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We are working towards endorsement of this title for the Cambridge Pathway.
Original material © Cambridge University Press & Assessment 2027. This material is not final and is subject to further changes prior to publication.
SA M
PL E
We are working towards endorsement of this title for the Cambridge Pathway.
Original material © Cambridge University Press & Assessment 2027. This material is not final and is subject to further changes prior to publication.
We are working towards endorsement of this title for the Cambridge Pathway.
Contents
Contents viii
How to use this book
x
Assessment pathways
xii
How to access your digital resources
xiii
Introduction
xiv
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How to use this series
1 Data representation and logic gates 1.1 1.2 1.3
Number systems and logic gates Text, sound and images Data compression
Practice questions
2 Logic circuits and hardware Logic circuits CPU architecture Data storage
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2.1 2.2 2.3
Practice questions
Data transmission Internet protocol addresses Packet switching The internet and the world wide web
65 69 84 96
114 122 124 129 144
4 Data integrity and cybersecurity
147
Data integrity Cybersecurity
150 163
Practice questions
194
5 Software 5.1 5.2
63
110
Practice questions
4.1 4.2
5 35 45
107
3 Data transmission and networking 3.1 3.2 3.3 3.4
1
197
Types of software and interrupts Programming languages, translators and IDEs
200 216
Practice questions
229
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CAMBRIDGE IGCSE™ AND O LEVEL COMPUTER SCIENCE: COURSEBOOK
6 Automated systems and robotics 6.1 6.2
230
Automated systems Robotics
233 250
Practice questions
261
7 Emerging technologies Artificial intelligence Quantum computers
Practice questions
8 Programming fundamentals 8.0 8.1 8.2 8.3 8.4
Introduction to programming Programming concepts Programming constructs String handling Programming operators
Practice questions
9 Algorithm design
Abstraction Decomposition Algorithm design and evaluation
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9.1 9.2 9.3
Practice questions
10 Further programming and testing 10.1 10.2 10.3 10.4 10.5
Variables and constants Procedures and functions Lists File handling Testing
Practice questions
11 Databases and structured query language (SQL) 11.1 11.2
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7.1 7.2
262
Databases Structured query language (SQL)
Practice questions
296
298 299 306 319 350 352 359
361 364 367 371 403
404 406 411 420 445 448 465
467 470 484 513
Appendix: Input and output devices
516
Glossary
521
Index
00
Acknowledgements
00
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How to use this series
How to use this series
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We offer a comprehensive, flexible array of resources for the Cambridge IGCSE™, IGCSE (9–1) and O Level Computer Science syllabuses (0478/0265/0984/2210) for examination from 2029. We provide targeted support and practice for the specific challenges we have heard that learners face: learning a new subject with English as a second language; programming skills; understanding theory; contextualising learning; and more.
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The Coursebook supports the full Cambridge IGCSE™ and O Level Computer Science syllabuses (0478/0265/0984/2210) for examination from 2029. Each chapter explains facts and concepts and uses relevant real-world contexts to bring topics to life, including case studies. Each chapter opens with a video and a computer science in context feature, which helps students understand why their learning is important, contains activities that build computer science and general skills and consolidate knowledge, worked examples, English unlocked features to enable deeper understanding of subject terminology, self and peer review activities, and much more. Questions and practice questions in every chapter help learners to consolidate their understanding. Answers can be found on Cambridge GO via the activation code found on the inside front cover.
The skills-focused Workbook has been carefully constructed to help learners develop the skills and knowledge that they need as they progress through their Cambridge IGCSE™ and O Level Computer Science courses, providing further practice for key topics in the Coursebook. The Workbook enables independent learning and is ideal for use in class or as homework and is appropriate for students taking both the print and digital pathways. Answers can be found on Cambridge GO via the activation code found on the front cover.
