Foreword By Jason Bailey

“While rationalism would say that the machine learns emotions from the training data, romanticism instead poses that they are innate.” - David Young

“In addition to memories from a long-distant conscious past, completely new thoughts and creative ideas can… present themselves from the unconscious – thoughts and ideas that have never been conscious before. ” - Carl Jung

David Young is one of the most articulate artists working in the AI art space today. Not only can he explain the technical workings of machine learning models like GANs (generative adversarial networks), but he is also expert in sharing his philosophical views on AI and how these views impact his artistic practice.

In his important essay Tabula Rasa - Rethinking the Intelligence of Machine Minds, Young compares the untrained machine learning model to John Locke’s conception of the human mind as a “tabla rasa,” or “blank tablet.” As Young explains, Locke believed we develop rational knowledge from our experiences in the natural world. As with the human brain, it is thought that machine learning models are also blank slates until they are trained on data. But as Young points out, machine learning models are trained on biased data curated by humans, and not (as with the human brain) on the natural world:

Given that the machine is reflecting on the sensation of biased data, what makes us think that the machine can possibly be rational? We must consider that the rationality offered by these systems may be an illusion.

How are we to think of the machine learning model if, as Young proposes, it may not, in fact, be rational? Young reminds us that the Romantic movement served as an antidote to the rationalism of the enlightenment and wonders aloud if we may need a similar antidote for our understanding of machine learning.

During the Romantic period of the arts, we placed “the artist’s emotions as the most authentic source of aesthetic experience,” according to Young. Young then asks if human emotion might be a better analogy than human rationalism when comparing AI to humans.

I’d personally like to offer up a Jungian alternative to the Lockean interpretation of the mind as a tabula rasa. Rather than starting with a “blank slate,” Swiss psychiatrist Carl Jung believed that we are all born with a set of shared ideas, a “collective unconscious.” According to Jung, this collective unconscious is filled with archetypes and visual symbols that resonate universally within humans regardless of their individual experience, geography, or era.

The artistic genius, according to Jung, was one who could pluck these universal symbols from the collective unconscious and manifest them as physical and tangible works of art. According to Jung, one way to do this was to tap into dreams. Jung posits that in dreams, the collective unconscious become more accessible. But what does this have to do with art?

Jung’s ideas on archetypes, dreams, and the collective unconscious were hugely influential on twentieth-century art, in particular with the Surrealists and the Abstract Expressionists. Artists including Jackson Pollock and Mark Rothko were heavily influenced by Jung’s writing and ideas, and many argue they saw themselves as tapping into the collective unconscious to unlock these archetypes and Jungian symbols through their work.

Whether we buy into Jung’s ideas of the collective unconscious or not, I think that we can agree that the best known abstract painters tapped into something primal which continues to resonate across boundaries, be they cultural, geographic, or generational.

When David Young shared a contact sheet of work from his latest machine learning model which was trained on minimal data, I was shocked at how similar the images were to twentieth-century abstract paintings.

Take a look at the visual comparisons below with known twentieth-century abstract painters on the top rows and David’s machine learning work trained on minimal data in the bottom rows.

Kenneth Noland