In this introduction to the course on Generative AI, Dr. Gwendolyn Stripling from Google Cloud explains the course's content. It covers the definition of generative AI, its workings, model types, and applications. She contrasts AI with machine learning, highlighting that AI deals with creating intelligent agents, while machine learning is a subset of AI where models learn from input data. Supervised and unsupervised machine learning are explained, with supervised models making predictions based on labeled data, and unsupervised models discovering patterns and clusters in unlabeled data.
Understanding Fundamentals: A graphical explanation deepens the understanding of core concepts as the foundation for grasping generative AI.
Supervised Learning: In this approach, input values (x) are given to the model, which outputs predictions. The error between predicted and actual values is minimized through optimization.
Deep Learning as Subset: Deep learning is a type of machine learning using artificial neural networks that can handle complex patterns more effectively.
Artificial Neural Networks: Modeled after the human brain, they consist of interconnected nodes (neurons) processing data to make predictions. Deep learning models have multiple layers for more intricate pattern recognition.
Semi-Supervised Learning: A subset of deep learning, utilizing both labeled and unlabeled data to enhance learning and generalization.
Generative and Discriminative Models: Discriminative models predict labels for data points based on features, trained on labeled data. Generative models create new instances from existing data's probability distribution.
Discriminative vs. Generative: Discriminative models classify data, while generative models generate new content.
Visual Comparison: A traditional machine learning model learns the relationship between data and labels, while a generative AI model learns patterns to generate new content.
Distinguishing Generative AI: Understanding Outputs, Learning Processes, and Capabilities
Differentiating Gen AI: Gen AI can be identified by its output being natural language, images, audio, or other content types, rather than numerical values or classes.
Mathematical Representation: The equation y = f(x) signifies how outputs depend on inputs in various processes, with y being the output, f representing the function, and x indicating inputs.
Traditional vs. Generative Learning: Classical supervised and unsupervised learning builds models from labeled data to provide predictions, classifications, or clustering. In contrast, Gen AI processes include labeled and unlabeled data to generate new content across different data types.
Evolution of AI: Progression from traditional programming, through neural networks predicting categorical outcomes, to the current wave of generative models allowing user-generated content.
Generative AI Definition: Generative AI is a type of artificial intelligence that generates new content based on existing content it has learned from through training. A statistical model is created during training, which predicts responses to prompts and generates new content by capturing the underlying data structure.
Understanding Generative AI: Models, Processes, and Applications
Generative Language Models: Can create new content based on learned examples, like large language models that generate text.
Generative Image Models: Input images yield text, images, or video. Examples include visual question answering, image completion, and animation.
Generative Language Models: Input text leads to text, image, audio, or decision outputs, e.g., question answering, video generation.
Pattern Learning in Generative Language Models: Learns patterns from training data and predicts next elements in sequences.
Examples with Bard: Trained on extensive text data, Bard generates human-like responses across a variety of prompts.
Power of Transformers: Transformers revolutionized natural language processing, featuring an encoder and decoder for tasks. See Transformers Explained
Hallucinations in Transformers: Nonsensical or incorrect words/phrases generated due to factors like data quality or context.
Prompts in Generative AI: Short input text controlling model output and influencing responses. See What is Prompt Tuning
Dependence on Training Data: Generative AI learns patterns from input data, but user-generated content is also possible.
Model Types and Associated Inputs:
Text-to-Text: Maps between pairs of text for tasks like translation.
Text-to-Image: Generates images from text descriptions.
Text-to-Video and Text-to-3D: Create videos or 3D objects from text.
Text-to-Task: Performs actions based on text input, like navigation or predictions.
Foundation Models: Large AI models pre-trained on extensive data, adaptable to various tasks. Revolutionize industries, from healthcare to finance, enabling fraud detection and personalized customer support.
"Leveraging Generative AI with Vertex AI: Tools and Applications"
Vertex AI Model Garden: Offers foundation models for various tasks, such as language and vision applications.
Language Foundation Models: Includes PaLM API for chat and text applications.
Vision Foundation Models: Features stable diffusion for generating high-quality images from text descriptions.
Task-Specific Models: Tailored models for specific use cases, like sentiment analysis and occupancy analytics.
Gen AI Applications: Demonstrates code generation for debugging, SQL queries, language translation, and documentation.
Generative AI Studio: Allows easy exploration and customization of Gen AI models with pre-trained models, fine-tuning tools, deployment options, and a community forum.
Generative AI App Builder: Enables code-free creation of Gen AI apps with a drag-and-drop interface, visual editor, search engine, and conversational AI engine.
PaLM API: Facilitates testing and experimentation with large language models and Gen AI tools, integrated with Maker suite for quick prototyping.
Maker Suite Tools: Includes model training, deployment, and monitoring tools for developers working with ML models.
Course Conclusion: Expresses gratitude for watching the Introduction to Generative AI course.