We designed and trained a connectionist network to generate letterfonns in a new font given just a few exemplars from that font. It was necessary to have separate but interconnected hidden units for " letter" and "font" representations - several alternative architectures were not successful.

Could a machine learning model be trained to generate realistic (or just interesting) letterforms? As it turns out, yes, albeit with mixed results. A styleGAN model was trained in RunwayML on a dataset of 2674 Google fonts organised as individual image-per-glyph in Drawbot. Runway-generated images were then piped via Python in to GlyphsApp to process the final font. Some characters took longer/more steps to create recognisable forms but generally speaking each glyph was processed on 3k model training steps.

Not zeros and ones or binary code--though that's a language, too--but a visual vernacular that helps humans make the connection that, yes, a computer was here and it made its mark. It comes in all forms: the perfect crispness of an illustration drawn on a Wacom pad; the trippy swirls of a Google Deep Dream image; the fuzzy imperfection of an AI-generated font. "We call it the computer accent," said Claire Evans, lead singer of synth pop band YACHT. For their recently released album, Chain Tripping, Evans, her bandmates, and a cast of creative AI experts explored how this so-called computer accent can be used to artistic ends. Sure, this is nothing new--you can't really get more "computer accent" than Kraftwerk (Computer Love was released way back in 1981, to name the most obvious example.)

We designed and trained a connectionist network to generate letterfonns in a new font given just a few exemplars from that font.

Here during the production event a very low infonnational input ("Madonna," and "Matisse") is used