1.1. What AI Is (and Isn't)

💡 First Principle: Artificial intelligence is the goal of getting software to do things that normally require human intelligence — recognizing a face, understanding a sentence, deciding a next move. Once you see AI as a goal rather than a single technology, the confusing soup of terms (machine learning, deep learning, neural networks) snaps into place as different means to that goal.

Why care? Because the exam constantly tests whether you can tell these terms apart, and because misusing them leads to bad design decisions in real life — like reaching for a giant deep-learning model when a simple rule would do. The mental model here is a set of nested circles: AI is the outer circle, machine learning sits inside it, and deep learning sits inside that. Each inner circle is a more specialized way of achieving the broader goal.

⚠️ Common Misconception: "AI and machine learning are the same thing." They aren't. Machine learning is one approach to building AI — the dominant one today — but AI also includes older rule-based systems that contain no learning at all. The terms get blurred in marketing because ML is everywhere now, but on the exam, treat AI as the umbrella and ML as a technique under it.