The front cover of this book features a pixelated rubber duck. This is a reference to both data representation and pixel art, but also the concept of “rubber duck debugging” in programming – all of which you will learn about in this book. Because of this, rubber ducks are a common symbol used by programmers and computer scientists! We have chosen this image to represent the course because computer science includes all sorts of fun and exciting things like digital art, music, AI, robots, collaboration, creativity, and much more. You will find out more about all of these things throughout this course.
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The Python Practice Book is composed of tasks that follow a scaffolded approach to programming skills development. This allows learners to gradually progress through ‘focus’, ‘practice’ and ‘challenge’ tasks to ensure that every learner is supported, building confidence and providing plenty of practice of novel scenarios. The Python Practice Book also contains sample answers, further practice questions and tasks that put the skills students have learned into practice to complete a project across all chapters. Answers can be found on Cambridge GO via the activation code found on the front cover.
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The Digital Teacher’s Resource contains detailed guidance for all topics of the syllabus, including common misconceptions to elicit the areas where learners might need extra support, as well as an engaging bank of lesson ideas for each syllabus topic. Differentiation is emphasised with advice for identification of different learner needs and suggestions for appropriate interventions to support and stretch learners. The Digital Teacher’s Resource also contains worksheets, PowerPoints and practice tests for each chapter. Answers for all components are freely accessible to teachers on the ‘supporting resources’ area of the Cambridge GO platform.
Knowledge Check is a collection of digital, auto-marked quizzes that assess students’ knowledge and understanding throughout a topic. It includes check-in quizzes to evaluate prior knowledge, subtopic quizzes to monitor progress, and topic evaluation quizzes to assess overall understanding. Teachers can track individual and class performance using reporting dashboards. Students must be set up by their teacher before accessing Knowledge Check. Available via Cambridge GO.
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How to use this book
How to use this book LEARNING INTENTIONS Each chapter begins with a set of learning intentions to explain what you will learn in the chapter.
BEFORE YOU START
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The Digital Coursebook (accessed via Cambridge GO) contains videos to accompany each chapter. Introductory videos are provided at the start of each chapter to introduce you to the topic. Within the programming chapter, there are worked example videos to demonstrate key concepts.
A short starter activity to help identify what you currently know about the topic and to introduce the concepts covered in the chapter.
COMPUTER SCIENCE IN CONTEXT
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This feature provides real-world examples and applications of the context in a chapter, encouraging you to look further into topics. There are discussion questions at the end which will ask you to think about the benefits and problems of these applications.
ENGLISH UNLOCKED
Provides helpful definitions or further understanding of complicated language. Sometimes, creative tips on how to remember a word are provided.
ACTIVITY
Activities help you to practise skills, understand the topic or demonstrate knowledge. They provide a good opportunity for you to think about what you have learned, take part in discussions, answer questions, or complete a practical task. Activities will help you to develop different learning styles and provide an opportunity for you to produce your own work either individually, or in pairs or in groups. Peer assessment
At the end of some activities and practical tasks, you will find opportunities to help you assess your own work, or the work of your peers and consider how you can improve the way you learn.
KEY TERMS
Key vocabulary is highlighted in the text when it is first introduced. An accompanying definition is given in the margin to tell you the meanings of these words and phrases. You will also find definitions of these words in the Glossary at the back of the book. Bold may be used elsewhere in the text for emphasis, but all key terms will have an accompanying definition in the margin.
TIP These tips help you to avoid common errors and provide advice.
Some activities provide a source file with instructions on how to use them, which are downloadable from Cambridge GO.
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WORKED EXAMPLE
COMMAND WORDS
Worked examples show you step-by-step how to work through a particular process or question. In some chapters, worked example videos are provided.
In each chapter, command words from the syllabus have been pulled out and will appear in feature boxes. These will remind you of what each command word means and which skills you should apply when answering different types of question. You can find the definitions for each command word in the Glossary at the back of the book.
DID YOU KNOW?
Questions
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These are fun and interesting facts about a topic or concept. These are not required learning from the syllabus.
