.

According to Farahi’s website, the “Breathing Wall” installation is made out of wood, PVC, and fabric, and its directly controlled by an Arduino that interacts with a Leap Motion system and depth camera to capture hand movement. The hand motions and signals are processed using DC systems and algorithms and then projected on the wall as contour lines, as well as the changes in the wall surface and color.

What I like most about this project is its exploration of the relationship between movement and the material, light, and color of the physical system. This installation gives its users the power to interact and impact their environment by allowing them to use intuitive controls like swiping and tapping to directly disturb the form and color of the wall, which contributes to a sensational experience. In my opinion, this art that is adaptive and involves activity from the user is the most compelling form of art.

A user interacts with “Breathing Wall II”.
Contour lines can be seen projected on the background of the wall.

Behnaz Farahi is known for her exploration in computational design, specifically with the use of interactive and motion based installations. Her work aims to connect human experience with structure as the focus is on how intuitive human motions and movement can impact a system. In addition to wall like installations, she has also designed interactive clothing and ceilings, which can be found on her website.

Project-01-Face cselloun

Here I am, added some interactive qualities to the self portrait as well, if you speak into the microphone it will transfer over to the actual animation.

sketch

var input;
var analyzer;
var volume = 0;

function setup() {
  createCanvas(600, 600);

  // Create an Audio input
  mic = new p5.AudioIn();

  // start the Audio Input.
  // By default, it does not .connect() (to the computer speakers)
  mic.start();
}

function draw() {
  colorMode(RGB);
  background(233, 255, 228);
   // Get the overall volume (between 0 and 1.0)
  var v = mic.getLevel();
  // "Smooth" the volume variable with an easing function
  volume += (v - volume) / 3;


  // Draw an ellipse size proportionally to the volume
  var mouthSize = map(volume, 0, 1, 20, 300);
  
   print(mouthSize);
  
  if(mouthSize < 26)
  mouthSize = 0;
  mouthSize = constrain(mouthSize, 0, 300);
  


  noStroke();
    //red pink rotating star in the back
 push();
  fill(255, 70, 231);
  translate(width*0.5, height*0.5);
  rotate(frameCount / 50.0);
  star(0, 0, mouthSize+290, 180, 30); 
  pop();
  //pink rotating star in the back
 push();
  fill(255, 13, 255);
  translate(width*0.5, height*0.5);
  rotate(frameCount / 50.0);
  star(0, 0, mouthSize+250, 170, 30); 
  pop();
  //inner rotating star 
  push();
  fill(107, 255, 0);
  translate(width*0.5, height*0.5);
  rotate(frameCount / 50.0);
  star(0, 0, mouthSize+220, 90, 30); 
  pop();
   //hair
  fill(54,17,17);
  rect(150,270,300,300,20);
  ellipse(300,240,300,300);
  //head
  fill(255, 215, 175);
  ellipse(300, 300, 310, 360);
   //eyebrows
  strokeWeight(15);
  stroke(54,17,17);
  line(230,200,180,200);
  line(420,200,370,200);
  //eyes
  noStroke();
  fill(300);
  ellipse(200, 250, 60, 60);
  ellipse(400, 250, 60, 60);
  //eyepuffs
  fill(255, 215, 175);
  ellipse(200, 325, 150, 100);
  ellipse(400, 325, 150, 100);
  //blush
  fill(255, 108, 155, 70);
  ellipse(200, 320, 90, 90);
  ellipse(400, 320, 90, 90);
  // nose
  noFill();
  stroke(214, 162, 126);
  strokeWeight(3);
  arc(300, 325, 50, 50, PI, 0);
  //lens big
  var eyeX;
  var eyeY;
  var lookUP;
  noStroke();
  fill(232, 134, 206);
  lookUP = map(mouseY, 0, 600, 240, 260);
  lookUP = constrain(lookUP, 240, 260);
  eyeY = map(mouseX, 0, 600, 200 - 10, 200 + 10);
  eyeY = constrain(eyeY, 200 - 10, 200 + 10);
  ellipse(eyeY, lookUP, 50, 50);
  eyeX = map(mouseX, 0, 600, 400 - 10, 400 + 10);
  eyeX = constrain(eyeX, 400 - 10, 400 + 10);
  ellipse(eyeX, lookUP, 50, 50);
  //lens small
  noStroke();
  fill(232, 12, 202);
  ellipse(eyeY, lookUP, 35, 35);
  ellipse(eyeX, lookUP, 35, 35);
  //pupil
  noStroke();
  fill(0);
  ellipse(eyeY, lookUP, 25, 25);
  ellipse(eyeX, lookUP, 25, 25);
  //white in the eye smaller
  noStroke();
  fill(300, 150);
  ellipse(eyeY - 5, lookUP - 10, 15, 15);
  ellipse(eyeX - 5, lookUP - 10, 15, 15);
  //white in the eye stronger
  noStroke();
  fill(300);
  ellipse(eyeX - 5, lookUP - 10, 10, 10);
  ellipse(eyeY - 5, lookUP - 10, 10, 10);
  //cheeks lines
  noFill();
  stroke(166, 39, 76, 70);
  strokeWeight(3);
  arc(400, 320, 94, 90, PI, 0);
   noFill();
  stroke(166, 39, 76, 70);
  strokeWeight(3);
  arc(200, 320, 94, 90, PI, 0);
  
