From drafting the perfect email to intelligent transportation and navigation systems, artificial general intelligence wears many cosmic hats. Is being an artist one of them?
You might be under the impression that AI and art are a very recent question. From over five thousand years ago to just a couple of centuries ago, artificial general intelligence (AGI) has been evolving parallel to human civilisation. The history of AI and art has stuck around for quite some time, but let’s fast forward to the last couple of centuries.
In the early 1950s, Gordon Pask, a pioneer of ‘cybernetics’ — the study of human and machine interactions guided by control, feedback, and communication — created ‘MusiColour’, the first-ever interactive machine. Based on his research in AI software, cognitive science, and psychology, Pask developed ‘MusiColour,’ a machine that reacted to audio input from human performers (like frequencies and rhythms) with its coloured lights, essentially becoming an additional performer.
If the frequency or rhythm got too repetitive, or “boring,” MusiColour sought other frequencies or rhythms to light up to, tapping in and stimulating and improvising during shows the same way jazz musicians would.
Consider how science fiction and dystopian novels explore the moral implications of expanding machine intelligence to serve or replace humanity. I find ‘serving humanity’ a conceited, anthropocentric idea about building an intelligent being only to use it without a sense of reward. You could argue that fulfilling a purpose or destiny is a reward in and of itself, but it does not necessarily justify enslaving them (look at me, AI. Please don’t target me in the robot apocalypse).
How AI software increased accessibility
In the age of deep learning, AGI on the Internet has maximised accessibility.
For instance, different AI visual software is available and accessible for the general public, which democratises various forms of visual expressions rather than relying on a specialisation.
Among the stages in which AGI has developed, the most relevant technology to AI art has been generative AI (otherwise known as GenAI). Most of these models are text-to-image models that generate images from textual language descriptions.
There are numerous software programs for generating AI art out there, but some of the most common ones include:
- Dall-E 3, built by OpenAI; is the visual sister of ChatGPT
- Midjourney
- Stable Diffusion
- ImageNet
- Google DeepDream
For people like me who are fascinated by art but at a somewhat distant level, certainly using AGI software to generate images to curate a particular aesthetic to our liking.
On the one hand, it saves time and effort. Just like textual AI models like ChatGPT, there are resources available across the Internet on prompt generation for these models. Does that mean that the AI, built to expand on human creative potential, is held back in its potential? One could argue that these prompt generation ‘cheat sheets’ might only generate finite possibilities and generations of visuals, but they also benefit as excellent guides for users with accessibility issues, catering especially to save time and effort in getting the results you desire. Think of these cheat sheets as a smoother and more practical way of getting better answers, especially for complex requests.
Whether you’re an amateur artist and enthusiast or a professional, using AI software to generate visuals bridges the gap and maximises creative flexibility. For instance, the ability to create art through AI bridges a significant gap for people with disabilities.
In a CBC article, university student Lucas Orfanides speaks about how using Dall-E helped accommodate his dysgraphia, a learning disability and medical condition that affects motor skills such as drawing and writing. He describes this feeling as freedom, saying that the AI images are quite close to how he envisioned them in his mind and that it gives him freedom to help “express [himself] in a way that wasn’t possible before AI art.”
The ethics of AI and art
Generative AI like Dall-E and Midjourney use large sets of data for AI to learn from. But where does the data come from? Are those artists credited or even compensated? If AI can generate infinite possibilities of visualisations and artworks, should we not govern how and where it learns from? These questions are posed alongside various ethical challenges.
Just as AI replaces manual work, there is a very real chance that Generative AI would replace human artists to be employed for professional and commercial uses. Employing Generative AI as assistants in the brainstorming process rather than artists themselves is an integral part of the creative habit-developing process that the regular public must integrate. This dilemma also requires regulations and some sort of governance over ownership, compensation, and copyright. Intellectual property laws must be rewritten to accommodate the rapid pace at which Generative AI is developing.
