Faces are "interesting and we humans react strongly to them, we tend to read a lot into them," Tyka explained over email. "I find I connect with them when I work with them, I'm curious about who they might be, if they existed."

As such, Tyka has spent the past nine months or so developing the Portraits of Imaginary People project, which follows his earlier works, Inceptionism and The Groovik's Cube. For Imaginary People, Tyka sought to use generative neural networks to create original portraits, much like the one Alexander Reben used to mimic Bob Ross' speaking style.

To do so, Tyka turned to a machine learning technique known as a generative adversarial network (GAN). "I started experimenting with GANs for this installation I did with Refik Anadol, where we used the technique to generate imaginary historical documents from a large archive," Tyka wrote. "After we finished that project (it opened in April in Istanbul), I started looking at faces again using the same techniques."

If you want a generative model like a GAN to, say, draw you a picture of a cat, you'll first have to get a huge data set of cat pictures and then train the model to create a picture of a cat with all the requisite features like ears, whiskers and a tail. In this case, Tyka utilized roughly 20,000 high-resolution portraits from Flickr as his base training data set.

That's a good first step, but let's say you want a realistic picture of a cat, not a digital doodle. To do that, you need to set up a second (adversarial) neural network, known as a discriminator, for your GAN. So while the first network (the generator) creates pictures of cats, the discriminator's job is to compare those generated images against real-world samples (e.g., actual pictures of cats) and figure out if they're fake or not. Based on each result, the system then goes back and tweaks the generator network's parameters to make the output image appear more and more realistic.