Regular sets of questions throughout the book provide you with the chance to check your knowledge and understanding of what you have learned.
CASE STUDY
The case studies and accompanying questions allow you to actively explore real computer science scenarios.
PROJECT
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Each chapter contains a project that uses the skills you develop throughout the coursebook. The project encourages you to apply these skills to a real-world task in pairs or groups.
SELF ASSESSMENT CHECKLIST
The summary checklists provide you with a series of statements at the end of each topic outlining the content you should now understand. When you are revising, you might find it helpful to rate how confident you are for each of these statements. You should also provide an example to support your chosen statement. Learning intentions
Needs more work
Almost there
Confident to move on
Practice questions
At the end of each chapter, there are practice questions, written by the authors, to help you prepare for assessment.
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Introduction
Introduction Computers and our lives
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There is no area of our lives that is not dependent on computers. A computer probably recorded data at your birth and there will most likely be one there when you die. And, in between, computers will have an impact on every single aspect of your life. During the lifetimes of the authors of this book, we have gone from no personal computers at all – not even electronic calculators – to the everyday use of AI and the emerging technologies of robotics and quantum computers. It is easy to think that computers are in control of so much of our lives. But of course, they are not really. Humans control the computers and program them to carry out our instructions. Being able to program computers is therefore a highly desirable skill in today’s world. Studying Cambridge IGCSE™ and O Level Computer Science will help you develop this skill.
Why learn computer science?
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Learning about computers and how to use them is important because we use them in our daily lives. But that is not a good enough reason alone for you to study computer science and computer programming. The same argument could be applied to learning about houses; we could say that everyone needs to learn how to build a house because everyone lives in one. While it is true that it is useful to learn how to build a house, not everyone needs to know this or would be good at it. Learning how to use and do things is important and interesting but education is about much more.
Computational thinking
You should study computer science because it encourages you to think differently about how to solve problems. Computer science is all about writing problem statements and solving them in a particular and unique way. A computer scientist coined the term ‘computational thinking’ for the way in which problems are analysed, and solutions are created and tested. Solutions must be explained clearly and in language that cannot be misunderstood because they are going to be carried out by a computer: a mindless machine. If an instruction is not understood by a computer, it does not pause and try to work it out. It just stops and refuses to do anything! So, studying computer science will develop your problem-solving skills, which will be useful whatever you choose to do after your Cambridge IGCSE™. It will also help you to develop clarity of communication and creativity. Just as anyone can paint a picture, creating a work of art takes skill, creativity and effort. Similarly, producing a creative solution to a problem in a clever, simple and efficient way requires the same skills.
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CAMBRIDGE IGCSE™ AND O LEVEL COMPUTER SCIENCE: COURSEBOOK
About this book This Coursebook is divided into 11 chapters that provide coverage of the full Cambridge IGCSE™ , IGCSE (9–1) and O Level Computer Science syllabuses (0478/0265/0984/2201) for examination from 2029. At the start of each chapter, we have listed the expected learning intentions: what you should understand and be able to do by the time you reach the end of the chapter.
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Each chapter contains a Before you start activity to give you an idea of what the chapter is about and how much you already know. Computer Science in context and Case study features provide in-depth knowledge of how the ideas are applied to reallife situations, and Worked examples explain concepts and how to approach activities and questions. Throughout the Coursebook, there are activities and questions for you to complete. These will help you to see whether you understand the ideas covered and are able to apply the concepts to solve problems. At the end of each chapter, there are practice questions, written by us, to help you prepare for assessment.
We hope that this book assists you in your studies for this course and that it inspires you to study computer science in the future.
