Beyond LLMs: GenAI Applications

Introduction

In this basics guide, we've been focusing on working with AI that processes and generates text. These AI models are called large language models (LLMs) and they power applications like ChatGPT, which can generate text, answer questions, and even help with writing tasks. These models have taken the world by storm. However, text generation is just one part of the incredible range of capabilities that generative AI offers.

In this guide, we'll explore various types of generative AI applications, broadening your understanding of how AI is shaping the world through its diverse capabilities:

1. Introduction
2. What is a Generative AI Model?
3. Overview of Generative AI Applications

• Image Generation
• Code Generation
• Audio Generation
• Video Generation
• Multimodal Models
• Synthetic Data Generation

10. Conclusion
11. FAQ

What is a Generative AI Model?

Generative AI refers to machine learning models that generate new content from existing data—be it text, audio, video, or images. Unlike discriminative models, which classify or differentiate between inputs, generative models create original content by learning from vast datasets. This guide focuses on the broad spectrum of generative AI applications, showcasing its potential across multiple modalities.

Overview of Generative AI Applications

Generative AI spans numerous applications, with capabilities in the following areas:

• Text Generation: AI models that generate and understand language, like ChatGPT.
• Image Generation: AI that creates visual content based on text descriptions.
• Audio Generation: AI capable of generating and modifying sound or music.
• Video Generation: AI that produces video sequences from text or enhances existing videos.
• Synthetic Data Generation: AI-created data used for training other machine learning models.
• Multimodal Models: AI that integrates multiple data types, such as text and images.

Let’s explore each of these in more detail.

Image Generation

Image generation is one of the most fascinating applications of generative AI. In simple terms, it involves using AI models to create entirely new images based on patterns learned from vast image datasets. These images can be realistic, stylized, or entirely imaginative, depending on the prompt.

These models usually work based on text-to-image translation. It allows AI to generate images from textual descriptions, turning words into visuals. Models take user inputs and generate images that match those descriptions.

Use Cases

• Creative Arts: Artists, designers, and marketers use tools like DALL-E 3 and MidJourney v6 to generate artwork, design concepts, and visuals for campaigns with just a few descriptive words.
• Synthetic Imagery: StyleGAN and Stable Diffusion allow the generation of hyper-realistic or fantasy-like imagery, used in media, marketing, and entertainment.

Here's an example of a prompt for Midjourney:

AI Output:

Code Generation

Generative AI can now write code streamlining the software development process. These models are trained on large datasets of publicly available code and can generate functional snippets, offer code suggestions, or even write entire programs based on user inputs.

Code generation is similar to text generation althought it requires model a special type of reasoning.

Key Use Cases

• Automating Repetitive Coding Tasks: Developers can use AI to automate repetitive tasks such as writing boilerplate code, improving productivity.
• Code Completion: Tools like GitHub Copilot assist by predicting and completing code as the developer writes, which accelerates development and reduces errors.
• Bug Fixes and Debugging: Advanced AI models can detect and fix bugs or suggest improvements to existing code.

Here's an example of a prompt:

AI Output:

Here is a simple Python function to sort a list of numbers using Python's built-in sort() method:

def sort_numbers(num_list):
    # Sort the list in ascending order
    num_list.sort()
    return num_list

Audio Generation

Generative AI for audio involves creating new sounds or modifying existing audio, such as music or speech. These models can analyze audio signals and synthesize new pieces based on user prompts.

These models use text-to-speech (TTS) translation. They can convert written text into spoken language and audio in general.

Key Use Cases

• Music Creation: Tools like Google’s MusicLM and Meta’s AudioCraft enable users to generate music compositions based on text descriptions or existing audio inputs. Interactive Media: AI-generated soundtracks can dynamically adjust based on user interaction, used in video games, fitness apps, or live streaming platforms.

Here's an example of a prompt:

Video Generation

Video generation is the process of creating entire video sequences or enhancing existing videos with AI. Recent breakthroughs allow AI to generate high-quality videos from text descriptions, a capability that was still developing just a few years ago.

They use text-to-video translation or image-to-video to generate complex video scenes from static noise or animate still images.

Key Use Cases

• Film Production: AI assists filmmakers in generating animated sequences or drafting storyboards, streamlining the creative process.
• Marketing and Social Media: Short-form videos created by AI tools like Runway’s Gen-3 Alpha or OpenAI’s Sora help content creators produce engaging videos quickly.

Here's an example of a prompt:

Multimodal Models: Integrating Several Data Types

Multimodal models are designed to handle and integrate various data types, such as text, images, and video. Unlike traditional models that focus on a single type of input, multimodal models can process multiple formats simultaneously, enabling more versatile applications.

Key Use Cases

• Image Captioning: Multimodal models can generate descriptive captions for images, bridging the gap between text and visual data. For instance, you can upload an image, and the model will generate an accurate text description.
• Visual Question Answering: Users can ask questions about an image or video, and the model can provide meaningful, context-aware answers by understanding both visual and textual data.
• Video Analysis: These models can analyze video content, extracting key moments or summarizing the video using text, allowing for powerful insights in fields like security, media, and entertainment.

Here's an example of a prompt:

Synthetic Data Generation

Synthetic data generation refers to the creation of artificial data that mimics real-world data. This is particularly useful when real data is scarce or expensive to collect.

Key Use Cases

• Autonomous Driving: AI models like NVIDIA Omniverse create synthetic driving data that trains self-driving cars, simulating dangerous or rare driving conditions without putting real drivers at risk.
• Healthcare Research: AI generates synthetic medical data for research purposes, helping maintain privacy while allowing researchers to test algorithms on diverse datasets.

Conclusion

Generative AI has moved far beyond text-based applications. From creating art and music to enhancing videos and generating synthetic data, AI’s potential across multiple modalities is shaping industries worldwide. Whether you’re a content creator, developer, or simply curious about AI’s growing capabilities, understanding these diverse applications will help you see the vast potential of generative AI.

The future of AI is not limited to any one field, and we are just beginning to explore what’s possible. As we continue to push the boundaries, generative AI will become a vital tool in everything from creative endeavors to solving complex real-world problems.

FAQ

What is generative AI?

Generative AI refers to artificial intelligence models that can create new content—text, images, audio, or video—based on patterns learned from existing data.

What are the main types of generative AI applications?

The main types include text generation, image generation, audio generation, video creation, and synthetic data generation.

Valeriia Kuka

Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.