The Increasing Influence of Artificial Intelligence in Creativity - ParrotGPT
creative AI

We humans like to think we’re the only beings capable of creativity, but computers have been used as a generative force for decades, creating original pieces of writing, art, music, and design. This digital renaissance, powered by advancements in artificial intelligence and machine learning, has ushered in a new era where technology not only replicates but also innovates, blurring the lines between human and machine creativity. From algorithms that compose symphonies to software that drafts novels, the scope of computer-generated creativity is expanding, challenging our preconceived notions of artistry and originality.

A Brief Look Into the History of Creative AI

Generative Adversarial Networks (GANs) for image generation were introduced in 2014. Then in 2016, DeepMind introduced WaveNet and audio generation. Next year, the Google research team suggested the Transformer architecture for text understanding and generation, and it became the basis for all the large language models we know today.

The research advancements quickly transformed into practical applications. In 2015, engineer and creative storyteller Samim trained a neural network on 14 million lines of passages from romance novels and asked the model to generate original stories based on new images.

neural storryteller
Image from “Generating Stories from Images” (2015) by Samim Winiger

A year later, Flow Machines, a division of Sony, used an AI system trained on Beatles songs to generate their own hit, “Daddy’s Car,” which eerily resembles the musical style of the hit British rock group. They did the same with Bach music and were able to fool human evaluators, who had trouble differentiating between real Bach compositions and AI-generated imitations.

Then, in 2017, Autodesk, the leading producer of computer-aided design (CAD) software for industrial design, released Dreamcatcher, a program that generates thousands of possible design permutations based on initial constraints set by engineers. Dreamcatcher has produced bizarre yet highly effective designs that challenge traditional manufacturing assumptions and exceed what human designers can manually ideate.

Autodesk Dreamcatcher
Image from Autodesk Dreamcatcher, reprinted with permission

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AI Text Generation

The recent advent of generative AI has sparked a renaissance in computational creativity. OpenAI’s ChatGPT has become probably the most widely-known example of the AI’s text generative power, but it has many strong competitors, including Anthropic’s Claude, Google’s Gemini, Meta’s Llama, and others.

These large language models (LLMs) possess the ability to craft text on virtually any subject, all while reflecting a tailored writing style. For example, imagine we task ChatGPT with writing a piece about artificial intelligence’s worldwide domination through authoring books, crafting images, and generating code – all in the dramatic style of a poetry slam. The resulting creation is quite impressive.

creative AI

While this serves as a playful illustration, the potential applications of LLMs go well beyond simple entertainment:

  • Marketing teams are already tapping into the creative power of ChatGPT and similar models to craft captivating stories, blog posts, social media content, and advertisements that echo a brand’s unique voice.
  • Customer support teams utilize LLM-powered bots to offer round-the-clock assistance to their customers.
  • In software development, new AI-assisted engineering workflows are taking shape, powered by generative AI coding tools. These tools offer code suggestions and complete functions, drawing on natural language prompts and existing codebases.

However, LLM-based applications are full of their pitfalls. Their performance can be erratic, leading to instances of ‘hallucination.’ Several notable incidents have occurred where companies were forced to honor a refund policy fabricated by their chatbot or users were able to trick the chatbot into selling them a car for $1. At this juncture, it’s imperative to consider these risks and, in high-stakes situations, to incorporate human oversight into the process. Yet, it’s clear that this technology is already significantly influencing business processes, with its impact set to increase further.

AI Image Generation

While large language models are revolutionizing the field of text generation, providing novel tools and challenges to writers, diffusion models are making waves in the world of art and design.

Tools like Midjourney, Stable Diffusion by Stability AI, and DALL-E 3 by OpenAI can generate images so realistic they could be mistaken for actual photographs.

Midjourney creative AI

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