The most advanced generative AI models for text generation (LLM) use a specific neural network architecture known as a “Transformer” (more on this later). These models are initially pre-trained on large amounts of unsupervised text (learning to predict the next word in sentences, for example) and can then be fine-tuned for specific tasks.
Generative Artificial Intelligence has gained prominence and become the “hype of the moment” for several reasons, reflecting both technological advancements and innovative, practical applications. Here are some of the main factors contributing to its popularity:
Groundbreaking creative capabilities: Generative AI is capable of creating new and unique content, such as text, images, music, and videos, that are often indistinguishable from those created by humans. This ability to “create” rather than simply “analyze” or “predict” captures the imagination of audiences and businesses alike, suggesting panama whatsapp data a future where AI is not just an analytical tool but also a creative partner.
Significant advances in specific models: Some models have demonstrated extraordinary capabilities for generating coherent text and creative images from simple textual descriptions. The ability of these models to generate detailed and highly specific content has attracted significant media attention and commercial interest.
Compelling commercial applications: Companies are finding practical uses for generative AI that can transform industries. This includes everything from automating graphic design and content production to personalized applications in fashion, advertising, and entertainment. The ability to customize products and services at scale, without the costs associated with traditional human creation, is particularly appealing.
Accessibility and usability improvements: Platforms with increasingly user-friendly interfaces have made generative AI more accessible to developers and creatives without specialized training in AI or programming. This democratizes the power of generative AI, allowing a broader range of users to experiment and implement their own creative ideas.
Cultural and social impact: Generative AI has also sparked important debates about ethics, authorship and creativity. Questions about copyright, authenticity and the role of the machine in art and content creation stimulate public debates that increase the visibility and fascination of these technologies.
Technology evolution and investment: Continuous advancement in computational power and deep learning algorithms has enabled constant improvements in generative AI techniques. Furthermore, substantial investment in research and development by major technology players such as Google, OpenAI, and others is accelerating the development and application of these technologies.
3.2 Generative AI models
3.2.1 LLMS, NLP and generative AI
LLMs (Large Language Models) are actually the intersection between natural language processing (NLP) and generative AI (GenAI). In NLP, the emphasis is on understanding: understanding, interpreting, and analyzing human language. This includes tasks such as:
Speech recognition , where the goal is to convert speech into machine-understandable text.
Sentiment analysis , which consists of determining the attitude or emotion expressed in a text.
Information extraction , such as identifying specific people, places, dates, and other data in a text.
Language comprehension , where the model evaluates and interprets the meaning of the text to answer questions or provide ideas.
At IAGen, the emphasis is on “creation” – generating new textual content in such a way that it is not just a repetition of memorized information, but a creative recombination of acquired knowledge that can result in something new and original. This includes tasks such as:
Text generation , where the model produces completely new content, such as articles, stories, or conversations.
Machine translation , although a transformation process, involves the creation of text in a new language that reflects the meaning of the original.
Summary , which creates a shorter, more concise version of an existing text, highlighting its main points.
It should therefore be noted that the application of an LLM-type model for fluid interactions with human beings simultaneously presupposes two complementary approaches: the use of Natural Language Processing (NLP) to ensure the understanding of human language in the interaction, followed by Generative AI (AI Gen), capable of creating content or responses that are creative, contextualized and relevant to human understanding.
3.2.2 GANS
GANs are a specific type of generative AI model. They are composed of two neural networks that compete against each other in a theoretical game:
Generator: This network is responsible for generating new data. The purpose of the generator is to create fake data that cannot be distinguished from real data.
Discriminator: This network acts as a critic or judge. Its function is to distinguish between real data (true data from the training set) and fake data produced by the generator.
The training process of a GAN is a kind of game between the generator and the discriminator. The generator tries to “fool” the discriminator by producing increasingly plausible data, while the discriminator learns to better detect fakes. This process continues until the generator becomes so good at simulating real data that the discriminator can no longer effectively distinguish between the real and the fake.
Artistic image generation: Create new images that mimic the styles of famous paintings or generate human faces that don't exist.
Image Enhancement: Increases the resolution of low quality images.
Fashion Modeling: Create new clothing designs or try on clothes automatically on virtual bodies.
Simulations: Generate training data for other neural networks, especially in scenarios where real data is sparse or difficult to collect.
3.2.3 Other models
Apart from GANs themselves, below are some other generative AI models that are also widely used in different applications:
VAE (Variational Autoencoder Models): for tasks where the generation of new samples of continuous data is necessary (imaging applications and music modeling);
MGAN (Memory Generative Adversarial Networks): allows you to store examples of training data and use this knowledge to generate new, more consistent and high-quality data (medical and scientific applications);
DBN (Deep Belief Neural Networks): multiple layers trained in an unsupervised process, followed by supervised tuning (applications for pattern recognition and classification, to generate new examples);
Generative Transformer Model: Based on the “Transformer” architecture, they are able to predict the next “token” in a sequence, such as the next word in a sentence (applications for completing search messages in search engines, creating articles, chatbots and timing code).
3.3 Transformer Architecture (and why it is so important)
The term “transformer” refers to a machine learning model architecture that was introduced in 2017 by Vaswani et al. in the paper titled “Attention is all you need”.
This architecture has since revolutionized the field of natural language processing (NLP) and has become the basis for most LLMs, such as OpenAI’s GPT (Generative Pretrained Transformer), Google’s BERT (Bidirectional Encoder Representations from Transformers), and many others.
The transformer architecture has enabled significant advances in the quality and efficiency of NLP models, making it possible to train very large models that can generalize well from one set of NLP tasks to another. This generalist capability is part of what makes transformer-based LLMs so powerful and versatile across a variety of NLP applications.
3.4 Multimodal generative AI
Multimodal generative AI is an advanced concept in the field of Artificial Intelligence that refers to the ability of an AI system to understand and generate content in more than one type of media modality. These modalities can include text, image, sound, video, and other types of sensory data.
The technical challenges are large. Developing robust methods to analyze and synthesize information from different data modalities is challenging due to the complexity and variability of the information that each modality can contain. Training models requires large multimodally annotated data sets, which can be difficult and expensive to obtain. In addition, multimodal systems can require significantly more processing and storage resources.
Today, all generative AI solutions converge on a multimodal approach, practically becoming the premise of any provider. The goal of multimodal generative AI is to create systems that not only understand each type of media in isolation, but are also able to integrate and correlate information between different modalities in a coherent and useful way. Its main characteristics are:
Integration of modalities: Multimodal systems are capable of processing and relating information from multiple sensory sources or data types at the same time. For example, a system can analyze both the text and images in a document to better understand the content and context.
Cross-modal content generation: A cross-modal system can generate one modality of content from another. For example, it can create a textual description of an image (a task known as captioning) or generate an image from a textual description.
Data enrichment: These systems can enrich one type of data with information derived from another. For example, improving the understanding of a video by adding textual metadata extracted from the audio and image.