Introduction to Generative AI

Overview

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

What is Generative AI?

• Generative AI is a type of AI technology that produces content (text, images, audio, synthetic data).
• Context of AI and Machine Learning:
  • Artificial Intelligence (AI):
    • A branch of computer science focused on creating intelligent agents.
    • Involves reasoning, learning, and autonomous actions.
  • Machine Learning (ML):
    • A subfield of AI that trains models from input data to make predictions.
    • Allows computers to learn without explicit programming.

Types of Machine Learning Models

• Supervised Learning:
  • Uses labeled data (data with tags).
  • Example: Predicting tips based on historical data.
• Unsupervised Learning:
  • Uses unlabeled data to discover patterns or group data.
  • Example: Clustering employees based on tenure and income.

Deep Learning

• Deep Learning: A subset of machine learning using artificial neural networks to process complex patterns.
  • Inspired by the human brain with many interconnected nodes (neurons).
  • Can utilize both labeled and unlabeled data (semi-supervised learning).

Generative vs. Discriminative Models

• Generative Models:
  • Learn the probability distribution of data to create new instances.
• Discriminative Models:
  • Classify or predict labels for data points based on learned relationships.

Key Differences

• Discriminative: Predicts labels (e.g., dog vs. cat).
• Generative: Generates new instances (e.g., images of dogs).

Understanding Generative AI

• Generative AI can process both labeled and unlabeled data.
• It learns from existing content to create new content.

Applications of Generative AI

• Types of Models:
  • Text-to-Text: Translates or transforms text.
  • Text-to-Image: Generates images from textual descriptions.
  • Text-to-Video: Creates videos based on text input.
  • Foundation Models: Pre-trained large models adaptable to various tasks.

Generative AI and Programming

• Example: Code generation, debugging, and translating codes.
• Tools available in Google Cloud:
  • Vertex AI Studio: Explore and customize generative AI models.
  • Vertex AI: Build applications without extensive coding.
  • Palm API: Access to Google's large language models for prototyping.

Conclusion

• Generative AI allows for content generation across various formats.
• Importance of understanding the training data for successful outcomes.
• Encouragement to explore further resources for deeper learning.