Chapter 0 - What AI Actually Is (And Isn't)

The Crux

You've probably heard AI will change everything. Maybe it will. But before we get carried away, let's understand what AI actually is-and more importantly, what it isn't. This chapter is about stripping away the mysticism and seeing AI for what it really is: optimization at scale.

If you walk away from this chapter with one insight, let it be this: AI systems don't understand anything. They optimize loss functions over training data. Everything else-the apparent intelligence, the creativity, the human-like responses-is an emergent property of pattern matching at massive scale.

The Problem: Everyone's Confused

Here's a conversation that happens every day:

Manager: "Can we add AI to this feature?" Developer: "What do you want it to do?" Manager: "You know, AI. Make it smart."

This is like asking "Can we add programming to this?" Intelligence isn't an ingredient you sprinkle in. So what is AI, actually?

Let's start by clearing up some terminology that causes endless confusion:

Artificial Intelligence (AI): The broadest term. Any system that exhibits behavior that appears intelligent. This includes everything from simple if-else rules to large language models.

Machine Learning (ML): A subset of AI where systems learn patterns from data rather than following explicit programmed rules.

Deep Learning (DL): A subset of ML using neural networks with multiple layers (hence "deep").

Large Language Models (LLMs): A type of deep learning model trained on massive text datasets to predict and generate language.

The confusion comes from the fact that these terms get used interchangeably in marketing, but they represent different levels of specificity. When someone says "AI," they might mean a simple decision tree or GPT-4-very different things.