To understand AI, we must first understand how it differs from traditional programming.
In traditional programming, humans write the rules. We provide the computer with data (Input) and a set of instructions (Rules), and the computer calculates the Output.
Compare how software is built using hard-coded rules vs learned algorithms.
Input + Rules ➔ Output
As a developer, you write the logic. The computer reads the input, executes your predefined rule, and returns the output.
Input + Output ➔ Rules
You feed input and output examples to the training loop. The computer learns the weights and generates the rule itself!
| Input Data | Desired Output |
|---|---|
| 32.0°F | 0.0°C |
| 50.0°F | 10.0°C |
| 77.0°F | 25.0°C |
| 104.0°F | 40.0°C |
| 212.0°F | 100.0°C |
Machine Learning (ML) flips this on its head. Instead of writing the rules, we give the computer the Input and the desired Output, and we ask the computer to figure out the Rules!
Think of it like teaching a child to recognize a dog vs. a cat. You don't give them a rulebook about ear shapes and tail lengths. You point to a dog and say "Dog", then point to a cat and say "Cat". Eventually, they learn the pattern from the examples.
Determine which type of learning applies to each scenario. Select a badge to match.
Building an AI is not just about writing code. It follows a loop: Collect Data ➔ Train Model ➔ Evaluate ➔ Improve ➔ Deploy. In the next lesson, we'll talk about the absolute most important part of this loop: the Data!