What Is Generative AI? How It Works and How It's Used

What is generative AI? Since the boom of ChatGPT, released in 2022, that term has caught the attention of many people interested in technology development. ChatGPT is one of the many applications of generative AI.

Generative AI creates different types of content, such as written texts, audio, and images. It has been explored in several fields, including arts, marketing, and design. Check out the article to learn more about it.

What Is Generative AI?#

Artificial intelligence is a technology that enables machines to mimic human intelligence to perform specific tasks. Generative AI uses this process to create new content, generating texts, images, and sounds.

ChatGPT uses generative AI when you ask it to create a love poem or the lyrics of a song. Graphic design apps also use generative AI to generate new images based on what you describe. Another example is when you take a selfie, upload it to an image edition app, and ask it to create an illustration or a painting in the Renaissance style.

Generative AI is also implemented when you talk with a chatbot, for example through the JivoChat app. Natural language processing enables large language models to understand and formulate answers as close as possible to human behavior.

The generative AI learns based on the data it receives, therefore it's able to learn how to create not only texts but other types of content. It can be trained to learn biology, chemistry, math, grammar, and many other subjects to solve real-world problems.

How Generative AI Works#

To answer the question "What is generative AI?"It's important to comprehend how this technology works. It uses machine learning models that are trained by enormous amounts of data to be able to generate content. You will provide a specific type of data to a generative AI based on what you want it to generate.

The generative AI model identifies patterns and structures existent in the provided data and then uses it to generate new and original content. Through that process, the artificial intelligence creates foundation models capable of performing multiple tasks, such as Chat-GPT-3, and Stable Diffusion.

Large language models such as Chat-GPT are trained to perform language-based tasks like information extraction, writing, and summarizing texts. While the foundation model Stable Diffusion creates images from texts and image prompts.

Generative AI Models#

Diffusion Models#

Diffusion models start by making small changes in the original data. It changes the data progressively, so it looks less and less similar to the initial sample. These modifications are controlled to ensure the content created is coherent and realistic.

For instance, you can give an AI image generator a picture of the Statue of Liberty, and ask it to recreate it using Van Gogh's painting style,

Generative Adversarial Networks (GAN)#

The GAN model trains two neural networks. The first one, called the generator, creates fake data samples. The second one, known as the discriminator, is responsible for identifying the real data and the fake data made by the generator.

The process continues until the generator becomes so good at creating data that the discriminator can't tell which one is real or fake. This model is used in realistic image generation, style transition, and data augmentation tasks.

Variational Autoencoders (VAEs)#

Variational autoencoders learn using a mathematical representation of the data called latent space. A single code represents the data based on all its attributes. It has two neural networks, the encoder and the decoder.

Generative AI Use Cases#

Language#

Source: Chat-GPT

When the subject is "what is generative AI?", it isn't possible to not mention applications such as Chat-GPT. Large language models are commonly used to perform tasks such as generating captions, translating texts, and writing essays.

Audio#

Some generative AI models are capable of creating songs, snippets of audio with text inputs, and custom music. They have been used to create soundtracks for videos, for example.

Visual#

Another common use for generative AI is image creation. You can find several image generators online that are AI-powered and enable you to make new images based on a text description with just a few clicks. It's possible to generate realistic images for augmented reality, 3D models, and logos, and edit existing pictures too.

Marketing#

The marketing industry is also benefiting from generative AI models by using it to create content for blogs, offer customer support via chatbots, and edit and create images for advertising purposes.

Generative AI Benefits#

Enhanced Creativity#

Generative AI allows professionals from different industries to explore their creativity by using the new possibilities artificial intelligence offers. For example, a visual artist or graphic designer can create multiple versions of an image they envisioned just by describing it to the AI. They can test different options and put their ideas into practice with just a few clicks.

Faster Decision-Making Process#

Another advantage of using generative AI models is its ability to analyze large amounts of data in a few seconds. You can provide it with multiple research and make more accurate decisions based on data analysis, which diminishes the risk of error.

Constant Availability#

With AI solutions like chatbots, you can offer 24/7 customer support. This way your clients can have their demands solved whenever they need, which helps to improve customer satisfaction, and increases the retention rate.

Dynamic Personalization#

Generative AI can use history and user preferences to create a complete personalized experience when someone accesses your website or app, for example. This helps to improve user experience.

Generative AI Limitations and Risks#

AI Hallucinations#

AI hallucinations is the term used to describe when the AI system gives an inaccurate answer. To prevent this problem, some developers restrict artificial intelligence to use only trusted data sources. Continued evaluation and tuning can also help to reduce inaccuracy.

Bias#

Since generative AI models learn from the data they are provided with, they can develop results with the same biases present in the data source. To avoid this problem, developers must use diversified training data, create guidelines, and continuously evaluate the AI models.

Threats to Security#

Unfortunately, with the development of generative AI models, a new challenge has arisen: how to prevent people from using artificial intelligence to create malicious content such as AI-generated or manipulated videos and images. Known as deep fakes, they are often created to spread misinformation and damage someone's reputation.

Discover More About Generative AI#

Now that you know the answer to "What is generative AI?", what about start exploring its models? Maybe you already use some of them at work, or you can find AI-powered tools that will help you enhance productivity and get better results. It's worth discovering how AI has been used in your industry sector to keep updated with the latest trends in the market.