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Cat, David and Mia
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Chapter Chapter77
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Emerging Emerging technologies technologies LEARNING INTENTIONS In this chapter you will: •
understand what is meant by artificial intelligence (AI)
•
explore the main features of AI
•
explore common applications of AI
•
describe the structure of neural networks as the input layer, hidden layer(s) and output layer
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explore the purpose of machine learning including how neural networks are used
•
explore ways that AI is used in real-world scenarios
•
evaluate fairness and bias within data and AI systems
•
evaluate ethical and societal considerations of AI
•
evaluate the implications of AI, including its effects on employment, safety and environmental sustainability
•
describe the differences between a quantum computer and a classical computer
•
explain the advantages and disadvantages of using quantum computers instead of classical computers.
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BEFORE YOU START
KEY TERM
Both humans and machines learn by recognising patterns and this is key to how artificial intelligence (AI) works.
artificial intelligence (AI): a field of computer science dedicated to creating systems that can simulate human intelligence and perform tasks that typically require human thought
2
Look at the images here. For each image, write down: a
your guess for what the object in the image is
b
your confidence score e.g. 75% sure.
Discuss with the person next to you: a
How did you reach your guess?
b
How did your confidence level change the more you looked at the image?
c
How is this process of recognising a pattern similar to how computers learn? A
B
D
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F
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7 Emerging technologies
7.1
Watch Introduction video 7: What is generative AI? Cambridge GO.
COMPUTER SCIENCE IN CONTEXT How AI learns
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Think about how you learned to read. You did not start by memorising every word in the dictionary. Instead, your parents and teachers showed you lots of examples of letters, words and sentences. Over time, your brain gradually started to recognise patterns, like the sound a letter makes and the meaning of a sentence. This process of learning through experience is exactly what computer scientists are now teaching computers to simulate through AI (Figure 7.1).
Figure 7.1: Learning to read is all about recognising patterns, just like how AI systems learn
For a long time, computers could only do exactly what humans told them to do with code. If they encountered something new, like a word they had not seen before, the program would often fail. Today, AI is different. Instead of telling a computer specific rules, it can be given an objective and lots of data. For example, to teach an AI to recognise a cat, we do not code a list of features such as ‘whiskers’ or ‘pointed ears’. We simply show the system millions of labelled photos of cats and ‘not-cats’. The AI then works by itself to identify the patterns that separate a cat from other animals or objects. The more data it processes, the better and smarter it becomes. This is why computers can now achieve things that a few years ago we could only read about in books or saw in movies. They power systems that know what you want to watch next, they help doctors to analyse complex medical images, and they are the brains inside self-driving cars.
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CONTINUED
COMMAND WORDS
Discussion questions
show (that): provide structured evidence that leads to a given result
Imagine you want to teach a computer to recognise different types of cloud. What kind of data would you need to show it?
2
Give an example of a simple task that you do every day that could be done by an AI.
ENGLISH UNLOCKED
give: produce an answer from a given source or recall / memory
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To simulate something means to create a copy or a ‘fake’ version of a real-life situation. In computer science, we use programs to simulate how things work in the real world, like how a bridge stays up in a storm or how a car moves. In AI, we often simulate how the human brain functions, such as learning from mistakes or recognising patterns. Think of simulation like a game, such as a flight simulator. The game is not the real world but acts just like it!
7.1 Artificial intelligence
AI is a field of computer science that is rapidly evolving. It involves teaching machines and computers to simulate intelligent behaviour and perform tasks that would usually require human thought. AI systems are designed to think, learn and solve problems artificially, in a similar way to humans.
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AI is commonly used in our daily lives. For example, it powers recommendation systems on streaming services, to suggest movies or music that you may like. Also, voice assistants in your home use AI to understand and answer your questions (Figure 7.2), and AI is built into many web browsers to help you find the information you are looking for online. If you need a reminder about the internet and world wide web, look back at Chapter 3.
Beyond our daily lives, AI is being used in self-driving cars to interpret the environment, in medicine to help with discovery and diagnosis, and to analyse weather data to predict climate events.
ENGLISH UNLOCKED
Figure 7.2: A smart home device uses AI to understand what you’re saying and answer your questions
A diagnosis is when someone identifies a problem like a disease by looking at the issues or symptoms and comparing them to a documented problem.
