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David Young

David Young is an artist and technologist whose intimate AI artworks are helping develop new understandings of what AI is – and what it can enable in the future.

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Intro:

David Young has spent his 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.

Selected AI projects:

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Flowers

AI / Machine Learning generated images. 2018-2019.

Using AI & machine learning the computer was trained with photographs that Young took of flowers at his farm in Bovina, NY during the summer of 2018. It then generated its own images. Young notes that what emerges is not 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.

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Cloud Canyon

AI / Machine Learning generated images and videos. 2018.'

Using AI & machine learning the computer was trained on photographs that Young took near Mulholland Hwy in Los Angeles. It then generated its own images.

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Winter Woods

AI / Machine Learning generated images and videos. 2018.

Using AI & machine learning the computer was trained in photographs that Young took in the woods on his farm in Bovina, NY during the winter (November & December) of 2018. It then generated its own images

Young on using artificial intelligence:

How do you approach AI in your creative practice?

“We live in an age of scale. Of trillion dollar companies. Of social media followers measured in the hundreds of millions. Of efficiency, consumption, and greed being the greatest of our goals and ideals. And it is into this context that emerges a new technology: Artificial Intelligence.

AI seems perfectly aligned with our current moment, for it is built on the idea of scale — AI requires huge, almost unimaginable, amounts of data to learn and function. These are quantities of data that only our largest companies can provide, for they are the ones who see and capture everything we do.

Embedded within this data are the beliefs and biases present in our culture. And as a result we develop, not entirely intentionally, AI applications that further accelerate and optimize the scales, and inequalities, already in place.

Is this future inevitable? Can only our largest organizations, with their vast data sets, decide how we will use AI?

What if, instead, we could start small? To work at the scale of the personal. 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?

It is from this perspective that I approach my work. Instead of scale and efficiency, I ask: Can aesthetic experiences give a fresh start to how we think about AI? Can beauty be a basis from which we imagine new possibilities for AI?”

What have you learned from your artistic exploration of AI?

“In an era where critics were unconvinced by photography, and photographers tried to mimic painterly styles, Walter Benjamin argued for something different. In his essay “Art in the Age of Mechanical Reproduction” Benjamin made the case for embracing the machine-enabled art form and the aesthetics that are derived from it.

How then should we feel about the role of AI in today’s world of art? Does the machine still bend to the will of the artist, or does it replace the artist all together?

What I’ve discovered, through the process of helping the machine learn nature, is that it is indeed a symbiotic process. The “artist” must tune the imagery that’s put into the “machine” to craft its interpretation of nature. And the artist must continue to select the work that the machine creates (much like photographers would use a contact sheet) in order to make the most unique, and frankly beautiful, interpretation of nature.

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.

This process is not dissimilar to that used by both Hudson River School and Audubon artists. In each, the artists used elements from multiple scenes, or birds, to create their final composite image. Their images show something which doesn’t exist in the real world, but that captures their understanding of the subject.”

Has your understanding of AI changed throughout your work?

“AI forces us to reconsider what we mean by intelligence and its endless forms. Through this work I have come to realize that AI is not an all-knowing super intelligence. But it is a new tool, and with it come issues and considerations much the same as tools from earlier eras.

We should never accept new technologies purely for their novelty. So, too, we should reject them if they reinforce existing, or create new, inequalities. But if they give us new ways to experience and understand the world around us, then they’re enabling new ways for us to imagine our future.

Using AI in the service of art may be a small step, but if beauty can be used to make AI more approachable, then we’re a step towards building a more diverse and just future.”

Describe your series Little 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.”

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