Types of Generative AI: Image, Text, Video & More Explained

Introduction

Generative AI is one of the most exciting advancements in artificial intelligence today. Unlike traditional AI, which focuses on analyzing or classifying data, generative AI has the incredible ability to create entirely new content. From generating realistic images to composing human-like text and videos, these AI models are reshaping creative industries. This blog will dive into the various types of generative AI and showcase how platforms like Glance are leveraging AI to revolutionize content creation.

What is Generative AI?

Generative AI refers to algorithms capable of generating new, original content based on the data they have been trained on. Unlike traditional AI which responds to data with predefined rules or patterns, generative AI creates new and unseen data, such as images, text, music, and videos. This type of AI is increasingly being used across industries like marketing, entertainment, fashion, and even software development, enhancing creativity and productivity in ways previously unimaginable.

Types of Generative AI Models

Generative AI can be categorized into several types based on the content it creates. Below are the most prominent types of generative AI models:

1. Image Generation AI

Image generation AI has gained significant attention in recent years. These tools utilize neural networks and deep learning algorithms to generate highly realistic images from scratch based on textual descriptions or other input forms. AI-driven platforms like DALL·E 2, Artbreeder, and DeepArt are famous for creating artwork and visual concepts that would traditionally require human artists.

How It Works:

Image generation AI leverages Generative Adversarial Networks (GANs) or other neural network architectures to learn the characteristics of the images it has been trained on. When given an input, such as a description of a scene or even a specific artist's style, the AI can generate images that match the input request.

Example:

Imagine asking the AI to generate a painting in the style of Van Gogh or a surreal landscape with floating islands. The AI processes the description and creates unique visual artwork.

2. Text Generation AI

Text generation AI models like GPT-4 (the model you're reading now), BERT, and T5 have revolutionized the way content is produced. These models generate human-like text based on a prompt, capable of writing articles, stories, poetry, dialogue, and code.

How It Works:

Text generation AI uses natural language processing (NLP) to understand the context, structure, and grammar of the input text. By processing large amounts of data from various sources, these models can generate text that aligns with human writing patterns and even mimic specific writing styles.

Example:

ChatGPT, like the one you are interacting with, can write articles, answer queries, or even help with brainstorming ideas. In marketing, tools like Jasper AI use similar technology to generate product descriptions, ad copy, and blog posts.

3. AI-Generated Video

AI-generated video is one of the most fascinating developments in generative AI. Using technologies like deepfake and Generative Adversarial Networks (GANs), these AI models can create fully realized video content, from realistic animations to video clips that feature AI-generated human avatars.

How It Works:

AI video generation works by combining machine learning models with video-editing tools to create new sequences of video. By analyzing millions of hours of video content, these AI models can understand movements, facial expressions, and voice modulation to generate videos that look like real human content.

Example:

Synthesia uses AI to generate videos featuring virtual avatars. These avatars can speak any script provided by the user, offering businesses a way to create instructional videos, product demos, or even personalized messages at scale.

4. Audio and Music Generation AI

AI-powered music and audio generation tools are also making waves in content creation. These systems are capable of composing music, generating voiceovers, or even mimicking specific voices.

How It Works:

Audio and music generation AI models are trained on vast datasets of music, voice recordings, and sound effects. This allows them to understand musical patterns, rhythms, and voice characteristics, enabling them to compose original music or generate lifelike human-like speech.

Example:

OpenAI’s Jukedeck generates personalized music tracks based on mood or genre, while Descript offers AI-powered voice generation tools, allowing users to create voiceovers in different styles or voices with just text input.

5. Code Generation AI

AI-driven code generation tools are gaining popularity among developers. Models like GitHub Copilot use machine learning algorithms to help developers by predicting and generating code snippets based on natural language prompts.

How It Works:

Code generation AI leverages models trained on vast codebases from programming languages like Python, JavaScript, and Java. These models understand the context and purpose of the code being written and suggest lines of code or complete functions to speed up development.

