Mozart, Van Gogh, and Bob From Next Door: Creating Art With Artificial Intelligence

KEY TAKEAWAYS

Generative art has redefined art by granting artistic expression to non-artists, revolutionizing creation and emotion sharing. It is a true challenge to established artists, yet its current constraints suggest a prolonged evolution timeline.

There was a time when art creation was limited to those with the skill – or those who thought they had the skill.

But now anyone can create inspiring music, art worthy of being hung on your wall, or even a highly-detailed weapons pack for a computer game.

All with a simple prompt inside a text box.

This article looks into the exploding, colorful world of generative AI art.

What is Generative AI Art?

Any time artificial intelligence (AI) creates something on its own, it can be called generative AI, and when an AI is prompted to create art, we get generative AI art in all its forms, including visual art, poetry, prose, animations, music, videos, and interactive installations.

Since its inception, generative AI art is becoming more sophisticated, to the point of being indistinguishable from art forms created by human beings.

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In most examples, it starts with humans providing a leaping-off point (a prompt, an image, a style) and AI producing the rest, creating some fascinating work as an outcome.

For example, to draw a bouquet, a model learns about the shapes, colors, hues, and intricate designs of various flowers and bouquets before getting to work.

However, generative AI art may generate art differently than a human might envision it.

The prompt and the output may not match because of the highly complicated algorithms used to generate the art – and perhaps creating something more complex than what was envisioned by the original prompt.

While the quality of the art is a different debate, dynamic generative art has led to one outcome – it has made art creation more accessible, particularly for non-artists who nevertheless want to express their thoughts and feelings.

And now, we are seeing it manifest across the art, media, and entertainment industry.

Its impact can be felt everywhere – from art creations on the internet to strikes in Hollywood over the potential of AI in script creation.

However, generative AI art has certain limitations, and it may be a long time before it seriously evolves to take over the role of CGI artists and scriptwriters.

In this middle ground, dynamic generative art has multiple use cases, and its effects are being seen in industries.

Generative AI Art Use Cases

  • Video Games

AI can generate unique and intricate characters, landscapes, and objects such as cars or weapons – with a short turnaround time and good quality.

AI is already used in prominent games like FIFA 22 (machine learning), Red Dead Redemption 2 (NPC interactions), and Middle-Earth: Shadow of Mordor (also NPC interactions).

  • Film and Animation

AI can create unique and complex backgrounds, landscapes, characters, and scenes in movies and animations.

For example, Adobe After Effects, a prominent special effects software, uses a feature named Content-Aware Fill, an AI-driven feature to remove unwanted objects in movies or animations.

  • Fashion and Design

Fashion designers create designs that are thematic and unique. Let’s take, for example, a fashion designer creating a cloth styled after a Victorian-era pattern.

Generative models can then absorb the data to create their own thematic versions.

For example, the German fashion design platforms Zalando and Google used AI to generate Project Muze fashion designs.

  • Music and Audio

Feed the AI generative models some data on existing music, and it can quickly learn from the patterns and generate unique output.

For example, AI can produce songs or sounds that deeply resemble renowned artists.

One example is the Savages – AllttA track, comprising the French producer 20Syl and American rapper Mr. J Medeiros. Similarly, Oasis fans were treated to an uncanny experience of a “lost 90s album” – an AI-generated album created by fans.

Is This a Democratization of Art?

Before AI, art and entertainment were the exclusive domain of artists in the form of painters, musicians, and more. These were skills beyond the access of people outside the art, fashion, and entertainment industries.

But now AI has widened access to anyone who can think up a prompt. Now, you can generate complex art with a few words or descriptions.

But there are limitations of dynamic generative art.

Lack of Control

You don’t have precise control over what will be produced. Generative  AI art is governed by complex algorithms of generative art models and can produce annoyingly random outputs that don’t match your expectations.

It’s like a lottery – you provide input, but the algorithm produces something entirely different from your expectations.

Human artists don’t have the problem – they (often) know where they’re going.

Lack of Explanation

When you are not sure of what the generative AI art is going to produce exactly based on your inputs, how can you explain to someone the meaning behind the art?

Art is a complex creation with a layered and intricate meaning hiding within. An artist can create a picture depicting multiple emotions, feelings, and themes, and the artist can choose to explain the picture to their audience.

The same cannot be said from a tool output.

Reproducibility

Generative AI art can be extremely difficult to reproduce even if you provide the same prompts or inputs.

The algorithms can be extremely (annoyingly?) random, and it can be extremely difficult to reproduce the same patterns.

The Bottom Line

Generative AI art is reshaping the media, art, and entertainment industry – but it has serious limitations regarding reproducibility and reliability.

While fast production of images, videos, and audio may be exciting, there are currently serious limitations.

And (we like to think) whatever advances AI makes, the human brain is still miles ahead in terms of thinking, creativity, and complexity.

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Kaushik Pal

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…