The Power of Computational Thinking

The following sections are included:

• Preface
• About the Authors
• Acknowledgements
• Contents

Computational thinking is an important skill set that computer scientists learn and use to solve problems. It’s so important that in many countries all school kids are now expected to learn the skills. What is it, though? How has it changed the way we do just about everything? And why is it the basis of so much fun?

One of the worst medical conditions imaginable is locked-in syndrome. It leaves you totally paralysed except perhaps for the blink of an eye. Your intelligent mind is locked inside a useless body, able to sense everything but unable to communicate. It could happen to anyone, out of the blue, as a result of a stroke. If you wanted to help people with locked-in syndrome, the obvious thing might be to become a doctor or nurse, but how might a computer scientist help?

The skills you need to be a great stage magician, inventing magic tricks, are the same as those you need to be a great computer scientist: computational thinking. Magic tricks are algorithms and that is all a computer program is too. When early computers searched for data, they were actually doing a magic trick called the Australian Magician’s Dream. Computer programmers really are wizards!

How do we solve logic puzzles? Logical thinking is obviously a core part of doing puzzles, but generalisation and pattern matching are the secret skills of experts. They are used in solving puzzles, in doing computer science, and in many other areas too, from playing chess to fighting fires. Logical thinking, generalisation and pattern matching are all central to computational thinking, too.

Find a way for a chess knight to visit every square on a board exactly once, solve a problem for a city tour guide, and finish off by advising a tourist information centre. Do all three better as a result of some computational thinking. Oh and help the tourists to pack too. Algorithms are at the heart of computational thinking, allowing us to solve a problem once and then avoid having to think about it ever again. However, choosing a good representation for the information involved is another important part of computational thinking. Do it well and your algorithms will be so much easier to come by.

Having seen the basics of computational thinking, let’s explore how it might lead to building minds for robots. Building robot bodies is fun, but without a mind what can they do? We investigate the history of robot building, ask what understanding is all about, and then build a chatbot mind.

Rather than trying to engineer a mind directly, we’ll now use computational thinking to build a simple brain from the bottom up. It will help us explore how our brains, built of lots of nerve cells linked together, actually work, and how this leads to the complex behaviour of humans. We will then start to see how one might create an Artificial Intelligence that doesn’t just do what we do, but does it because it has a brain that works the same way. Perhaps if we manage to accurately build a brain, a mind will just emerge.

Having seen the basics of brain and chatbot building, and how some chatbots have been turned to the Dark Side of computing, let’s now put it all together. It’s time to explore how we might build a simple bot brain that can con humans. As a result, we will see why computational thinkers, whether human or machine, need ethical thinking too. Robots first entered the scene with world domination on their minds, so we will also explore some reasons why robots may never manage to rule the world.

Grids and games can matter. Grids form the basis of many games. They also matter a lot in computing. They are a way to represent images, for example, and a grid-based game about life even led to a whole new way to do computing. Rules for another grid-based game will help us explain why humans are no longer the best game players on the planet. Computer scientists are even now turning life itself into a game.

Pattern matching is at the heart of computational thinking, and patterns are everywhere. Computer scientists need to be good at seeing patterns, but also to be good at building algorithms that work with patterns. We can understand the ideas by looking at pattern matching in the algorithms behind mind reading magic tricks. Using generalisation in the form of mathematical theorems, we can devise both tricks and other algorithms that we can be sure work. The same ideas are central in the powerful algorithms that allow computers to see the world as well as humans can. By creating algorithms that can both discover and then use patterns, we can create ever more useful programs. We are teaching computers to do computational thinking so they can do human things as well as we can.

Modern healthcare relies on computer technology and the cunning computational thinking behind it. However, you need mathematicians and, scientists to come up with the basics first. The computer scientists and electronic engineers then create algorithms and electronics that turn the science and maths into technology that saves lives. Let’s start with a game to see how.

Computers follow their programs precisely, doing exactly what the instructions tell them to do. Does our brain work like a computer? Are we logical? Can we follow our plans as accurately as a computer does? It turns out that our brains have some serious limitations and it leads to some pretty strange things. Understanding the weirdness helps us build better technology. One thing is clear though. Your brain is in control, not you.

We’ve run through lots of examples of computational thinking in action. Hopefully you now have a general idea of what computational thinking is all about, and how the different elements, like abstraction and algorithmic thinking, come together to give you a powerful way to both solve problems and understand the world. In this final chapter, we will run through all those different components that we have seen contribute towards the thing we call computational thinking.

The following sections are included:

• Further Reading
• Index