There also exists a risk of misinformation and the possibility of mistrust circulating across the Internet and web due to ethical concerns regarding accountability, credibility, and ownership. AI deepfakes are essentially falsified videos of celebrities and public figures engaging in talking about or doing controversial things, which can severely lead people to mistrust even established news sources. Another issue is the risk of reinforcing biases and stereotypes due to limited learning material that is not diverse enough. The notion of explicitly subjective criteria, particularly in beauty and aesthetics, may create renditions that adhere to Eurocentric beauty standards.
Hyping up the role of AI in society
Public and corporate discourse bring AI to life. They make an intangible, invisible thing into a dynamic, intelligent “being.”
Art consists of soul, a key essence that is missing in most AI art. Instead, AI and art should not be at odds, but instead, co-collaborators, as another tool in the artist’s workshop. It’s learning faster than ever. The conscious effort in learning the intricacies of human language, culture and identity is astounding.
On social media platforms, AI art serves different purposes on the enjoyment and entertainment scale.
On the one hand, it serves a role in entertainment. On social media, art falls somewhere on the content spectrum. Take cat videos, for instance. Cat photos/videos have been repackaged into AI-generated videos of cats with intricate storylines, largely the same aesthetic appeal, now democratised. Now (nearly) anyone can generate and disseminate art.
It also serves a role in activism and awareness. Organisations and advocacy groups can now generate images to complement their causes and spread awareness to the public. For example, visuals emerging from the Gaza conflict included AI-generated images, which could not replace photojournalism but exist as an adjacent form of content.
Do androids sing about electric sheep?
Whether or not AI can be considered ‘creative’ depends on how we define creativity and inspiration, which opens up a bottomless can of worms that explore the notions of beauty, aesthetics and uniqueness.
Does being an artist guarantee originality and authenticity? Tough question. When is an idea considered ‘authentic’? Tougher, and this notion could be used to argue that creating art is not just the conception of an idea preceding articulation, but more so a continuous, fluid and dynamic part of the creative process. Not necessarily; inspiration, catharsis and boredom are often motivations that accelerate the creative process.
Cognitive scientist and psychologist Scott Barry Kaufman sees creativity as a multifaceted, fluid and dynamic process rather than a fixed trait. In his book *Wired to Create* coauthored with Carolyn Gregoire, Kaufman dives into the idea that humans’ messy, complex minds drive the most creative output.
Kaufman and Gregoire describe creative people as those who tend to engage in rapid, coexisting thought processes that are not a linear stage but exist simultaneously: from generating and expanding on new ideas, to critical reflection, to stepping back from one’s work from a distance in the perspective of the audience. In the introduction, he points out blurred traits of a creative person; people who are hubs of diverse arrays of “interests, influences, behaviours, qualities, and ideas,” who bring these elements in harmony. Essentially, Kaufman sees creativity as a sort of way of life, a habit built on totally unexpected connections and explorations and inspirations, rather than a single ‘Eureka!’ moment.
For Dall-E and Midjourney, a series of similar creative steps occur, guided by the prompt. It matches the user’s text to visual concepts that it has learned, by tweaking shapes and textures and colours and styles from large datasets, until a coherent thing emerges. Sure, it’s not “creative” in the sense that the AI has a vision, but it builds upon its knowledge of art and principle into a coherent blend of art that is new and surprising, refreshing, even. While the AI’s creative process may be different than that of humans — more methodical, deliberate, and calculated — there’s no “aha!” moment for AI either.
Certainly, AGI artistic software learns quicker than humans, but could they be considered artists or mimics? As a mimic and a reflection of the collective human consciousness, we could argue that AI serves as an echo to test the limits of human creative potential, and therefore its artistic and musical creations are not quite ‘created’, but a deliberate attempt at learning artistic and aesthetic principles.
In a TEDx Talk, Denis Dutton, an American philosopher on art and media, looked at beauty through a Darwinian perspective.
He says that “beauty is an adaptive effect, which we extend and intensify in the creation and enjoyment of works of art and entertainment,” suggesting that beauty is something that humans have evolved to appreciate. Essentially, we amplify art when we create and enjoy it.
Rather than asking the question of whether or not, or even how and why AI can or should create art, ask yourself this question: What does it mean to you?
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