  //blush lines
    strokeWeight(5);
    stroke(255, 70, 231, 70);
  line(180, 290, 185, 320);
  line(200, 290, 205, 320);
  line(220, 290, 225, 320);
     strokeWeight(5);
    stroke(255, 70, 231, 70);
  line(380, 290, 385, 320);
  line(400, 290, 405, 320);
  line(420, 290, 425, 320);
  
  
  
  //eye creases
  stroke(166, 39, 76, 20);
  strokeWeight(3);
  arc(200, 240, 68, 60, PI, 0);
   noFill();
  //eye creases
  stroke(166, 39, 76, 20);
  strokeWeight(3);
  arc(400, 240, 68, 60, PI, 0);
   noFill();
  



  // Get the overall volume (between 0 and 1.0)
  var v = mic.getLevel();
  // "Smooth" the volume variable with an easing function
  volume += (v - volume) / 3;


  // Draw an ellipse size proportionally to the volume
  var mouthSize = map(volume, 0, 1, 10, 300);
  fill(0)
  ellipse(300, 400, mouthSize, mouthSize / 2);

}
function star(x, y, radius1, radius2, npoints) {
  var angle = TWO_PI / npoints;
  var halfAngle = angle/2.0;
  beginShape();
  for (var a = 0; a < TWO_PI; a += angle) {
    var sx = x + cos(a) * radius2;
    var sy = y + sin(a) * radius2;
    vertex(sx, sy);
    sx = x + cos(a+halfAngle) * radius1;
    sy = y + sin(a+halfAngle) * radius1;
    vertex(sx, sy);
  }
  endShape(CLOSE);
}

Kimberlyn Cho- Looking Outwards- 02

Alan Palomo, the lead singer of Neon Indian, uses self-generative art to digitally project his body movements on the screen during performances. I found this artwork interesting in its innovative nature and promising future of contemporary stage art. As technology continues to progress, generative art such as this would allow for an enhanced stimulating experience at not just concerts, but potentially other events as well.

 

Neon Indian performing “The Glitzy Hive” at Webster Hall;

Palomo engages his audience both visually and auditorily by capturing his movements through a motion camera and projecting the data in different geometric forms on a screen behind the band. He uses a Microsoft device called “Kinect” to capture the movements on stage. Although Kinect hasnt been commonly enjoyed by artists due to its abstracted projection, Palomo shows great appreciation for the symbiotic relationship it provides for his music and performance. In the past, Palomo has attempted incorporating visuals into his performances but has always felt a disconnect between his music and his performance. Hence, he worked with a friend to use audio synthesizers to track audio lines to manipulate various projections. The algorithm of Palomo’s generative art allow for an endless possibility of digital projections based on his music and performance.

Hyejo Seo – Looking Outwards 02

Mario Klingemann is a German artist who uses neural networks, codes, and AI to create generative art. He put images of portrait paintings from the 17th and 19th centuries into GAN (Generative Adversarial Networks), which then produces replica or newly generated portraits based on its own interpretations. GAN is a deep neural net architecture that consists of two nets, going one against the other; the generator and the discriminator. The generator takes in random numbers in order to create images. Then, those generated images are sent to the discriminator with a stream of images from a given dataset (i.e. images of paintings that were put in). The discriminator’s job is then to produce probabilities, a number from 0 to 1, where 0 represents authenticity and 1 represents fake.

Memory of Passerby I by Mario Klingemann

In Klingemann’s artwork, GAN isn’t simply duplicating old paintings (input). GAN decides its own aesthetic using a Tinder-like selection of what it finds aesthetic or not, and after studying its own interpretation of the basic features of a face, it produces an infinite stream of portraits that are rather uncanny and creepy. Personally, I always believed that art was a field AI could never achieve to learn, since there are no set of rules on how to express and create art. Creativity is unique to individuals since we all have different interpretation of the world and different ways of self-expression. Mario Klingemann’s pioneering artworks proved me wrong, and this is why they caught my eye. I am excited, at the same time, terrified to see what the future holds for the AI art.

Alec Albright – Looking Outwards – 02

Linienbild series of generative curves by Zdeněk Sýkora

The Linienbild series of generative curves by Zdeněk Sýkora is a group of works by a pioneer in the field of generative art. Zdeněk Sýkora, creator, began this work in 1975 and continued it for over two decades. The inspirational aspects of this series for me are the connection and evolution of its general aesthetic as time progresses. It is as if for each several years of time passing, the thicker the lines and the more simple yet bold the next installment of the series becomes. I suppose that the algorithm used to create these works may have included some time variable to implement this idea of physical bulk and growth as time progresses. It also appears that the algorithm may have randomized the color of the lines.

Sewon Park LO-02

One piece of generative art that I have been most inspired by is the AI-generated fashion designs by Robbie Barrat. As AI is the up and coming technology that is being tested in numerous fields of business and science, it was very interesting to see it in action in the realm of art.

AI generated clothing designs inspired by dataset from Balenciaga

Robbie Barrat trained an AI model with numerous data from Balenciaga including their look books, campaigns, and magazines. The system then used the given data to create a whole new collection of Balenciaga inspired clothing line photos. The design is notable for its striking resemblance to existing styles that the designers of the brand has created. However the touch of the AI driven mechanism gives the designs an eerie touch that helps the new works of art stand out.

Although the results were not completely ready for immediate production, this work of art points to a path of incorporation of sophisticated artificial intelligence data analysis for art and fashion. Robbie Barrat’s combination of scientific intelligence and artistic sense produces high-level pieces of work that will inspire a new era of digital art.

Link: https://www.fastcompany.com/90223486/this-ai-designs-balenciaga-better-than-balenciaga