The generative artificial intelligence (generative AI) is a branch of artificial intelligence designed to create original and new content from text to images, sounds, videos, code and more. It is based on advanced deep learning models, such as generative adversarial neural networks (GANs) and and large language models (LLMs, for its acronym in English) trained to analyze large amounts of data and generate results that mimic human patterns. That is, from data it has been trained on, it can solve problems and provide solutions.
What is Generative AI for?
This advanced branch of artificial intelligence (AI) is driven by machine learning models capable of generating results that previously required exclusively human creativity. In contrast to traditional AI, which classifies or predicts, generative AI creates new content such as:
- Text (articles, dialogues, stories). The LLM as GPT-4 specialize in generating coherent text.
- Images (illustrations, designs, digital art). The GANs are used to create hyper-realistic images and videos.
- Audio (music, voice recreation).
- Videos (animations, visual effects).
- Code (automatic programming).
In short, this technology not only analyzes the world, but also recreates it in an innovative way. It not only interprets data, but uses it to create new content with applications in a wide variety of industries.
Generative AI applied to different sectors
The Generative AI has a significant impact on a variety of industries thanks to its ability to create unique content. Here are some outstanding examples:
Marketing and Advertising
- Generation of personalized advertising campaigns.
- Automatic creation of promotional images and videos.
- SEO optimized content writing.
Medicine and Health
- Creation of AI-assisted medical reports.
- Analysis of diagnostic images such as ultrasound or X-rays.
- Protein modeling for pharmacological treatments.
- Rare disease studies and clinical trials.
- Creation of 3D models for surgical planning.
Entertainment
- Production of scripts for film and television.
- Generation of characters and virtual worlds in video games.
- Creation of original music.
Education
- Generation of customized educational materials, such as exercises and exams.
- Automatic summaries of long texts.
- Creation of interactive experiences with AI.
E-commerce
- Generation of product descriptions.
- Virtual assistants to answer frequently asked questions.
- Creation of personalized purchase recommendations.
Banking and financing
- Fraud detection.
- Chatbots to answer your users’ questions.
- Accelerate the loan approval process.
- Be up to date with all your company’s regulations.
- Automation of document analysis.
- Preparation of reports for audits.
This is just a breakdown of what generative AI can do for you, your business or your company. Platforms such as Serenity Star provides training and advice to users who wish to start implementing AI in their company.
Start by creating your AI agent
How Generative AI works
The core of generative AI lies in two major concepts:
- Model training The system learns from huge data sets. For example, a language model learns writing patterns by analyzing millions of texts.
- Content generation Once trained, the model can generate content upon receiving a “prompt”. But what is a prompt?
What is a Prompt in Generative AI?
A prompt is an instruction that the user provides to the generative AI model to produce a result. It can be a sentence, a question, or a detailed description. For example:
- To generate text: “Write an article about the benefits of renewable energy.”
- To generate images: “Create an illustration of a futuristic landscape with mountains and lakes.”
Well-structured prompts are key to obtaining accurate and personalized results.
But to understand more deeply how generative AI works, it is essential to to know the technologies that make it possible.
Generative AI models
These generative AI models are the programs through which Artificial Intelligence operates, collecting data to detect patterns and perform tasks for which they have been trained. The basic models are:
Deep learning models
Generative AI uses deep learning algorithms trained on large amounts of data. The models learn to identify patterns, structures and relationships in this data and then reproduce similar content.
Example:
A model trained with millions of landscape images can learn to generate new landscape images that have never existed before.
Large Language Models (LLMs)
The LLMs GPT-4 models, such as GPT-4, are specialized models for processing and generating text. Their operation is based on:
- Massive training with texts from multiple sources (books, articles, forums, etc.).
- Contextual prediction The model predicts the next word or phrase based on the context provided by the user (prompt).
Example:
An LLM can generate an entire essay from a prompt such as: “Write about the impact of AI on education.”
Generative Adversarial Networks (GANs)
GANs are another key pillar of generative AI. These networks work through the interaction between two models:
- Generator: Creates content.
- Discriminator: Evaluates whether the content is real or AI-generated.
This competitive process enables the generator to improve with each interaction, producing incredibly realistic results.
Example:
With GANs, images of human faces can be generated that look like real photographs, even though those people do not exist.
Multimodal Artificial Intelligence
A recent development is the multimodal AI which combines multiple data types (text, images, audio) in a single model. Examples such as DALL-E allow you to generate images from textual descriptions.
Example:
Prompt: “A robot walking on a beach at sunset.”
Result: An image that displays exactly that description.
Fine-Tuning and adaptation
Generative models can be customized for specific tasks through a process called fine-tuning, which involves fitting a pre-trained model with additional data relevant to a particular case.
Example:
A generic model such as GPT-4 can be adjusted to generate legal contracts or medical diagnoses with specialized training.
More advanced Generative AI models
Some of today’s most prominent models include:
- GPT-4 (OpenAI): Ideal for text generation.
- DALL-E 3 (OpenAI): Generates realistic images from text.
- Stable DiffusionStable Diffusion: Specializing in images.
- Serenity Star AI Hub: Allows you to create AI agents that combine text generation, images and more, in an integrated environment.
Advantages of using Generative AI in your business
AI should not be seen as an enemy, with the belief that it will take away jobs, but as a tool (which it is) that will complement the performance of repetitive tasks across different departments of a company.
When implemented in a company, AI can increase worker productivity by saving time and resources in tasks such as design, drafting, reporting, code creation, programming, and translations. Additionally, it allows for the automation of large-scale tasks and the customization of actions by training specific agents for each sector.
However, there are many other advantages that AI will offer in the future, some of which we can’t yet fully anticipate. AI is advancing rapidly, and we will all have the opportunity to witness its impact.
Generative AI, future and challenges
Generative artificial intelligence is ushering in an era of transformation across industries.
Many official bodies, such as UNESCO, have commented that in the field of education, AI will be competing with human intelligence within five years. However, we must strive to use it to address existing inequalities between countries in knowledge, research, and education. The goal is to ensure that AI is accessible to everyone, regardless of their cultural background, so that all can benefit from the technological revolution.
Likewise, UNESCO is committed to supporting its member countries in harnessing their potential to achieve the Education 2030 Agenda.
The UN, for its part, sees the need for AI regulation to protect data privacy, a major challenge in the technological revolution.
Moreover, studies already predict that AI could help save 5.4% of GDP in Latin America, 14.5% in North America, and 8% of GDP in Spain over this decade.
Generative AI will continue to evolve, fueled by advances in large language models (LLMs) and techniques such as multimodal artificial intelligence which combines text, images and other formats in a single model. Its impact on sectors such as healthcare, commerce, entertainment and education will be transformational.
If you are looking to innovate in your industry, investing in generative AI could be the step you need.