Example:

GitHub Copilot assists developers by offering suggestions, helping with repetitive coding tasks, and ensuring faster development cycles.

Generative AI Tools

Below is a table showcasing some of the most popular tools used in various generative AI models:

How Do Generative AI Models Work?

At the core of generative AI are deep learning models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. These algorithms are trained on massive datasets, learning the underlying patterns, structures, and relationships within the data.

Once trained, these models are capable of generating new content that shares similarities with the data they were trained on but is unique. For example, an image generation model might be trained on millions of images and learn to generate images that follow the same patterns but are not replicas of the training data.

Why is Generative AI Important in 2025?

In 2025, generative AI will play a pivotal role in content creation, marketing, entertainment, and business. With the rise of personalized experiences and the demand for high-quality content at scale, generative AI offers businesses the ability to automate and personalize content creation while driving innovation. Platforms like Glance have already embraced AI technology to generate personalized content for users in real-time, showcasing the enormous potential of generative AI in the mobile-first world of 2025.

Benefits of Generative AI Models

Generative AI offers numerous benefits, including:

1. Creativity at Scale

Generative AI allows businesses to create vast amounts of unique content quickly. Artists, marketers, and designers can experiment with limitless ideas without needing to start from scratch every time.

2. Cost Efficiency

AI can produce high-quality content without large creative teams, reducing costs for businesses and creators.

3. Personalization

Generative AI helps create tailored content that speaks directly to individual preferences, resulting in better customer experiences.

4. Innovation

Generative AI is opening new possibilities for art, entertainment, and business. It allows businesses to generate highly unique and innovative content that would be impossible to produce manually.

Challenges of Generative AI

Despite its immense potential, generative AI faces some challenges:

1. Ethical Concerns

The use of deepfake technology and the creation of AI-generated content that can deceive or mislead people raise serious ethical issues.

2. Data Privacy

The data used to train generative AI models often comes from public sources, raising concerns about privacy and consent, especially when the content is used for commercial purposes.

3. Bias

AI-generated content can reflect the biases present in the data used for training, which can result in problematic or skewed outputs.

The Future of Generative AI

The future of generative AI is bright. As technology advances, generative AI models will become more sophisticated and accessible to a broader range of businesses and individual creators. Expect to see even more impressive capabilities in image, text, video, and music generation, with AI models becoming more intuitive and versatile.

In the coming years, AI models will offer more complex content creation features, including personalized, hyper-relevant video content for users, more advanced AI-driven music, and deeper integration into healthcare, entertainment, and education. 

Glance is already leading the charge, leveraging AI to create dynamic, real-time content for its audience, while also revolutionizing shopping experiences on phones and smart TVs through the Glance AI app.

Conclusion

Generative AI is revolutionizing the way we create and consume content. Whether it’s images, text, videos, or music, these models are enabling businesses and creators to unleash their full creative potential. As generative AI models evolve, expect more powerful and personalized content creation tools to emerge, transforming industries and enhancing the way we interact with digital content.

FAQs

What is generative AI vs other types of AI?

Generative AI creates new content, while other types of AI (like discriminative AI) focus on identifying patterns or classifying data.

What is the most famous generative AI?

Some of the most famous generative AI tools include DALL·E, GPT-4, and DeepFake.

Are there different types of AI?

Yes, there are several types of AI, including narrow AI (task-specific) and general AI (capable of performing any intellectual task a human can do).

Is ChatGPT generative AI?

Yes, ChatGPT is a generative AI model that creates human-like text based on input prompts.

What are the three types of AI with examples?

The three main types of AI are:

• Artificial Narrow Intelligence (ANI): Task-specific (e.g., Siri)
• Artificial General Intelligence (AGI): Human-level AI capable of performing any intellectual task
• Artificial Superintelligence (ASI): AI surpassing human intelligence in all areas