February 13, 2024

How Generative AI Works and Why You Should Care


Generative AI is transforming the digital landscape.

This new technology has the potential to mimic human-quality outputs like blogs, images, and even sound and videos.

However, how do we know this isn’t just another tool for artists and writers? Why should businesses and entrepreneurs care about the output of these new models?

We stand on the brink of a new era in content and monetization, not just for tech enthusiasts but for anyone with a stake in digital marketing, business, and the creative landscape.

Understanding how generative AI works and how to monetize it will be a fundamental skill in 2024 and onwards. Here’s what you need to know.

Understanding Generative AI

At the core of how generative AI works is a singular goal: mimicking human creativity.

This is possible when operators feed large amounts of data, such as human-generated text, images, music, and more, into a machine-learning model.

Over time, and through human feedback, the model learns what makes human images feel real and how to consistently recreate them.

Generative AI vs. Other AI Technologies

The starkest difference is in the name: while other AI technologies use machine learning to learn and make predictions, they primarily identify and categorize information. On the other hand, generative AI uses its training to create completely new data instances.

Large Language Models (LLMs)

LLMs are one specific type of generative AI. The most well-known are generative pre-trained transformers (GPTs like ChatGPT). As the name implies, they are focused on understanding and generating human language.

LLMs undergo a process of pretraining, fine-tuning, and output generation.

Pretraining: In this phase, models are shown a large corpus of text without any specific task in mind. They use this to understand language patterns, grammar, and context.

Fine-Tuning: Models can then be fine-tuned on smaller, specific data sets.

Output Generation: When it’s time to generate text in response to an inquiry, LLMs calculate the probability of each word in a sentence. It can generate text that reflects the patterns and styles present in the training data.

How Generative AI Works Under the Hood

Understanding neural networks and deep learning, specifically Generative Adversarial Networks (GANs), is necessary to fully grasp how generative AI works.

Neural Networks

Neural networks are based on the biological networks that constitute animal brains, including humans, but with some major differences.

At their core, neural networks consist of layers of nodes, or "neurons," each receiving input, processing it, and passing on its output to the next layer.

  1.       Input Layer: This layer receives raw input data.
  2.       Hidden Layer: This is where computation is mainly performed. Through weighted connections, these layers extract features and learn complex patterns from data.
  3.       Output Layer: The final layer produces the output of the entire network.

Deep Learning

Deep learning uses neural networks with more layers than usual (hence the term “deep”).

These networks can understand and model more complex data patterns, making them ideal for tasks involving complex representations such as image and speech recognition, natural language processing (NLP), and generative AI.

Generative Adversarial Networks (GANs)

GANs consist of two neural networks working together. Or, more accurately, against one another.

These two networks are called the generator and the discriminator:

Generator: This network tries to generate indistinguishable data from real data and then shows it to the discriminator.

Discriminator: This network’s job is to distinguish between the real data (drawn from the training set) and the “fake” data generated by the Generator.

Real-World Applications

Generative AI is currently used in a range of applications like:

Image Synthesis: Applications include generating art, designing virtual environments, and modifying photographs.

Drug Discovery and Material Science: Leveraging generative models to predict molecule interactions can speed up the identification of new medicines and compounds.

Text Generation: Generative AI systems like ChatGPT can follow style guidelines and generate contextually relevant material.

Monetization: Applications like Revenue Engine can be used to seamlessly add monetized links to generated content, enhancing user experience and opening up new revenue streams for AI-driven applications of the future.

Benefits of Generative AI

In the world of content creation, the ability to generate new, high-quality images and text enables creators to focus on creating the best content possible.

Given how easy these tools make content creation, only the best will remain relevant. This process will force content creators to focus on innovative new content strategies facilitated by AI.

State-of-the-art chatbots, virtual assistants, synthetic data generation, and personalized healthcare could fundamentally alter fields like customer service, data science, and medicine.

We’re still in the early stages of this burgeoning field of technology, but one thing is certain: reducing the need for manual effort will allow humans to be more productive, reduce creativity costs, and explore brand-new avenues of research and development.

Why You Should Care

Generative AI can potentially transform not just individual careers but multiple industries. Everything from marketing to bio-engineering to advanced technology will soon be affected by generative AI and its ability to enable unique solutions and reduce the need for manual effort.

In business, the increase in efficiency and productivity means new opportunities for growth.

For the medical field, the ability to understand diseases and treatments at a more fundamental level could lead to mass increases in life expectancy and quality of life.

Regarding technology, generative AI’s ability to create unique new datasets could have applications in virtual reality, engineering, and innovation.

Though we’re just beginning to understand the full potential of generative AI, its development signifies a transformative era for global economic prosperity.

How People Are Using AI to Build Money-Making Products

Marketers who understand how generative AI works already use it to generate social media posts, search-engine-optimized sites and web content, detailed product descriptions, press releases, and even personalized email marketing campaigns.

Tools like Revenue Engine.ai allow creators to seamlessly include monetized links within AI-generated content whenever a product or brand is mentioned and create new revenue streams for these types of posts.

The applications aren’t just limited to market products. Businesses have begun to rely on AI chatbots as a reliable 24/7 customer service solution. These bots are also useful in the realms of sales development and product education.

Generative AI facilitates a wide array of products and services that can be monetized innovatively. Platforms like RevenueEngine.ai exemplify how businesses can leverage AI to open up new revenue streams, from content creation and chatbots to AI-enhanced products.

If you’re looking for a new way to monetize your AI-generated content, sign up for Revenue Engine for free today.