Understanding Generative AI and Its Applications

Sep 21, 2024

Introduction to Generative AI

Lecture Overview

  • Instructor: Roger Martinez, Developer Relations Engineer at Google Cloud
  • Course Objectives:
    • Define generative AI
    • Explain how generative AI works
    • Describe generative AI model types
    • Describe generative AI applications

What is Generative AI?

  • Generative AI: Type of AI that produces content (text, imagery, audio, synthetic data)
  • Contextualizing AI:
    • Artificial Intelligence (AI): Discipline of computer science focused on creating intelligent agents that can reason, learn, and act autonomously.
    • Machine Learning (ML): Subfield of AI that trains models from input data to make predictions on new data.

Types of Machine Learning Models

Supervised vs. Unsupervised Learning

  • Supervised Learning:
    • Involves labeled data (e.g., predicting tips based on historical bill data).
  • Unsupervised Learning:
    • Involves unlabeled data (e.g., clustering employee data based on tenure and income).

Deep Learning

  • Deep Learning: Subset of ML using artificial neural networks to learn complex patterns.
  • Neural Networks: Mimic human brain structure with interconnected nodes for data processing and predictions.
  • Semi-Supervised Learning: Combines labeled and unlabeled data for training neural networks.

Generative vs. Discriminative Models

  • Discriminative Models: Classify or predict labels for data points based on labeled training data.
  • Generative Models: Generate new data instances based on learned probability distributions from existing data.
    • Example: Discriminative models classify images (dog or cat), while generative models can create new images of dogs.

Mathematical Perspective

  • Models output based on functions of inputs:
    • If output is a number (e.g., predicted sales), it is not generative AI.
    • If output is natural language or image, it is generative AI.

Generative AI Process

  • Involves training on labeled/unlabeled data to create a foundation model that can generate various content types (text, images, audio, etc.).
  • Transition from traditional programming to generative models allows users to create their own content.

Definition of Generative AI

  • Generative AI creates new content based on learned patterns from existing data.
  • Prompting: The process of providing input to generate desired outputs from models.

Model Types in Generative AI

  1. Text-to-Text: Translates or generates text.
  2. Text-to-Image: Generates images from text descriptions (e.g., diffusion methods).
  3. Text-to-Video: Generates videos from text input.
  4. Text-to-3D: Creates 3D objects from text descriptions.
  5. Text-to-Task: Performs specific actions based on text inputs.

Foundation Models

  • Large pre-trained AI models designed for a range of tasks (e.g., sentiment analysis, image captioning).
  • Vertex AI: Offers tools for customizing and deploying generative AI applications.

Generative AI Applications

  • Code Generation: Assists with debugging, translating code, and creating documentation.
  • Vertex AI Studio: Customizes generative AI models for developers.
  • Vertex AI Agent Builder: Enables creation of chatbots and digital assistants with minimal coding.
  • Gemini Model: A multimodal AI that understands and generates text, images, audio, and code.

Conclusion

  • Generative AI enables users to generate content across multiple media, revolutionizing various sectors.
  • For more advanced learning, refer to additional resources provided by the course.