Introduction to Generative AI Concepts

Aug 19, 2024

Introduction to Generative AI

Overview

  • Instructor: Roger Martinez, Developer Relations Engineer at Google Cloud
  • Course Focus: 4 key learning objectives:
    1. Define generative AI
    2. Explain how generative AI works
    3. Describe generative AI model types
    4. Describe generative AI applications

What is Generative AI?

  • Definition: A type of artificial intelligence that produces various types of content (text, imagery, audio, synthetic data).

Context: Artificial Intelligence (AI)

  • AI: A discipline of computer science focused on creating intelligent agents that can reason, learn, and act autonomously.
  • Machine Learning (ML): A subfield of AI that trains models from input data for predictions on new data.

Types of Machine Learning Models

  • Supervised Learning:
    • Uses labeled data (data that comes with tags).
    • Example: Predicting tips based on historical bill amounts and order types (pickup/delivery).
  • Unsupervised Learning:
    • Uses unlabeled data to discover patterns (e.g., clustering employees based on tenure and income).

Understanding Supervised vs. Unsupervised Learning

  • Supervised:
    • Model learns from past examples to make predictions about future data.
    • Optimizes to reduce prediction error.
  • Unsupervised:
    • Focuses on discovering underlying patterns in raw data without labels.

Deep Learning

  • Deep Learning: A subset of machine learning using artificial neural networks to process complex patterns.
  • Neural Networks: Inspired by the human brain, consist of interconnected nodes (neurons).
  • Semi-supervised Learning: Uses both labeled and unlabeled data to train models.

Generative AI in Context

  • Generative Models: Create new data instances based on learned probability distributions.
  • Discriminative Models: Classify or predict labels for existing data points.
  • Key Distinction:
    • Discriminative: Predicts labels (e.g., dog vs. cat).
    • Generative: Generates new instances (e.g., creates an image of a dog).

Generative AI Process

  • Definition: Creates new content based on learning from existing data.
  • Foundation Model: Trained on extensive data to generate diverse content types (text, images, audio, etc.).
  • Generative Language Models: Learn from text data to produce natural-sounding language responses.

Importance of Transformers

  • Transformers: Key architecture for generative AI, comprising encoders and decoders.
  • Challenges with Generative Models: Hallucinations (nonsensical outputs) can occur due to inadequate training or context.

Prompts in Generative AI

  • Prompt: Input text that guides the output of a language model.
  • Prompt Design: Crafting prompts for desired responses from models.

Model Types in Generative AI

  1. Text-to-Text: Translates or reformats text (e.g., translation models).
  2. Text-to-Image: Generates images from textual descriptions (e.g., using diffusion).
  3. Text-to-Video: Creates videos from text input.
  4. Text-to-3D: Generates 3D objects based on text descriptions.
  5. Text-to-Task: Performs defined tasks based on text input.

Foundation Models and Applications

  • Foundation Models: Pre-trained on vast data for various tasks (sentiment analysis, image captioning, etc.).
  • Examples of Use: Fraud detection, personalized customer support, and more.

Google Cloud Generative AI Tools

  1. Vertex AI Studio: Explore and customize generative AI models on Google Cloud.
  2. Vertex AI: Build AI-powered applications with little to no coding experience.
  3. Palm API: Test and prototype large language models with various tools.
  4. Gemini: A multimodal AI model that analyzes text, images, and audio.

Conclusion

  • Generative AI creates new content and has various applications across industries.
  • Learning Resources: Check out additional videos for more insights on AI.