Types of Generative AI Models and Its Applications

Generative AI refers to a class of artificial intelligence that is designed to produce original content, such as text, images, videos, and music, and is increasingly being applied across industries to automate workflows and drive innovation.

A generative AI model is a type of AI that can create new content, like text, images, and music, based on patterns and examples it has learned. Generative artificial intelligence enables the production of diverse forms of new content, solving complex problems and supporting a wide range of applications. It uses algorithms to generate original and realistic output without direct human input.

According to Forbes, the value of generative AI is expected to increase by $180 billion over the next eight years. With its most popular types, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models like the Transformer, generative AI enables companies across all industries to create better and faster content. Foundational models represent a key technological advancement in generative AI, driving digital transformation and responsible AI practices across sectors.

The use cases of generative AI are growing daily, making almost everything possible, from creating music to utilizing AI-based music visualizer solutions and producing top-quality AI videos with minimal human intervention. AI-generated content is becoming a strategic asset for digital transformation, enabling businesses to scale content creation and enhance customer experiences. For a comprehensive overview, read our Generative AI guide.

Tools like an AI sound effect generator also allow creators to enhance their audio tracks with realistic, custom sound effects in seconds. For generated content, authenticity and proper attribution are crucial, especially as businesses increasingly rely on AI-driven digital assets.

In our blog, we have outlined the top six types of generative AI models and their applications for various purposes. 

Before we begin examining the types and use cases, here are some key numbers to consider, which will help you understand how quickly this technology is growing, ensuring that adapting to generative AI will be the best decision.

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• The chart above shows that by 2032, the revenue in generative AI trends will reach 1.3 trillion.

• Among the entrepreneurs, 64% of executives have a sense of urgency to adopt generative AI models for better performance.

These numbers demonstrate that entrepreneurs have recognized the value of generative AI algorithms, and the majority have already begun to utilize them. Generative AI is also transforming software development workflows by automating code generation and streamlining processes. The others are planning to do so within the next two years.

Let us now examine the various types of AI models that can be utilized for multiple purposes, including content creation, image generation, and video production. Generative AI tools cover a wide range of solutions, from text and image generation to audio and code creation. One popular tool is an AI, which can transform text prompts into unique visuals by leveraging deep learning techniques. Similarly, tools like Venngage’s AI infographic generator enable users to effortlessly convert ideas into professional-looking infographics, combining automation with design flexibility for enhanced content creation.

Popular generative AI models, such as GPT-4 and DALL·E, are widely used in these tools and have set new standards in AI-driven content generation. These tools can change how we process data, solve problems, and generate text. These AI models can also automate the conversion of text into PowerPoint presentations. Therefore, there is immense potential in working with these Generative AI capabilities. But first, let us explore the top types of gen AI models.

Generative Adversarial Networks (GANs) are a class of generative AI models that consist of two neural networks: the generator and the discriminator. These are trained simultaneously through adversarial learning. The adversarial process enables the generator to produce increasingly realistic data.

GANs generate fake data by transforming random noise into synthetic outputs that closely resemble real-world data. The quality and diversity of generated samples are crucial for model evaluation, as issues like mode collapse can reduce the variety of samples produced. GANs are also widely used for video generation and as image generators, producing visual content that can deceive discriminators in the network. GANs do not involve human intervention and enable applications in various domains, such as art generation, video enhancement, and data generation for training purposes. They are also widely used in areas such as image-to-image translation.

Variational Autoencoders (VAEs) are another type of generative model that creates new data by sampling from the learned latent space after encoding input data into a low-dimensional latent space. It then decodes it back to reconstruct the input. VAEs learn to encode data into a latent space, which is useful for data reconstruction and generation. These models are applied to representation learning, data production, and compression problems.

VAEs are also used for anomaly detection, particularly in financial institutions for fraud detection. In semi-supervised learning, VAEs can leverage both labeled data and unlabeled data to improve model performance. These models are used for various tasks like data generation, compression, and representation learning.

Transformer models are a popular type of neural network architecture designed for subsequent data processing. These models utilize a self-attention mechanism to assess the relative significance of words within a phrase, enabling long-range interdependence and parallel processing of input sequences.

Transformer-based models, including transformer-based generative models and generative pre trained transformers, are foundational to large language models and language models such as GPT and LaMDA. These architectures enable advanced capabilities in natural language processing, text generation, and summarization. The training process for large language models requires significant computational resources, leading to environmental impact and efforts to develop more energy-efficient methods. Transformer models are highly effective at generating content and processing human language, allowing them to produce contextually relevant outputs across a range of applications. To get started with developing these powerful models, here's a detailed look at how to build llms.

Transformer models have achieved state-of-the-art performance in several natural language processing tasks, like machine translation, text-to-speech, text generation, and sentiment analysis.

Deep Convolutional Generative Adversarial Networks (DCGANs) are a class of deep learning models employed for generating synthetic images built on the foundation of convolutional neural networks (CNNs). As deep generative models, DCGANs are capable of modeling complex data distributions and capturing the structure of real-world data through multiple layers of neural networks.

DCGANs have demonstrated exceptional capabilities in generating realistic images, paving the way for advancements in image synthesis and reconstruction tasks. Advanced diffusion models like stable diffusion have further enhanced image generation capabilities within the AI ecosystem. Tools like the Depositphotos AI image generator can provide unique resources for creating high-quality visual content efficiently and effectively.

Recurrent Neural Networks (RNNs) are a class of neural networks specifically designed for analyzing sequential data. These contain loops that allow information to persist.

RNNs are well-suited for processing time series, speech recognition, and natural language processing tasks because of their ability to retain memory of previous inputs. RNNs learn hierarchical representations and underlying patterns in sequential data, enabling them to model complex dependencies. The design of neural networks, including RNNs, is inspired by the human brain, mimicking the structure and function of neurons and synapses to perform learning and pattern recognition.

Top Business Applications of Generative AI

For a comprehensive look at how these models are applied in different fields, read our blog on Generative AI Applications, which covers:

1. Customer Operations
2. Marketing and Sales
3. Software Engineering
4. Product Research & Development