# Understanding machine learning: the intelligent piece of paper

Machine Learning is about machines doing specific tasks ‘intelligently’. There are several ways to try to make a machine do this. For example:

• Rule-based systems (writing rules explicitly)
• Neural networks (copying the way our brains learn)
• Genetic algorithms (copying the way evolution improves species to fit their environment)
• Bayesian Networks (building in existing expert knowledge)

We will explore each in turn, before looking at social and ethical issues of machine learning. First though let’s look at rule following

## Intelligence & rules: the intelligent piece of paper

The first step in understanding Machine Learning is to understand how computers might be able to do things ‘intelligently’ without learning. This can be illustrated with our ‘Intelligent Piece of Paper’. We claim our intelligent piece of paper is more intelligent than most humans! How could this be true? It is just a piece of paper.

Why do we claim it is intelligent? Because it is better than most humans at its favourite game: Noughts and Crosses. It has played hundreds of times and has never lost. Of course if both players play well then any game of Noughts and Crosses should be a draw.  Our piece of paper has drawn about half the games it has played but won the rest. If you didn’t know you were playing a piece of paper you would think it is rather smart! (for example, if it stayed in another room and we just texted you its moves)

What did we do to the piece of paper to make it smart? We wrote on it some rules: rules to play the perfect game of noughts and crosses. Follow what it tells you blindly and you will play the game perfectly even if you’ve no idea what you are doing. Is it intelligent if it is just about following rules? That is all any computer does. If the paper isn’t intelligent because it is just following rules, then no computer could ever be as that is all they do.

Here are its rules for going first (you need the rules on its other side too if you are going to make it play second ever!):

```I am X, and I go first.

Move 1:
Go in a corner.

Move 2:
IF the other player did not go there
THEN go in the diagonally opposite corner to move 1.
ELSE go in a free corner.

Move 3:
IF there are 2 Xs and a space in a line THEN go in that space.
ELSE IF there are 2 Os and a space in a line THEN go in that space.
ELSE go in a free corner.

Move 4:
IF there are 2 Xs and a space in a line THEN go in that space.
ELSE IF there are 2 Os and a space in a line THEN go in that space.
ELSE go in a free corner.

Move 5:
Go in the free space.```

To write the rules – the algorithm – we had to work out a perfect strategy to win the game. We played lots of games in a structured way. We played a game and noted the result, then took back the last move and tried another again noting the result, and so on. We then took back two moves. Eventually we worked out which moves guaranteed we would not lose and turned them in to rules.

Arguably the intelligence is not in the rules but in coming up with them – in learning how to play. However, we can easily turn the above strategy we followed into an algorithm for working out how to play perfectly. Would those new rules be intelligent then? They came up with the way to win!

Perhaps intelligence itself is not the point, just being able to do specific tasks. It is being able to learn that matters whether that is intelligent or not. If we can create algorithms that can learn then we can create programs that can do all sorts of things well. That is where Machine Learning comes in…

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