Introduction to Generative AI

Course Overview

• Instructor: Roger Martinez, Developer Relations Engineer at Google Cloud
• Topics Covered:
  • Definition of generative AI
  • How generative AI works
  • Types of generative AI models
  • Applications of generative AI

What is Generative AI?

• A type of AI technology that produces content (text, images, audio, synthetic data).

Context: Artificial Intelligence (AI)

• Definition: AI is a branch 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 to make predictions on unseen data.

Types of Machine Learning Models

• Supervised Learning:

  • Uses labeled data (data with tags) to train models.
  • Example: Predicting tips based on bill amounts and order types.

• Unsupervised Learning:

  • Uses unlabeled data to identify patterns or clusters in data.
  • Example: Clustering employees based on tenure and income.

Deep Learning

• Definition: A subset of ML that uses artificial neural networks to process complex patterns.
• Neural Networks:
  • Inspired by human brains, made of interconnected nodes or neurons.
  • Can process both labeled and unlabeled data (semi-supervised learning).

Generative AI in Context

• Generative vs Discriminative Models:
  • Discriminative Models: Classify or predict labels based on features (e.g., identifying a dog).
  • Generative Models: Generate new data based on learned distributions (e.g., creating images).

Key Differences:

• Discriminative: Predicts labels.
• Generative: Generates new content.

Mathematical Visualization

• Y = f(X):
  • Represents the relationship between input data (X) and model output (Y).
  • Non-generative AI outputs numerical or class-based results.
  • Generative AI outputs natural language, images, etc.

Generative AI Process

• Involves training on diverse data (labeled/unlabeled) to build a foundation model that can generate new content.

Types of Generative AI Models

• Generative Language Models: Produce human-like text.
• Generative Image Models: Generate images or videos based on text inputs.
• Generative Code Models: Assist in coding tasks.

Models and Applications

• Foundation Models: Large AI models trained on vast data, adaptable for various tasks (sentiment analysis, fraud detection, etc.).
• Vertex AI: A platform for creating and deploying generative AI models.
• Gemini: A multimodal AI capable of understanding text, images, audio, and programming code.

Conclusion

• Generative AI enables users to create content across multiple media types.
• Key takeaway: Generative AI is about creating new, contextually relevant content based on learned patterns.