DID YOU KNOW?
The concept of AI is not new! The term ‘artificial intelligence’ was first used in the 1950s at a conference where researchers were exploring the idea of machines that could learn and simulate thinking.
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7 Emerging technologies
Characteristics of AI AI systems usually have several key characteristics that enable them to simulate human behaviour.
Data and rules AI systems are built on two core components: data and rules. First, they require the collection of large amounts of data, including images, text and numerical data. Then, they are usually programmed with rules for applying that data, to process, interpret and respond to information.
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For example, in medicine, the data might include symptoms and test results with the rules defining how to interpret this data based on medical knowledge, as shown in Figure 7.3. This can support doctors by suggesting diagnoses.
Figure 7.3: AI can help doctors find patterns and suggest diagnoses
WORKED EXAMPLE 7.1
Imagine an AI system designed to help interpret symptoms. The system would be built on a large amount of medical data, such as a list of symptoms like headache, dizziness, runny nose and fever. The rules would be used to help interpret this data. For example: •
IF symptom = head pain AND dizziness THEN suggest headache
•
IF symptom = head pain AND runny nose THEN suggest common cold.
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Processing large datasets Imagine you have a huge amount of information, like all the photos ever taken or every message sent on a social media app. It would be impossible for a human to look through it all and find patterns. A key characteristic of AI is its ability to help us process these large datasets. To process a large dataset, AI takes all the raw data and uses it to create new, useful insights and understanding. AI is great at this because it can: find hidden patterns, connections and trends in data
•
identify and highlight important pieces of information
•
sort data into groups
•
pull out names, places or feelings from documents, or large amounts of text
•
make predictions about what might happen in the future based on past data.
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This is especially useful in industries such as science, where scientists need to analyse huge amounts of research data to make new discoveries or predictions. It is also useful in business, to help companies understand what their customers like and to predict what they might buy.
Reasoning
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Another key characteristic of AI is the ability to reason. AI can take the data it has collected as well as the rules it has been told to follow and apply logic to draw conclusions, make decisions and solve problems. This allows AI systems to respond intelligently to requests and situations. For example, an AI opponent in a game of chess uses reasoning to analyse the board, apply the rules of chess and decide on the best move to win the game.
DID YOU KNOW?
In 1996, IBM’s Deep Blue became the first computer to defeat a world chess champion, Garry Kasparov, in a match, as shown in Figure 7.4. This was a landmark moment in the history of AI!
Figure 7.4: Deep Blue won its first match against Garry Kasparov
Learning and adaptability
AI systems can also learn and adapt. This means they can improve over time without needing to be reprogrammed for new scenarios. AI can identify patterns in new data, adjust its rules and improve its decision making, becoming more accurate and effective over time.
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7 Emerging technologies
For example, AI is increasingly being used to personalise learning, as illustrated by Figure 7.5. Over time, the AI will learn the user’s strengths and weaknesses as they answer questions, and it will adapt to provide more practice questions on areas of challenge for the user.
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AI systems learn through various methods, including supervised learning, unsupervised learning and reinforcement learning.
KEY TERMS
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Figure 7.5: AI can help you with your learning by creating a personalised experience
Supervised versus unsupervised learning
Supervised learning is where the AI system learns from labelled examples, whereas unsupervised learning is where the AI system explores unlabelled data unguided to find hidden patterns. Table 7.1 outlines the key features of and differences between supervised and unsupervised learning.
supervised learning: an AI system method for learning based on the processing of a large dataset of labelled examples; it is guided by the examples unsupervised learning: an AI system method for learning by exploring unlabelled data to find hidden patterns
Feature
Supervised learning
Unsupervised learning
Data type
Labelled data: the data includes the ‘answer’ or a target variable.
Unlabelled data: the data has no predefined ‘answers’ or a target variable.
Learning process
The system is guided by the labels to learn the relationship between the input data and the correct output.
