DAVID YOUNG

© Copyright 2014-2019 David Young. All rights reserved.

David Young

David Young has spent his entire career at the leading edge of emerging technologies. His current work explores how beauty and aesthetic experiences can give a fresh start to how we think about new technologies. This work, which uses AI and machine learning, is a return to his roots where he began his career at the height of the 1980’s AI boom.

Over the course of his career he founded a cutting-edge design studio, worked for design agencies, worked in-house, and independently consulted. He has served on the board of AIGA/LA, and taught at both Art Center College of Design and Parsons at The New School. His design work has been recognized with a wide range of press and awards — including a Gold Medal from Businessweek & IDSA.

David has a masters degree in visual studies from the MIT Media Lab, and a bachelors degree in computer science from UCSC.

David is based in Brooklyn, NY and Los Angeles, CA.


 

 
About Learning Nature

Learning Nature asks: Is it inevitable that only our largest organizations, with their vast data sets, will decide how we will use AI? What if, instead, we could start small, to work at the scale of the personal, and to engage directly with AI. Could doing so allow us to develop new intuitions and understandings of what the technology is, and what it could enable? Can aesthetic experiences give a fresh start to how we think about AI?

This work started in the rural context of upstate New York and the domain of nature. I chose this, not only to emphasize its difference from mainstream AI, but to place it in the area’s rich creative history. Almost two hundred years ago the Hudson River School used painting to express man’s relationship to nature. What would it mean for an AI today to understand/interpret that same nature?

The machine was taught using my photographs, and it generated its own “photographs” using a GAN. Nothing that emerges is accurate, but the work isn’t asking for accuracy — it’s asking for the machine to build its own unique vision of the natural world. The misinterpretation is a piece of the work. Just as theorists have argued that the entire history of culture has been interpretation and misinterpretation of the cultural movements that preceded it, so too this work embraces the misinterpretation of nature by machine. We know that there’s not a human “intelligence” in the code, but still we anthropomorphize.

The Learning Nature series includes the projects Flowers, Winter Woods, and Cloud Canyon.

For additional information see the essay Little AI.