9How does it work?

It only learns what you show it

The machine in chapter 8 saw 18,000 pictures. This time you teach the machine.

Poco's photos go in Poco's box, Pong's photos in Pong's box.

Once they're sorted, press Poco. The machine gets it right.

It does one thing. It finds the three closest cards. If two of the three are Poco, it says Poco. The bigger side wins.

Those three stay on screen, so you can see exactly why it answered that way.

Now press a dashed friend. You never taught it that one.

It answered anyway, didn't it? It can't say I don't know. It only learned two boxes, so it has to pick one of them.

The machine isn't broken. That's just all it was shown.

A little deeper

For grown-ups, and for children who want more.

This classifier is k-nearest neighbours with k set to three: find the three most similar cards, take the majority. It is among the simplest methods in machine learning.

Simple was the point. Its reasoning shows on screen as it works. A neural network would do better and could not show you why.

When the cards lean one way, the answers lean with them. That is bias — not a fault in the machine but the ordinary consequence of what it was shown. Real cases where face recognition fails more often on certain ethnicities, or hiring filters score one gender down, have exactly this shape.

The dashed friend matters. With only two boxes learned, the machine picks one of the two, always. Nobody ever gave it the option of saying it does not know.