The system operates without guidance. It explores the data on its own to find hidden patterns and relationships.
Main goal
To predict an outcome, such as a category or value, based on prior experience.
To discover patterns and groups within data.
Example
Training an algorithm with emails labelled as ‘Spam’ or ‘Not spam’, so it can categorise new emails.
Feeding a database of customer purchases to an algorithm to automatically group similar customers, without prior knowledge of the categories.
Table 7.1: Supervised and unsupervised learning
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Reinforcement learning Reinforcement learning is where an AI system receives positive or negative feedback for its performance. For example, when a user gives an AI response a thumbs up or thumbs down. In many AI systems you can mark a response as good or bad to provide feedback; this reinforces its learning.
Questions An AI system is used to filter emails and classify them as ‘spam’ or ‘not-spam’. Describe how the AI uses data and rules to sort the emails.
2
Explain the key difference between supervised and unsupervised learning.
3
An online shop uses an AI system to predict the popularity of new products, to help them decide how much stock to buy. Explain how the AI might use large datasets, learning and adaptability to make these predictions more accurate over time.
Applications of AI
reinforcement learning: an AI system method for learning that relies on positive and negative feedback on its performance recommendation system: a type of AI system designed to predict user behaviour and preferences; they often use unsupervised learning to find patterns and compare with other users
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KEY TERMS
AI systems are used to simulate intelligent behaviour in a variety of ways. In this section, you will learn about different applications (uses) of AI systems, including recommendation systems, natural language processing and large language models, generative AI, computer vision, predictive analysis, machine learning and neural networks. You will consider different ways that AI can be used in different scenarios.
Recommendation systems
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Recommendation systems are a type of AI system designed to predict user behaviour and preferences. When you are looking for a movie to watch on your favourite streaming service, they often use recommendation systems to suggest what we might like to watch based on what we have watched in the past. These systems often use unsupervised learning to find patterns from our behaviour and compare this to other similar users.
DID YOU KNOW?
Many recommendation systems rely on cookies (Figure 7.6). Cookies are bitesize pieces of data stored by your web browser that track your activity. (Look back at Chapter 3 to remind yourself about cookies.) AI recommendation systems analyse your tracked data to build a picture of your preferences and generate personalised recommendations rather than display the same content for everyone.
Figure 7.6: The term ‘cookie’ comes from a programming term ‘magic cookie’ which is used to describe a piece of data that a program passes to another, so it has nothing to do with the cookies we eat!
COMMAND WORDS describe: state the points of a topic / give characteristics and main features explain: set out purposes or reasons, make the relationships between things clear, say why and / or how and support with relevant evidence
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7 Emerging technologies
ACTIVITY 7.1 a
Give three other places or websites where you have received personalised recommendations.
b
With a partner, discuss the advantages and disadvantages of recommendation systems.
Natural language processing
NLP is used to:
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Natural language processing (NLP) is a field of AI that focuses on enabling computers to understand, interpret and generate natural, human language. The goal is to bridge the gap between human communication and computer understanding, allowing us to interact with AI using everyday language. analyse text such as emails, documents and web pages
•
identify key information and sentiment in speech or audio recordings
•
generate human-like text, such as document summaries and email drafts
•
translate text or audio into different languages
•
answer questions that have been asked using human language. For example, Figure 7.7 shows a voice assistant that uses NLP to answer a question.
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KEY TERMS
natural language processing (NLP): a field of AI that teaches computers to understand, interpret and generate human language sentiment: the emotional tone or attitude expressed in a piece of text
Figure 7.7: Voice assistants are an example of NLP in action
Large language models
One of the most significant advancements within NLP has been the development of large language models (LLMs). They are powerful machine learning models that have been trained on large amounts of text data from the internet, books and other sources. (You will learn more about machine learning later in this chapter.) This training allows LLMs to better understand sentiment, context and meaning. As a result, LLMs are excellent at generating content, for a wide range of purposes and audiences, that sounds like it has been produced by a human.
