Generative AI Models Types, Training and Evaluation Strategy

Generative artificial intelligence (GenAI) models are robust AI frameworks capable of producing diverse outputs using extensive training datasets, complex neural networks, deep learning structures, and prompts from users. The specific outputs depend on the nature of the generative AI models being utilized, which can include generating images, converting text into visual representations, producing original content, and creating artificial data. The emergence of new AI companies, artificial intelligence services, and GenAI models are positively influencing various industries.

This article will cover all the necessary information about GenAI models, types of Gen AI, their training processes, advantages, AI vs GenAI, what type of deep learning algorithms are used by generative AI, and the most suitable applications for your business. Let’s begin!

What are Generative AI Models?

Generative AI models are a subset of artificial intelligence that focuses on producing new, realistic data based on the patterns and structures they learn from existing datasets. Unlike traditional AI, which primarily recognizes patterns or predicts outcomes, generative AI techniques can generate entirely new and unique outputs, such as images resembling real-world scenes or human-like conversational text.

The different types of AI models are powered by deep learning algorithms, particularly those designed for unsupervised and partially supervised learning. Their applications span various industries, making them a core offering in the IT solution company landscape.

Generative AI Types

Understanding what are the two main types of generative AI models helps lay the foundation for their applications. Broadly, types of AI models can be classified into two categories:

1.    Implicit Generative Models

Implicit models do not directly estimate the probability distribution of data. Instead, they generate new data samples by learning the underlying patterns of the dataset.

2.    Explicit Generative Models

Explicit models estimate the probability distribution of the data explicitly, either through parametric or non-parametric methods.

Types of Generative AI Models

To better understand the capabilities of generative AI, here are the different AI models examples and how they work:

Generative Adversarial Networks (GANs)

GANs consist of two neural networks, a generator and a discriminator, that work together.  The generator creates data samples, while the discriminator evaluates their authenticity. Over time, the generator improves its ability to create realistic data.

Variational Autoencoders (VAEs)

These types of AI generative models are probabilistic models that encode data into a latent space representation and then decode it back to reconstruct or generate new data. This approach is effective for generating diverse outputs.

Autoregressive Models

These models generate data element by element. Based on past data, this type of AI model decodes the pattern among the elements and predicts the next pattern.

Training Generative AI Models

The process by which generative AI models are trained is quite complex. Here’s a step-by-step overview of the process:

Selection of Model Architecture

The architecture of a model is its basic structure which allows you to dictate how the model acquires knowledge from the given data and produces fresh material. The decision regarding the model’s architecture is influenced by the type of data and the task you want your model to perform.

Collection and Preparation of Data

An AI model will be unable to efficiently analyze and understand data without accurate data gathering and preparation. When collecting data, it’s important to gather a large and diverse data set that accurately represents the task you want your AI model to do. Following the collection of data, you should eliminate any wrong, corrupt, duplicate, or missing data by cleaning and preprocessing it.

Training the Process and Algorithms

Defining the loss function is one of the most important steps in training an AI model. This measures how much the model’s prediction is different from the actual data. Mean square error is used for numerical prediction, and cross-entropy loss is ideal for categorical predictions.

Training the Process and Algorithms

Defining the loss function is one of the most crucial steps in training AI models. This evaluates the discrepancy between the model’s prediction and the actual data. For numerical prediction, the mean square error is utilized, and for categorical prediction, the cross-entropy loss is used.

Evaluating Generative AI Models

Evaluating generative AI requires a balanced blend of qualitative and quantitative indicators to confirm that the system is indeed beneficial and achieves its goals. Perplexity and BLEU scores, along with FID (Fréchet Inception Distance) are part of quantitative measures and are used to check accuracy, fluency, or variety in text or image creation.

Specific to the domain, metrics like ROUGE are utilized for summarization to assess relevance and accuracy, while CER/WER is for text transcription. This provides a standard for comparison with pre-defined datasets to verify the quality of the results by measuring performance against these metrics.

Generative AI Applications

Generative AI and other types of AI models have positively impacted industries in a number of different ways, a couple of which are given below:

Generative AI in E-commerce

Product descriptions, customer support responses, and personalized recommendations are generated dynamically. For example, a clothing retailer might use generative AI in e-commerce to create tailored product descriptions for thousands of items.

Generative AI for Marketing

Marketers use AI to create engaging ad copy, generate social media posts, or even design visuals for campaigns. For instance, a company launching a new product might rely on generative AI for marketing to draft compelling promotional materials quickly.

Conclusion

Generative AI models represent the cutting edge of artificial intelligence, enabling businesses to innovate and create like never before. With diverse types of generative models, tailored training, and robust evaluation strategies, generative AI stands apart as a transformative tool for industries ranging from e-commerce to marketing.

If you want to learn more about the capabilities of generative AI, contact Mobiz now!

FAQs

What are generative models in AI?

Generative models in AI are systems that create new data samples by learning patterns from existing data. They differ from traditional AI, which primarily predicts or classifies, by focusing on generating unique outputs like images, text, or audio.

What are the different types of generative AI models?

The types of generative AI models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive Models (e.g., GPT), and Diffusion Models. Each has unique strengths suited to specific tasks, such as image synthesis or text generation.

What is the difference between AI and generative AI?

AI encompasses systems designed to analyze data, predict outcomes, and automate tasks. Generative AI, a subset of AI, focuses specifically on creating new content, such as realistic images or human-like text.