Welcome to Module 4 — Generative AI.

In this lesson, you will learn how modern AI systems can generate new content such as text, images, audio, video, and code. You will understand how Generative AI differs from traditional AI, how generative models work at a high level, and how these technologies are being used to build modern AI applications.

Topics Covered:

1. What is Generative AI?
   Understand the concept of Generative AI and how AI systems can create new content based on learned patterns.
2. Traditional AI vs Generative AI
   Learn the key differences between traditional AI systems that primarily analyze, classify, or predict and generative systems that create new content.
3. How Generative AI Works
   Understand the basic process through which generative models learn patterns from large datasets and generate new outputs.
4. Types of Generative AI
   Explore different forms of AI-generated content:
   • Text Generation
   • Image Generation
   • Audio Generation
   • Video Generation
   • Code Generation
5. Generative Models
   Get introduced to major approaches used for generative modeling, including:
   • GANs
   • VAEs
   • Diffusion Models
   • Transformer-Based Models
   • Foundation Models
6. Text Generation
   Understand how AI models generate human-like text for chatbots, content creation, summarization, translation, coding, and other applications.
7. Image Generation
   Learn how modern AI systems generate and transform images from text prompts and other inputs.
8. Audio and Speech Generation
   Explore AI-generated speech, music, sound, and other audio applications.
9. Video Generation
   Understand the emerging capabilities of AI systems for generating and transforming video content.
10. Multimodal Generative AI
    Learn how modern AI systems can work across multiple modalities such as text, images, audio, video, and other forms of information.
11. Foundation Models
    Understand the concept of foundation models and how they provide a base for building different AI-powered applications.
12. Generative AI Applications
    Explore practical applications in content creation, education, marketing, software development, customer support, design, research, and business automation.
13. Limitations of Generative AI
    Understand important challenges including hallucinations, inaccurate information, bias, copyright concerns, data privacy, reliability, and responsible AI usage.
14. Generative AI in Modern Applications
    Understand how Generative AI is combined with other AI technologies to create assistants, copilots, content tools, knowledge systems, and intelligent applications.

Mini Project:
Build a simple Generative AI application that accepts a user prompt and produces useful AI-generated content, such as a content generator, AI writing assistant, or creative idea generator.

Learning Outcome:
By the end of this lesson, you will understand the fundamentals of Generative AI, the major types of generative models, how AI generates different forms of content, common real-world applications, and the key limitations that must be considered when building and using Generative AI systems.

Lesson Resources:
Video Lesson + Presentation PDF + Quize