KEY TERM large language model (LLM): a type of AI trained on large amounts of text data
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CAMBRIDGE IGCSE™ AND O LEVEL COMPUTER SCIENCE: COURSEBOOK
Generative AI Generative AI is an AI system that can create new and original content, such as text, images, audio and even code. LLMs are an example of generative AI, as they can produce text such as document summaries, email drafts and creative stories in human language.
generative AI: an AI system that can create new and unique content such as text, images, music and videos
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All generative AI models are now also trained on massive datasets of media, which allows them to create very realistic images and videos (Figure 7.8). This has potential applications in design, entertainment and advertising. Generative AI is rapidly changing how we interact with technology and the media.
KEY TERM
Figure 7.8: Pieces of art created with the help of generative AI on display at MIT Media Lab, Cambridge, Massachusetts, USA
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7 Emerging technologies
Computer vision Computer vision is a field of AI that trains computers to understand and interpret visual inputs, such as images and videos. It allows machines to: recognise objects in an image or video, such as identifying people, cars or traffic lights
•
classify and label images with specific categories. For example, this can be useful in making medical diagnoses based on X-rays or scans
•
recognise and identify faces. This is increasingly used in security footage or for checking people’s identification, as shown in Figure 7.9.
computer vision: a field of AI that enables computers to ‘see’ and understand digital images and videos
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KEY TERM
Figure 7.9: Computer vision is essential for security and facial recognition systems
Computer vision is essential for advances in robotics and automated systems, as it allows them to understand and interpret the environment around them. Two examples are given here.
•
In robotics, computer vision allows the robots to ‘see’. This helps a robot to navigate around, identify and interact with objects.
•
In automated systems, computer vision helps them to monitor the environment. For example, self-driving cars use computer vision to identify traffic lights, road markings, people and other vehicles. This is crucial when making decisions while driving.
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DID YOU KNOW?
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A CAPTCHA is a computer security tool used to identify that a user is a human rather than an AI. They are puzzles that ask you to identify objects or distorted text. By solving it, you prove you are not a robot through analysis of your mouse movements, response times and interactions. You are often helping to train AI systems by completing CAPTCHA puzzles! By correctly identifying images or text, you provide valuable labelled data that machine learning models use to improve their ability to recognise and understand the world, making AI smarter. (You will learn more about machine learning later in this chapter.)
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Predictive analysis Predictive analysis is an application of AI that analyses data and uses statistics to predict future events or behaviours. This process is useful in a wide variety of contexts, such as in the following examples. Businesses often use predictive analysis to predict customer buying habits. This helps them to manage their stock.
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Banks often use predictive analysis to predict economic changes, so that they can make decisions about loan applications.
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In healthcare, predictive analysis can be used to identify patients who are at high risk of certain conditions, based on their medical or family history.
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In environmental science, predictive analysis is used to predict climate change scenarios, by analysing historical weather and climate data, as shown in Figure 7.10.
predictive analysis: a process where AI uses information about what happened in the past to identify patterns and make a ‘best guess’ about what will happen in the future
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KEY TERMS
Figure 7.10: Predictive analysis helps predict disaster events, such as the path a hurricane might take. This helps people to prepare and evacuate the area
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Questions 4
Imagine an AI system that suggests new songs you might like based on your listening history. Is this an example of supervised learning or unsupervised learning? Briefly explain why.
5
A smart home assistant uses AI to understand commands. Identify three ways that NLP might be used in a smart home assistant.
COMMAND WORD identify: name / select / recognise
Machine learning
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Machine learning is a form of AI that enables a machine learning model to learn from data without being programmed. Instead of following a fixed set of instructions, machine learning algorithms identify patterns in data to create the model. The model uses the patterns to make decisions and predictions. They automatically adapt their own processes and data. The more data a machine learning model is trained on, the better it becomes at its task.
