AI Basics Course Summary

General Information

• Video Overview: Summarizes a 4-hour Google AI literacy course into 10 minutes.
• Purpose: To provide foundational AI knowledge valuable for using tools like ChatGPT and Google Bard.
• Expectation: Geared more towards conceptual understanding.

AI and Its Subfields

• Artificial Intelligence (AI): A broad field of study akin to physics.
• Machine Learning (ML): A subfield of AI, similar to how thermodynamics is a subfield of physics.
• Deep Learning (DL): A further subfield of ML.
• Types of DL Models: Can be divided into discriminative models, generative models, and large language models (LLMs).
• Tools: ChatGPT and Google Bard belong to DL, occupying the overlapping area of LLM and generative AI.

Machine Learning

• Function: Uses input data to train models to make predictions based on new data.
• Example: Training a Nike sales data model to predict Adidas sales.

Types of ML

• Supervised Learning

  • Uses labeled data.
  • Example: Predicting tips based on the total bill and delivery type (self-pickup/delivery).
  • Distinction: Model compares predictions with training data and adjusts accordingly.

• Unsupervised Learning

  • Uses unlabeled data.
  • Example: Identifying relationships between employee tenure and salary.
  • Distinction: Automatically forms groups from raw data.

Deep Learning

• Overview: A type of ML using artificial neural networks, inspired by the human brain.
• Structure: More nodes and neurons lead to a more powerful model.
• Semi-Supervised Learning: Combines a small amount of labeled data with a large amount of unlabeled data.
  • Example: Banks detecting fraud by labeling 5% of transaction data.

Types of DL Models

• Discriminative Models

  • Learn relationships between labeled data points.
  • Example: Classifying images as cats or dogs.

• Generative Models

  • Learn patterns in data to generate new content.
  • Example: Creating new dog images from learned features (two ears, four legs, etc.).

Generative AI

• Identification: Outputs natural language, images, or sounds, not just numerical values or categories.
• Examples: ChatGPT, Google Bard, Midjourney, DALL·E, stable diffusion, imagen video, CogVideo, make-a-video, OpenAI's shap-e model.
• Specialized Functions: Text-to-task models perform specific tasks using AI.
  • Example: Bard summarizing unread emails in Gmail.

Large Language Models (LLMs)

• Role: A subset of DL but not identical to generative models.
• Training: Pre-trained on large datasets and fine-tuned for specific tasks.
  • Example: Pre-training a general model and fine-tuning it for use in healthcare.
• Business Model: Large companies create general models sold to smaller entities for industry-specific fine-tuning.

Course Notes

• Modules: The course has 5 modules, each awarding a badge upon completion.
• Additional Resources: Visit cloudskillsboost.google/course_templates/536 for the free course.
• Tips: Watch a video on mastering AI prompts for practical skills.