Video summary

Where Generative AI Fits in the AI and ML Landscape

Main summary

Key takeaways

Educational

Main ideas, concepts, and lessons

Purpose of the course (4 learning goals)

  • Define generative AI
  • Explain how generative AI works
  • Describe generative AI model types
  • Describe generative AI applications

Generative AI: what it is

Generative AI is an AI technology that can produce new content, such as:

  • text
  • images
  • audio
  • synthetic data

AI vs. Machine Learning

  • Artificial Intelligence (AI): a branch of computer science focused on building intelligent agents that can reason, learn, and act autonomously (theory and methods for machines that think/act like humans).
  • Machine Learning (ML): a subfield of AI where a system trains a model from input data so the model can make useful predictions on new, unseen data, without explicit programming.

Supervised vs. Unsupervised learning (foundation for understanding GenAI)

  • Supervised ML

    • uses labeled data (data tagged with known outputs such as name/type/number)
    • learns from past examples to predict future values

  • Unsupervised ML

    • uses unlabeled data
    • focuses on discovery (e.g., clustering/grouping patterns in raw data)

Deep learning (where GenAI fits)

  • Deep learning is a type of ML using artificial neural networks (many interconnected neuron-like nodes).
  • Deep learning is typically better at learning complex patterns.
  • Semi-supervised learning: trained on small labeled + large unlabeled datasets to improve generalization.

Generative AI position in the landscape

  • GenAI is a subset of deep learning
  • Uses neural networks and can involve supervised, unsupervised, and semi-supervised methods.
  • Large Language Models (LLMs) are also a subset of deep learning.

Discriminative vs. Generative models

  • Discriminative models

    • learn to classify/predict labels
    • learn relationships between features and labels

  • Generative models

    • learn probability distributions to generate new data/content
    • learn how to produce new instances similar to training data

How to tell “GenAI vs not” (practical rule)

  • Not GenAI when the output is:

    • a number
    • a class/label (e.g., spam vs not spam)
    • a probability

  • GenAI when the output is:

    • natural language (text/speech)
    • audio
    • images (or other generated media)

Traditional ML vs Generative/“foundation model” approach

  • Traditional supervised ML

    • uses training code + labeled data
    • produces predictions/classifications/clustering depending on the use case

  • Generative AI approach

    • uses training code + labeled data + unlabeled data
    • builds a foundation model
    • foundation models can generate:
      • text, code, images, audio, video, etc.

Definition (formalized in the video)

  • Generative AI: creates new content based on what it learned from existing content.
  • Training produces a statistical model.
  • Given a prompt, the model predicts an expected response and generates new content resembling what it learned.

Transformer-based power + key risk: hallucinations

  • Transformers drove a major revolution in NLP (2018).

High-level transformer structure:

  • encoder processes the input sequence
  • decoder learns how to decode representations for the task

Hallucinations:

  • nonsensical or incorrect phrases/text produced by the model
  • causes mentioned:
    • not enough training data
    • noisy/dirty training data
    • insufficient context
    • insufficient constraints
  • impacts:
    • confusing or misleading outputs

Prompts and prompt design

  • A prompt is short text given to an LLM to control its output.
  • Prompt design is the process of writing prompts to get the desired results.

Model types when input is text (categories presented)

  • Text-to-Text

    • input: natural language
    • output: text (e.g., translation)

  • Text-to-Image

    • trained on images with captions
    • diffusion mentioned as a method

  • Text-to-Video

    • input: sentence/script
    • output: a video matching the input description

  • Text-to-3D

    • input: text description
    • output: 3D objects (useful for games/3D worlds)

  • Text-to-Task

    • input: text
    • output: an action performed (examples: answering questions, searching, making predictions, navigating UI, making doc changes via GUI)

  • Foundation models

    • large, pre-trained models adaptable/fine-tunable to many downstream tasks
    • examples of downstream tasks:
      • sentiment analysis, image captioning, object recognition
    • industry impact areas listed: healthcare, finance, customer service; also fraud detection and personalized support

Foundation models via Vertex AI

Vertex AI Model Garden includes:

  • Language foundation models: chat, text, code
  • Vision foundation models: includes stable diffusion (high-quality images from text)

Examples of task-specific use:

  • sentiment analysis for customer sentiment
  • occupancy analytics for vision use cases

Generative AI applications (with code example)

Example: code generation (using Gemini)

  • The user provides a code conversion goal (Python DataFrame → JSON).
  • Gemini returns steps and outputs the result in JSON format.

Gemini code generation can help with:

  • debugging code line-by-line
  • explaining code
  • crafting SQL queries
  • translating code between languages
  • generating documentation/tutorials

How Google Cloud helps (3 offerings)

  • Vertex AI Studio

    • explore and customize generative AI models
    • includes:
      • library of pre-trained models
      • tools for fine-tuning
      • tools for deploying to production
      • developer community forum

  • Vertex AI / Vertex AI Agent Builder (formerly Vertex AI Search and Conversation)

    • for low/no coding and little/no ML experience
    • create:
      • chatbots, digital assistants
      • custom search engines
      • knowledge bases
      • training applications, etc.

  • Gemini (multimodal AI model)

    • not limited to text; can analyze images and audio and interpret programming code
    • described as adaptable/scalable for diverse applications
    • Model Garden continuously updated with new models

Instructional / methodology-style elements (detailed bullets)

A) Distinguishing what counts as GenAI output

  • If output “y” is:
    • a number → not GenAI
    • a class/label (e.g., spam vs not spam) → not GenAI
    • a probability → not GenAI
  • If output “y” is:
    • natural language (speech/text) → GenAI
    • audio → GenAI
    • images/video → GenAI

B) Typical ML training logic described (supervised learning)

  1. Input test values X are fed to a trained model f
  2. Model outputs a prediction Y
  3. Compare prediction to actual values
  4. If prediction differs from actual values:
    • treat difference as error
    • model reduces error via optimization until predictions align more closely

C) Prompt usage concept

  • User writes a prompt (short text)
  • The prompt is provided as input to an LLM
  • Prompt design aims to structure the prompt so the model generates the desired output

D) Generative AI process (high-level workflow described)

  • Gather:
    • training code
    • labeled data
    • unlabeled data
  • Train to create a foundation model (statistical model)
  • Provide a prompt
  • Model predicts likely responses and generates new content (text/code/media)

Speakers / sources featured

Speaker

  • Roger Martinez (Developer Relations Engineer at Google Cloud; course instructor)

Companies / systems mentioned

  • Google Cloud
  • Gemini (Google’s multimodal AI model)
  • LaMDA (mentioned as “Language Model for Dialogue Applications”)
  • Vertex AI
  • Vertex AI Studio
  • Vertex AI Agent Builder (formerly Vertex AI Search and Conversation)
  • Vertex AI Model Garden
  • Stable Diffusion (vision foundation model referenced)
  • Transformers (2018 NLP revolution; general architecture referenced)

Other examples/tools mentioned

  • Jupyter Notebook and Google Colab
  • Pandas DataFrame (example in code generation)
  • Example context: restaurant tip prediction; employee clustering example; cat classification example

Original video