Overview
This lecture introduces the fundamentals of Generative AI, covering definitions, how it works, types of models, and practical applications, especially in the context of Google Cloud tools.
Artificial Intelligence (AI) Basics
- Artificial Intelligence (AI) is a branch of computer science focused on building systems that reason, learn, and act autonomously.
- AI aims to create machines that can think and behave like humans.
Machine Learning (ML) and Types
- Machine Learning is a subfield of AI where computers learn from data without explicit programming.
- Supervised ML uses labeled data to predict outcomes; unsupervised ML uses unlabeled data to find patterns or groups.
- In supervised learning, models minimize prediction errors compared to known outcomes.
- Unsupervised models cluster data to discover underlying groupings.
Deep Learning and Neural Networks
- Deep Learning is a subset of ML that uses artificial neural networks inspired by the human brain.
- Neural networks consist of interconnected nodes (“neurons”) and can process complex patterns.
- Semi-supervised learning uses both labeled and unlabeled data to improve learning.
Generative AI (GenAI) Overview
- Generative AI is a subset of deep learning focused on creating new content (text, images, audio, etc.) from learned data patterns.
- Discriminative models classify or predict labels; generative models generate new data similar to training examples.
- GenAI uses foundation models trained on vast datasets to create novel outputs from prompts.
How Generative AI Works
- GenAI models learn a statistical representation of data during training, enabling them to generate new, similar content.
- Outputs can include text, images, video, audio, or code, depending on the model type.
- Large Language Models (LLMs) like Gemini and LaMDA generate human-like text responses to prompts.
Model Types and Applications
- Text-to-Text: Translates or transforms one text into another (e.g., translation).
- Text-to-Image: Generates images from text descriptions using methods like diffusion.
- Text-to-Video/3D: Creates videos or 3D objects from text prompts.
- Text-to-Task: Executes defined tasks (e.g., answering questions, navigating interfaces) from text prompts.
- Foundation Models are large pre-trained models adapted for diverse tasks in various industries.
Tools and Platforms
- Gemini: A multimodal AI model handling text, images, audio, and code.
- Vertex AI Studio: Platform to explore, fine-tune, and deploy GenAI models.
- Vertex AI Agent Builder: No-code/low-code tools to build AI chatbots, assistants, and search applications.
- Model Garden in Vertex AI: Library of foundation models for language, vision, and other domains.
Key Terms & Definitions
- Artificial Intelligence (AI) — Study of creating machines that act intelligently.
- Machine Learning (ML) — Subfield of AI where models learn from data.
- Supervised Learning — ML using labeled data to train predictive models.
- Unsupervised Learning — ML for finding patterns in unlabeled data.
- Deep Learning — ML using layered neural networks for complex pattern recognition.
- Generative AI (GenAI) — AI that creates new content by learning data patterns.
- Discriminative Model — Model that classifies or predicts based on data.
- Foundation Model — Large, adaptable AI model pre-trained on diverse data.
Action Items / Next Steps
- Review definitions of key terms.
- Explore Google Cloud tools like Vertex AI Studio and Model Garden.
- Complete assigned readings or watch additional videos on Generative AI basics.