How Generative Adversarial Networks (GANs) are redefining content creation

September 4, 2023
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AI & ML Insights
Mario Grunitz
How Generative Adversarial Networks (GANs) are redefining content creation

As artificial intelligence (AI) and the technologies aiding its growth evolve, so does the excitement surrounding its potential applications. Currently, the spotlight is placed on AI-generated content thanks to the meteoric rise in the popularity of text and image generators like ChatGPT and DALL-E. 

These tools give users the ability to develop creative content in seconds, helping to create websites, videos, blogs, music and more from simple text prompts. But what kind of technological wizardry is responsible for powering these incredibly useful tools? 

Generative Adversarial Networks (GANs) are the binary neurons that drive generative AI, or GenAI. Let’s take a look at how GANs are reshaping industries and exploding the boundaries of digital creativity.

What are GANs? 

GANs are a form of deep learning architecture consisting of two neural networks designed to compete against each other in a zero-sum framework (hence the term adversarial) to generate new data. 

How do GANs work?

The two competing networks are known as the generator and discriminator. The generator is responsible for creating fake examples of data such as text or an image. The discriminator is tasked with distinguishing which examples it receives are real or fake. 

This competing dynamic drives the learning process, with improvements to both achieved with each iteration. The process continues several times until the discriminator is unable to discern whether the data it is given comes from the real training data or the generator. This means the generator has produced content so realistic that it fools the discriminator.

The result? GANs produce incredibly lifelike and unique images that are creating new art forms in seconds. However, this process requires intense computational power to train GANs and generate their content. This power comes at a financial expense as the process is slow, especially for generating high-quality images and videos. Additionally, GANs can be difficult to train and pose risks of instability of model collapse and failure to converge.

How GANs are used for content creation

There are many exciting applications of GANs in digital content creation that are fueling our collective creativity like never before. 

For example, GAN-powered image synthesis seamlessly generates realistic images of human faces, landscapes – pretty much anything and everything. Image editing, blending, face ageing, inpainting, etc. mean that creators can access any style and subject in an image for endless creative possibilities.

GenAI videos bring imagined scenarios to life through a simple prompt, exploding creativity to push the boundaries of film to visually present any conceivable idea. Think of the possibilities of user-created videos or films created without large budgets or actors. In film production, GANs can generate entire scenes and sequences, effectively redefining the limits of video-based storytelling. 

Video game designers can also use GANs to create lifelike characters and immersive environments to develop almost endless ranges of realistic interactive experiences.

Consider the impact on digital marketing, where GANs can produce high-quality creative content such as images and videos for ad campaigns, ultimately removing the need for expensive production shoots. 

Ethical considerations of GAN content

While GANs are ushering in unparalleled creativity to every netizen, they also possess some critical ethical questions and concerns that need to be addressed. 

A concerning example is the existence of deepfakes – AI-generated videos and images that swap faces or voices of people. While this technology is predominantly used for entertainment, it has the alarming potential to spread misinformation or cause harm. Imagine the real ramifications of a video emerging on the internet of what appears to be a powerful political leader inciting violence or war. 

Deefakes also pose the threat of identity fraud and theft, as well as a growing array of ways to cause harm to people and societies from the distribution of fake news and nefarious content.

Moreover, the growing issue of copyright and ownership disputes over AI-generated content is proving to already be a complex challenge to navigate. GAN-powered GenAI content uses existing content created by humans to generate work. This results in the boundaries of intellectual property becoming blurred – who owns AI-generated content? Creators of the content, creators of the algorithms, or the algorithms themselves?

Currently, laws in the USA and UK state that only humans are able to claim ownership of content, leaving copyright issues regarding AI-generated unresolved at this stage.

Addressing these ethical challenges requires consolidated efforts from governments and oversight institutions. As technology advances rapidly, regulations and laws must match the pace to be consistently updated and reviewed to tackle the current and future nuances of AI-generated content.

The future of GAN-powered content

The future of GANs is certainly paved with exciting prospects for both creative content creation and our collective imaginations. As technology advances, new architectures and techniques will continue to emerge, promising results beyond our current expectations. 

But what does this mean for the content creation industry, and for human creativity itself? We’re living in a time where creative output that used to be the result of teams of individuals working together in complex production processes can now be replicated by a single user using only a computer and text prompt. 

Does the resulting content lack what would be traditionally deemed as ‘creative essence’ because humans aren’t as involved? Or will we see the rise of a new form of art powered by algorithms, detached from the nuances of human creativity?

Final thoughts

A brighter perspective could be that GAN-powered GenAI content and human-made creations will serve each other through an adversarial dynamic – generator and discriminator – to inspire the other’s progress and proficiency. 

After all, GenAI uses man-made content to generate its own. But will it begin to flow both ways in the future and push the boundaries of creativity far beyond our current expectations of algorithms and biological neurons?

We’ll have to wait and see.

Mario Grunitz

Mario is a Strategy Lead and Co-founder of WeAreBrain, bringing over 20 years of rich and diverse experience in the technology sector. His passion for creating meaningful change through technology has positioned him as a thought leader and trusted advisor in the tech community, pushing the boundaries of digital innovation and shaping the future of AI.

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