Teach a Computer with Examples

In the last lesson you trained a tiny apple-or-banana classifier by moving a dot. Real AI learns the same way — but with millions of examples instead of ten. Let’s understand what was actually happening.

Labels are the secret

Every example you added had a label — “apple” or “banana.” Labels tell the AI what the answer is. Without labels, the AI has no clue what it’s looking at.

Lots of tiny rules

The AI doesn’t write one big rule. It writes lots of little ones, adjusting every time you add an example. The more examples you give it, the better its rules get.

What goes wrong

• Not enough examples. The AI guesses badly.
• Unfair examples. If you only train it on red apples, it thinks a green apple is a banana.
• Mislabeled examples.Feed it ten bananas accidentally labeled “apple,” and it learns the wrong lesson.

This matters for big AI too

When you hear about an AI being biased, it’s usually because its examples were biased. Garbage in, garbage out — that’s the oldest rule in computer science.