With the release of Sora and Dall-E by OpenAI, Veo by Google, and other powerful AI models generating a plethora of AI-generated art, a new portal of endless possibilities has opened up — one where digital art is just a prompt away. This is a fascinating world to consider, where movies like Interstellar could be created with just a few steps of prompt engineering. But is there something that separates the code from the canvas? The algorithms from the art? The data from design?

How do artists and AI learn art?

Both AI and humans learn about art by observing it — the difference being that AI is trained on vast amounts of data, while humans are equipped with eyes and a brain to appreciate and absorb the beauty of nature and other art forms. This raises the question — is AI art also “art” as we define it? Humans also draw inspiration from existing art throughout their lifetimes, similar to what an AI model does.

But what is creativity?

In simple terms, isn’t creativity just iterating over a few concepts, mixing them up, and coming up with something that hasn’t been seen before — yet originates from previously known ideas?

In this way, we can argue that both AI and humans continuously iterate on ideas until they find the best one, which can be labeled as creative and unique.

What’s the difference then?
The major difference between AI art and human art is the intent and the emotion behind it (for now). Humans infuse emotions into their art, heavily influenced by the feelings they experience through interactions with others. It conveys what they feel, what they want to express, or the emotions it evokes in others.

“ART IS HOW WE DECORATE SPACE, MUSIC IS HOW WE DECORATE TIME.” — JEAN-MICHEL BASQUIAT

With this definition of art and music, it is fair to say that for something to resonate with us and be part of our space and worth our time, it must connect with us on a personal level. Humans have the ability to tailor-make that — that’s what makes human art unique.

Another difference lies in how humans learn — it’s not just about observing art, but also interpreting it, influenced by factors like culture, thought processes, and various other qualities. This makes the learning process unique to each individual, which isn’t the case with AI. AI models learn by analyzing previous data and iterating on different parameters to generate the output.

One more major difference is imperfection. Human art is often imperfect and unpredictable. The artist’s thoughts and progress can be seen in their work, whereas AI rarely changes its learning or output methods.

What’s next?

With advancements in AI, we might see more sophisticated and emotionally resonant art pieces. Could AI someday develop its own unique style? How might AI tools evolve to assist artists in ways we can’t yet imagine?

AI can also create interactive art experiences that respond to the audience’s inputs in real time. This shows us a new dimension where art responds to the viewer and is unique to each viewer.

ABOUT AISQUARE
AISquare is an innovative platform designed to gamify the learning process for developers. Leveraging an advanced AI system, AISquare generates and provides access to millions, potentially billions, of questions across multiple domains. By incorporating elements of competition and skill recognition, AISquare not only makes learning engaging but also helps developers demonstrate their expertise in a measurable way. The platform is backed by the Dynamic Coalition on Gaming for Purpose (DC-G4P), affiliated with the UN’s Internet Governance Forum, which actively works on gamifying learning and exploring the potential uses of gaming across various sectors. Together, AISquare and DC-G4P are dedicated to creating games with a purpose, driving continuous growth and development in the tech industry.

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Author — Reyansh Gupta