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AI Overview and Subfields

Jul 2, 2025

Overview

This lecture provides a beginner-friendly summary of Google's introductory AI course, clarifying the relationships between AI, machine learning, deep learning, and large language models, as well as the main types and uses of modern AI.

AI and Its Subfields

  • Artificial Intelligence (AI) is a broad field of study focused on creating machines that simulate human intelligence.
  • Machine Learning (ML) is a subfield of AI; it uses data to train models that make predictions on new data.
  • Deep Learning (DL) is a further subset of ML that uses artificial neural networks, inspired by the human brain.

Types of Machine Learning

  • Supervised learning uses labeled data to train models to make predictions (e.g., predicting tips based on labeled order types).
  • Unsupervised learning uses unlabeled data to find natural groupings or patterns (e.g., segmenting employees by tenure and income).
  • Supervised models compare predictions to training data and adjust; unsupervised models do not have this feedback loop.
  • Semi-supervised learning (via deep learning) combines a small labeled dataset with a large unlabeled one to improve performance.

Deep Learning Models: Discriminative vs. Generative

  • Discriminative models classify data into categories using labels (e.g., cat vs. dog image classification).
  • Generative models learn data patterns to generate new, similar samples (e.g., producing new images or text).

Generative AI and Model Types

  • Generative AI produces new outputs, such as text, images, audio, or video, based on learned patterns.
  • Common generative model types: text-to-text (e.g., ChatGPT), text-to-image (e.g., DALL-E), text-to-video, text-to-3D, and text-to-task.

Large Language Models (LLMs)

  • LLMs are a type of deep learning model trained on large language datasets and then fine-tuned for specific tasks or industries.
  • LLMs support applications in areas like healthcare, finance, and retail by adapting general models to specialized data.

Key Terms & Definitions

  • Artificial Intelligence (AI) — The study of making machines mimic human intelligence.
  • Machine Learning (ML) — A subfield of AI focused on training algorithms to make predictions from data.
  • Deep Learning (DL) — ML using artificial neural networks with multiple layers.
  • Supervised Learning — ML using labeled data for training.
  • Unsupervised Learning — ML using unlabeled data to find patterns.
  • Semi-supervised Learning — DL using both labeled and unlabeled data.
  • Discriminative Model — Learns to classify data points into categories.
  • Generative Model — Learns to create new data samples similar to its training data.
  • Generative AI (GenAI) — AI that generates new content (text, images, etc.) from input prompts.
  • Large Language Model (LLM) — Deep learning model trained on massive text datasets, used for language tasks.

Action Items / Next Steps

  • Review each module of the full Google AI course for a deeper understanding.
  • Practice identifying supervised, unsupervised, and generative AI in real-world tools.
  • Explore prompting techniques to improve AI tool usage.

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