What is Generative AI and Why is it So Popular?

What do people mean when they talk about "generative AI" and why do these systems seem to be finding their way into practically every imaginable application?

Definition of Generative Artificial Intelligence

Generative Artificial Intelligence (or Generative AI) is a field of artificial intelligence that focuses on developing models capable of generating new data, images, texts, or other types of content, rather than just analyzing or classifying them.

These models often use generative neural networks, such as generative adversarial networks (GAN) or generative autoregressive networks (GAN), which can learn complex patterns in the training data and generate new ones.

These can be applied to different types of data, such as images, text, audio, and more.

For example, they can be trained on the artistic works of a famous painter to generate new paintings that reflect the artist's style.

The use of generative models has seen significant advancement with the advent of deep learning, which has allowed handling more complex data and obtaining more accurate and realistic results.

In 2017, Google researchers introduced the transformer architecture, which was used to develop large-scale language models, such as those powering ChatGPT.

In natural language processing, a transformer encodes each word in a text corpus as a token and then generates an attention map, capturing the relationships of each token with all other tokens.

How Generative Artificial Intelligence Works

Before Generative Artificial Intelligence (GAI) became popular in recent years, artificial intelligence was mainly known for creating models that made predictions based on data.

For example, it could predict if a radiograph showed a tumor or if someone would have trouble repaying a loan; this type of AI learned from millions of examples.

Generative artificial intelligence, on the other hand, is a bit different; it's like an AI artist: it learns from a lot of data and then creates something new, like texts or images, that resemble what it has learned.

Although it may seem like a novelty, the technology behind GAI is not so recent; it is based on research and progress made more than 50 years ago.

A simple example of generative AI is the Markov chain, a method invented over a century ago.

It was used for things like suggesting the next word while writing an email, but it was not advanced enough to create texts that sounded realistic.

Today, models like ChatGPT are much more advanced, working similarly to Markov chains but being much larger and more complex, with billions of parameters.

They have been trained on huge amounts of text from the internet, which helps them better understand word and phrase sequences and make predictions about what will come next in a text.

In essence, today's generative AI is like a supercharged version of old models, capable of creating much more complex and interesting things:

generative artificial intelligence mimics human creativity, producing high-quality content (presumably material that did not exist, but which, in practice, could be equivalent to reformulated or re-proposed information): text, images, answers to questions, videos, songs, report summaries, diagrams, poems, marketing texts, webinars, essays, computer code, and much more.

It is based on complex algorithms like neural networks, which mimic the functioning of the human brain.

These systems are trained on large datasets to "learn" patterns and styles, allowing them to generate novel content.

The Main Types of Generative Artificial Intelligence

For example, OpenAI's DALL-E is a brilliant example of how AI can be used to create images.

This revolutionary technology has the ability to generate unique images from a text description.

Furthermore, AI has been used to create music. Jukebox, also developed by OpenAI, is an incredible demonstration of how artificial intelligence can be used to generate original songs, imitating the style of various artists and musical genres.

AI has also been used to produce programming code.

GitHub Copilot is an example of how artificial intelligence can help programmers write code faster and with fewer errors.

And by 2032, the generative AI market is expected to reach $191 billion.

Interest has grown exponentially, partly thanks to innovations such as AI-generated art and the use of these technologies in educational environments to assist in teaching and learning.

The Benefits of Generative Artificial Intelligence

It is also emerging as a powerful tool for personalizing user experiences in digital services, proposing significant improvements in terms of efficiency and creativity.

In product design, AI can quickly generate different concepts, speeding up the development process and offering new creative perspectives.

It is also emerging as a fundamental tool for personalizing user experience in digital services, such as e-commerce and streaming platforms, where AI can suggest products or content based on individual preferences.

Emerging Trends in Generative Artificial Intelligence

For example, with the help of AI, we can optimize the comfort of our living spaces, increase the level of security, and promote energy efficiency.

This revolutionary technology has the potential to automatically manage indoor temperature, provide more sophisticated security systems, and more effectively manage energy use, contributing to a more sustainable future.

In the world of marketing and finance, Generative Artificial Intelligence has a significant impact; companies are leveraging GAI's ability to analyze and predict market trends.

This provides companies with a competitive advantage, offering a more detailed and anticipatory view of market dynamics.

As a result, strategies can be formulated with a deeper understanding of the market, leading to more informed business decisions and more effective marketing strategies.

The Importance of Understanding Generative Artificial Intelligence

Understanding it is essential for using it responsibly and harnessing its benefits.

This involves addressing ethical and social issues, such as the accountability of decisions made by AI, privacy protection, and fairness.

Public education and informed regulation are essential for navigating this new technological landscape in an ethical and sustainable manner.

We at Colibryx deeply understand the importance and potential of Generative Artificial Intelligence.

If you want to learn more about how GAI can transform your business or projects, feel free to contact us.

With our experience and expertise, we are ready to guide you in exploring these revolutionary technologies.