Google & Generative Art

Kate Vass Galerie Team is a big fan of Google Arts & Culture, the largest online platform which allows users to explore art collections from around the world featuring content from over 2000 leading museums and archives, we like to reference it from time to time in our blog. Platform features over 130 different art movements from Renaissance to Contemporary art, however, everyone can easily notice that there is still a lack of content and no subsection for generative art nor digital art. We would like to give a humble recommendation to Google team to look into generative art and maybe eventually create a subsection for it or add few artists, as we believe that the generative artists are among the most important artists in the history of art.

Generative art, once perceived as the domain of a small number of “computer nerds,” is now the artform best poised to capture what sets our generation apart from those that came before us - ubiquitous computing.

In the last few years we have seen a tremendous spike in the interest of “AI art,” ushered in by Christie’s and Sotheby’s both offering works at auction developed with machine learning. Capturing the imaginations of collectors and the general public alike, the new work has some conservative members of the art world scratching their heads and suggesting this will merely be another passing fad.

What they are missing is that this rich genre, more broadly referred to as “generative art,” has a history as long and fascinating as computing itself. A history that we have highlighted in our recent show Automat und Mensch (or Machine and Man) here at Kate Vass Galerie in Zürich, curated by Jason Bailey and Georg Bak.

Emphasizing the deep historical roots of AI and generative art, the show took its title from the 1961 book of the same name by German computer scientist Karl Steinbuch. The book contains important early writings on machine learning and was inspirational for early generative artists like Gottfried Jäger.

Jäger, generally considered the father and founder of “generative photography”, was among the first ones to use the term “generative aesthetics” within the context of art history. Don’t miss the exclusive interview with him, which you can find in our quarterly publication on Collecting Generative Art.

The show featured other important works from the 1960s through the 1980s by pioneering artists like Vera Molnar, Nicolas Schöffer, Frieder Nake, and Manfred Mohr.

Generative works from the early 1990s included pieces by John Maeda, former president of the prestigious Rhode Island School of Design (2008-2014) and associate director of research at MIT Media Lab. Though Maeda is an accomplished generative artist with works in major museums, his greatest contribution to generative art was his invention of a platform for artists and designers to explore programming called "Design By Numbers."

Casey Reas, one of Maeda’s star pupils at the MIT Media Lab, is the co-creator of the Processing programming language (inspired by Maeda’s “Design By Numbers”) which has done more to increase the awareness and proliferation of generative art than any other singular contribution. Processing made generative art accessible to anyone in the world with a computer. You no longer needed expensive hardware, and more importantly, you did not need to be a computer scientist to program sketches and create art.

Among the most accomplished artists to ever use Processing are Jared Tarbell and Manolo Gamboa Naon, who we both represented in the exhibition. Tarbell mastered the earliest releases of Processing, producing works of unprecedented beauty.

Argentinian artist Manolo Gamboa Naon - better known as “Manolo” - is a master of color, composition, and complexity. Highly prolific and exploratory, Manolo creates work that takes visual cues from a dizzying array of aesthetic material from 20th century art to modern-day pop culture. Though varied, his work is distinct and immediately recognizable as consistently breaking the limits of what is possible in Processing.

With the invention of new machine learning tools like DeepDream and GANs (generative adversarial networks), “AI art,” as it is commonly referred to, has become particularly popular in the last five years. One artist, Harold Cohen, explored AI and art for nearly 50 years before we saw the rising popularity of these new machine learning tools. In those five decades, Cohen worked on a single program called Aaron that involved teaching a robot to create drawings. Aaron’s education took a similar path to that of humans, evolving from simple pictographic shapes and symbols to more figurative imagery, and finally into full-color images.

AI and machine learning have also added complexity to copyright, and in many ways, the laws are still catching up. We saw this when Christie’s sold an AI work in 2018 by the French collective Obvious for $432k that was based heavily on work by artist Robbie Barrat.

Pioneering cyberfeminist Cornelia Sollfrank explored issues around generative art and copyright back in 2004 when a forum for new media plug.in refused to show her Warhol Flowers. The flowers were created using Sollfrank’s net.art generator, but the gallery claimed the images were too close to Warhol’s “original” works to show. Sollfrank, who believes “a smart artist makes the machine do the work”, believed she had a case that the images created by her program were sufficiently differentiated. Sollfrank responded to the gallery by recording conversations with four separate copyright attorneys and playing the videos simultaneously. In this act, Sollfrank raised legal and moral issues regarding the implications of machine authorship and copyright that we are still exploring today.

While we have gone to great lengths to focus on historical works, one of the show’s greatest strengths was the range of important works by contemporary AI artists. We start with one of the very first works by Google DeepDream inventor Alexander Mordvintsev. Produced in May of 2015, DeepDream took the world by storm with surreal acid-trip-like imagery of cats and dogs growing out of people’s heads and bodies. Virtually all contemporary AI artists credit Mordvintsev’s DeepDream as a primary source of inspiration for their interest in machine learning and art. We had the great opportunity to include one of the very first images produced by DeepDream in the exhibition.

The show also included work by Tom White, Helena Sarin, David Young, Sofia Crespo, Memo Akten, Anna Ridler, Robbie Barrat, and Mario Klingemann.

And for the ones who would like to learn more about this intriguing art genre, we highly recommend the brilliant article Why Love Generative Art? by Jason Bailey! Not to miss also our two publications covering the theme ‘Collecting Generative and Digital Art’ which you can find here.

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