Module 1 Wrap-up: Mapping the AI World
Reviewing the AI landscape and testing your ability to distinguish between different AI types.
Module 1 Wrap-up: The Strategic View
You have successfully defined the borders of the AI world. You know that AI is the goal, Deep Learning is the engine, and Generative AI is the new superpower of content creation.
Hands-on Exercise: Discriminative vs. Generative
Look at the following real-world features. Identify which ones use Discriminative AI (Labeling/Predicting) and which use Generative AI (Creating).
- FaceID: Unlocking your phone by recognizing your face.
- Stable Diffusion: Creating a picture of a "Viking astronaut" from a text prompt.
- Gmail Smart Reply: Suggesting "Thanks!" or "Got it!" as a response.
- Amazon Fraud Detection: Blocking a transaction that looks "stolen."
- GitHub Copilot: Writing a whole function based on a comment.
Module 1 Summary
- AI mimics human intelligence via pattern recognition.
- GenAI is a subset of Deep Learning that creates new data.
- Discriminative AI categorizes; Generative AI synthesizes.
- The Transformer (2017) changed the speed and scale of AI development forever.
💡 Guidance for Learners
As you move into Module 2, hold onto this idea: Generative AI is just a very advanced "Next Word Predictor." Everything you see (writing code, poetry, legal briefs) is the result of predicting the most statistically likely next token.
Coming Up Next...
In Module 2, we look inside the engine. We will explore Large Language Models (LLMs) and finally understand what sits behind the "Chat" box.
Module 1 Checklist
- I can explain the difference between AI and GenAI.
- I can give an example of a discriminative model vs a generative model.
- I understand that Transformers are the foundation of modern LLMs.
- I can identify at least 3 everyday uses of AI.