The AI Landscape &
Key Terminology
Before diving into Generative AI, we must demystify the "Russian Doll" structure of the field. This module clarifies the difference between AI, ML, and Deep Learning.
The Hierarchy of Intelligence
Many people use "AI" and "Machine Learning" interchangeably, but they are distinct. Think of them as concentric circles. Generative AI (the technology behind ChatGPT) is a very specific, modern subset of Deep Learning.
The AI Landscape
Understanding how Generative AI fits into the bigger picture.
Artificial Intelligence
Any technique that enables computers to mimic human intelligence.
Machine Learning
Algorithms that learn patterns from data without being explicitly programmed.
Deep Learning
Neural networks with many layers, capable of learning complex patterns.
Generative AI
Subset of DL that can create new content (text, images, code).
Speaking the Language
To make strategic decisions, you need to understand the vocabulary of your engineering team. Below is a decoder for the most common technical terms you will encounter in enterprise AI projects.
Core Terminology Decoder
Mapping technical buzzwords to actual business value.
Neural Networks
A computing system inspired by the biological neural networks that constitute animal brains.
The engine under the hood. Used for complex pattern recognition like predicting stock market trends or detecting subtle fraud patterns.
How Machines Learn
Computers don't "know" things; they learn patterns. The method of learning depends entirely on the goal. You wouldn't use the same teaching method for a math student (Right/Wrong answers) as you would for an art student (Creative exploration).
How Machines Learn
Matching the learning style to the business problem.
Supervised Learning
The model learns from labeled examples (Input + Correct Output).
Unsupervised Learning
The model finds hidden patterns in unlabeled data on its own.
Reinforcement Learning
The model learns through trial and error based on rewards/penalties.
The Fuel: Data Types
AI is only as good as the data it feeds on. Understanding the difference between Structured data (Excel, SQL) and Unstructured data (PDFs, Emails) is critical because they require completely different processing pipelines.
Data Taxonomy
Understanding the fuel that powers AI models.
1. Format
| ID | Age | Sales |
|---|---|---|
| 001 | 24 | $500 |
| 002 | 35 | $120 |
Organized in rows and columns. Easily searchable by traditional algorithms.
2. Annotation
Data tagged with the 'right answer'. Used for Supervised Learning (e.g., This email is SPAM).
Ready for the next step?
Now that you understand the basics, let's dive into the mechanics of Generative AI and how LLMs actually work.
Continue to Module 2