Generative AI: what it is, how it works, and how to use it

Artificial intelligence is no longer something in the future and has entered our everyday lives. From mobility apps to virtual assistants, it is already used to organize calendars, forecast trends, and even recommend online buys. But in recent years, a new kind of AI has started to make waves: the kind that generates.

Have you ever read something and wondered if it was written by a human or a computer? Have you ever watched a video that seemed real but was generated artificially? If so, then you’ve already seen generative AI.

This is a major step ahead in the development of artificial intelligence. It does not just analyze or classify information but can create new, original, and even creative content based on patterns it has learned.

In this article, you will know how generative AI works, its real applications, advantages, and disadvantages, and find out how your business can implement it strategically and securely.

What is generative AI?

Generative AI is a form of artificial intelligence that can produce new content like text, images, videos, audio, code, and even 3D models—based on patterns that it has learned from massive amounts of data.

That is, aside from identifying and understanding data like the conventional AI, it is also able to produce something new and unique based on what it has learned.

That is, rather than simply labeling an image as “cat” or “dog,” say, generative AI can generate a new image of an imaginary creature by combining characteristics learned from thousands of actual images.

Likewise, you can generate a new paragraph written in the style of a particular writer, write a melody, or propose lines of code for a computer program.

This capability is driven by models like Large Language Models (LLMs) and generative neural networks like Generative Adversarial Networks (GANs), which learn from massive datasets and produce results that often resemble—or surpass—those produced by humans.

Later in this article, you’ll better understand how generative AI works. But first, how about learning how to differentiate between the different types of artificial intelligence?

What are the types of AI?

In order to grasp generative AI, one should comprehend the fundamental types of artificial intelligence and how they vary:

• Symbolic AI: It is the most conventional model, reliant upon explicated rules as determined by human designers. It is applied in expert systems that obey “if this, then that” rules;
• Predictive or conventional AI : applies machine learning algorithm to forecast outputs from patterns of historical data. Examples are film suggestion algorithms or credit screening. It identifies patterns but does not produce new material;
• Generative AI : transcends prediction and analysis and has the capability of producing new material independently. It’s more sophisticated in creative terms and forms the foundation for models such as ChatGPT.

How is AI different from generative AI?

Whereas legacy AI looks at existing data to analyze, classify, or predict, generative AI can create something new, a new paradigm in the development of artificial intelligence.

And how does predictive AI differ from generative AI?

Predictive AI looks for patterns to forecast future actions or results. Generative AI takes it a step further and uses these patterns to generate new content.

How is generative AI done?

Generative AI employs deep learning methods, such as artificial neural networks. These models are trained on vast amounts of data (text, images, code, etc.) and, with the passage of time, absorb the structures, styles, and patterns within these materials.

Two of the key mechanisms that enable such operation are:

generative neural networks (e.g., GANs) : consist of two networks that work in harmony — one creates content and the other checks if it appears real, fine-tuning the process until the output is believable;
large-scale language models (LLMs) : like GPT (Generative Pre-trained Transformer), are models trained to predict the next word in a sentence, hence able to generate coherent and contextual text.

In addition, generative AI can be optimized using reinforcement learning, supervised tuning, and even human feedback—as is the case with user-preference learning tools like ChatGPT.

Examples of generative AI available in the market

Generative AI is used in a number of popular offerings and features in business tools used by many professionals on a daily basis. Some of the popular ones include:

• ChatGPT (OpenAI) : creates text, responds to questions, assists with summarization, translation and content generation;
• DALL•E (OpenAI) : Generates images based on textual descriptions, which have a lot of detail and creativity;
• Midjourney : Joint software that creates art and conception images based on prompts;
• GitHub Copilot (Microsoft) : Automatically provides lines of code in your programming context;
• Runway : Generates and edits description-based video, including auto-cuts, scene generation, and transitions;
• Descript : Transcribes and edits videos based on text to make it easy to produce audiovisual content.

These offerings showcase the ability of generative AI to accelerate, transform, and enable work in various industries. That’s why it’s already being embraced, in many cases directly affecting entire industries.

In education, for instance, generative AI writes personalized lesson plans, tailors content to varying levels of learning, and creates interactive content.

In the healthcare sector, it helps create medical reports, interpret tests, and even simulate procedures. This speeds up the diagnosis process and enhances doctor-patient communication.

In finance, generative AI is already employed to generate management reports, generate performance summaries, and even describe intricate data in natural language, making financial analysis simpler for a range of professionals.

And in advertising, it inspires creativity by creating advertising copy, scripts, slogans, and campaign concepts. Generative AI is also employed to test various content versions based on behavior data, enabling personalization at scale.

In short, generative AI is already creating tangible value in various realities and has the possibility of reinventing even more as it advances and democratizes.

And staying on top of this trend is crucial for companies that need to stay ahead of the game in a market because is more and more data-driven, agile, and personalized!