Introduction to Generative AI

Instructor

• Name: Roger Martinez
• Role: Developer Relations Engineer at Google Cloud

Course Outline

1. Definition of Generative AI
2. How Generative AI Works
3. Types of Generative AI Models
4. Applications of Generative AI

What is Generative AI?

• Definition: Type of AI technology that can produce various types of content including text, imagery, audio, and synthetic data.

Context: Artificial Intelligence (AI) vs Machine Learning (ML)

• Artificial Intelligence: Branch of computer science focused on creating intelligent agents that can reason, learn, and act autonomously.
• Machine Learning: Subfield of AI that involves training a model from input data to make predictions on new, unseen data.
• Types of ML Models:
  • Supervised Learning: Uses labeled data to predict outcomes.
  • Unsupervised Learning: Uses unlabeled data to discover underlying patterns.

Deep Learning

• Definition: Subset of ML that uses artificial neural networks to process complex patterns.
• Artificial Neural Networks: Inspired by the human brain, these are made up of interconnected nodes or neurons.
• Types of Learning:
  • Supervised Learning: Uses labeled data.
  • Unsupervised Learning: Uses unlabeled data.
  • Semi-Supervised Learning: Uses a mix of labeled and unlabeled data.

Generative AI

• Definition: Subset of deep learning, uses neural networks to process both labeled and unlabeled data.
• Generative Models vs Discriminative Models:
  • Discriminative Models: Classify or predict labels for data points.
  • Generative Models: Generate new data instances based on learned data distributions.

Use Cases and Applications

• Generative AI: Can generate text, images, audio, video, etc.
• Large Language Models (LLMs): Generate natural language text.
• Text to Text: Models translate or transform text into other text.
• Text to Image: Models generate images from text descriptions, using methods like diffusion.
• Text to Video and 3D: Models generate videos or 3D models from text.
• Text to Task: Models perform specific tasks based on text input.

Foundation Models

• Definition: Large AI models pre-trained on vast quantities of data, adaptable to a range of tasks.
• Applications: Sentiment analysis, image captioning, object recognition, etc.

Google Cloud Tools for Generative AI

• Vertex AI Studio: Create and deploy generative AI models with a variety of tools and resources.
• Vertex AI: Build AI applications with little or no coding experience.
• Palm API: Experiment with Google's LLMs and tools like Maker Suite for training, deploying, and monitoring models.

Key Points

• Transformers: Use neural networks to process sequences of data, revolutionizing natural language processing.
• Prompt Design: Creating input prompts to generate desired model outputs.
• Model Types: Range from text, image, code, and complex multi-modal systems like Gemini.
• Use Cases: Conversational AI, code generation, sentiment analysis, etc.

Potential Issues

• Hallucinations: Generated content that is nonsensical or incorrect, caused by insufficient or noisy training data.

Summary

• Generative AI is a powerful tool for creating new content across various media, grounded in complex learning models and extensive training data.