Welcome to Module 1: Look Past the Label

See why product names and human-like interfaces are weak evidence, and learn the rule that guides this module.

Let us begin

Imagine three products.

• The first calls itself an AI assistant. It speaks in a friendly way.
• The second shows only a score. The score comes from a model trained on many examples.
• The third follows a list of rules. Its website calls it intelligent automation.

Which one is really AI?

You may want to choose the friendly assistant. It looks intelligent. But its look and tone do not tell us how it works.

In this module, you will learn a better way to answer.

Our main rule

We will use one rule again and again:

Describe the system before you give it a label.

This rule helps us avoid two mistakes.

First, we should not trust a product name without proof. Second, we should not force a complex product into one box. A real product may use rules, models, search, tools, and people together.

You do not need to learn one perfect meaning of AI. The term is broad. Its exact boundary is not always clear.

You do need to learn how to say:

• What we know.
• What we do not know.
• What proof we still need.

Pause and decide

Read this description:

A website receives a question. It searches a set of approved documents and returns matching passages. The product page calls it Generative Knowledge AI.

What can we say for sure?

We can say that the system searches for and retrieves passages. We cannot yet say that it creates new content. We also cannot say that it uses machine learning or works as an agent.

We need more information before we make those claims.

That is careful thinking. It is not a weak answer.

What you will do

At the end of this module, you will study eight systems. You will name the parts that are supported by facts. You will also find at least one case where the best answer is:

There is not enough information.

Next, we will learn five simple questions that help us describe any system.