Introduction to Generative AI

Understanding Generative AI

• Introduction
  • Course Objectives and Overview
  • Setting the Context: The Era of Generative AI
• Understanding Generative AI
  • What is Generative AI?
    • Defining “Generative” in AI Context
    • Comparison to Traditional AI Systems
  • The Mechanism Behind Generation
    • Sampling from a Distribution
    • Neural Networks and Their Role
  • Key Concepts and Components
    • Latent Spaces and Their Role
    • The Balance Between Randomness and Training
• Real-world Uses of Generative AI
  • Media and Entertainment
    • Music, Movies, and Art
    • Game Design and Virtual Worlds
  • Design and Manufacturing
    • 3D Modelling and Prototyping
    • Fashion and Apparel
  • Content Creation
    • Blogs, Articles, and Scripts
    • Advertising and Marketing
  • Deepfakes and Their Implications
    • What they are and how they are made
    • Ethical considerations
  • Group Discussion: Encounters with Generative AI in Daily Life
• Limitations and Challenges of Generative AI
  • Technical Limitations
    • Training Data Needs
    • Computational Costs
  • Ethical and Societal Challenges
    • Bias and Misrepresentation
    • Economic Impacts (e.g., job displacement in creative fields)
• Dive into Prompt Engineering
  • Introduction to Language Models
    • Examples like OpenAI’s GPT series
    • Basics of their functioning
  • What is Prompt Engineering?
    • Definition and Importance
    • Crafting Prompts for Desired Outputs
  • Hands-on Activity: Crafting Prompts for a Language Model
    • Using predefined platforms
    • Testing and Iteration
  • Real-world Applications and Importance
    • Business, Research, and More
• Conclusion and Recap
  • Key Takeaways
  • Resources for Further Exploration

• The Big Picture
• ML is a subset of AI
• Deep Learning is a subset of ML
• GenAI is a subset of Deep Learning
• Understanding inference, influence, prompt, completion, and feedback
• Understanding Prompt Engineering
• Understanding AI Models
• Foundation Model
• Generative Models
• Large Language Models
• The Mechanism Behind Generation
• Machine Learning
• Deep Learning
• Artificial Neural Networks
• Training a Model
• The Balance Between Randomness and Training
• Hyperparameter Tuning
• Tokens and Tokenization
• Hands-on Activity: Latent Space Exploration
• Tokens and Model Usage Pricing
• Generative AI options

Real-world Uses of Generative AI

• Data Analytics using Generative AI
• Sentiment Analysis using Generative AI
• Music, Movies, and Art
• Create with AI Art Tools
• Game Design and Virtual Worlds
• 3D Modelling and Prototyping
• Fashion and Apparel
• Blogs, Articles, and Scripts
• Hands-on Activity: Scriptwriting with AI
• Advertising and Marketing
• Deepfakes and Their Implications

Ethical Considerations and Limitations of AI

Ethical Considerations

• Authenticity and Misinformation
• Deepfake detection workshop
• Bias and Fairness
• Intellectual Property
• Consent and Privacy
• Transparency and Accountability
• Impact on Employment
• Environmental Impact
• Safety and Security
• Regulation and Governance
• Public Perception and Trust
• Hands-on Activity: Bias detection workshop

Limitations and Challenges of GenAI

• Training Needs Data
• Computational Costs
• Quality and Realism
• Detection of AI-Generated Content

Conclusion