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Recommendation systems are often powered by machine learning. Instead of being programmed with specific rules like ‘if a user buys product A, recommend product B’, the system is given large amounts of historical purchase data. The machine learning algorithm analyses this data to identify patterns about what items customers buy. It then uses these to make personalised product recommendations (Figure 7.11). As it gets more data, the algorithm continues to learn and improve.
KEY TERMS
machine learning: a branch of AI that gives computers the ability to learn from data without being programmed to do so machine learning model: the output of a machine learning algorithm after it has been trained on a dataset and uses the patterns it has learned to make decisions or predictions machine learning algorithm: the sets of instructions that a computer uses to learn patterns from data
Figure 7.11: Compared with your friends, you might see different products advertised, because your browsing histories vary. These recommendations are powered by machine learning
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The main purpose of machine learning is to enable a computer system to make predictions based on past data. Instead of a person programming lots of rules, the system can look at the data to find its own patterns.
Neural networks
KEY TERMS
Neural networks are a type of machine learning inspired by the human brain. It is a system of interconnected parts that work together to solve difficult problems. They are made up of neurons, which is also a type of cell found in the human brain. Each neuron is like a tiny processor that receives information, performs calculations and produces an output. Just like biological neurons which receive signals from other neurons and send them onwards, artificial neurons in a network process incoming data and transmit their output to other neurons in the system.
neural network: a type of machine learning model that is based on the human brain
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neuron: in computer science, the most basic unit inside a computer’s neural network (in biology a neuron is a nerve cell that transmits information in the brain and other parts of the body)
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Think of a neural network like a team of tiny, interconnected nerve cells, called neurons, working together. In biology, a neuron is a nerve cell that transmits information in the brain and other parts of the body. When you learn something new, your neurons make new connections and strengthen old ones (Figure 7.12). Neural networks are similar. A neural network adjusts its internal connections as it learns from data, getting better at its task over time; just like your brain gets better at a skill with practice!
Figure 7.12: A biological neural network
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A neural network is made up of layers: •
the input layer receives data, such as pixels in an image or words in a sentence
•
hidden layers process data and find patterns; a neural network can have one or many hidden layers. This is where the learning happens
•
the output layer produces the result of the network. For example, identifying an object in an image or translating a sentence.
Every neuron in one layer is connected to every neuron in the next layer, therefore making up the neural network, as shown in Figure 7.13. Hidden Layer
Output Layer
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Input Layer
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Figure 7.13: A diagram of a neural network
Each connection between the neurons in the network is given a ‘weight’. The weight represents the importance of the input coming from that connection. High weight means the input from that connection is very important for the next neuron’s decision making. Low weight means the input is not very important and can be ignored. The network learns by adjusting these weights during a process called training. After the network makes a prediction, it compares the result to the correct answer to check how much of a mistake it made. It then sends an error signal back through the connections to slightly adjust the weights and improve the next prediction.
WORKED EXAMPLE 7.2
If a neural network was trying to decide whether an email is spam or not, it would consider: •
the sender: is it from someone you know? (High weight)
•
keywords: does it contain words often found in spam emails, such as ‘prize’, ‘winner’ or ‘free money’ (High weight)
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grammar and spelling: are there many mistakes? (Medium weight)
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subject: is the subject unusual? (Medium weight)
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the colour of the text: is the text brightly coloured? (Low weight)
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CONTINUED
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Each factor is assigned an initial weight to tell the program how important it is in deciding whether an email is spam. If an email has many keywords, it will probably classify it as spam but if it is from a known contact, it will likely not classify as spam, even if it detects some keywords.
ACTIVITY 7.2
Imagine you are developing a music streaming app. You need to decide which data is most important for recommending a new song to a user. Your neural network needs to decide whether to recommend a song, to a user that: •
likes the genre Indie-Rock
•
usually listens to music in the evening
•
always skips songs with heavy drums.
Complete the table by assigning a weight (High, Medium or Low) to each input. Include an explanation for your choice.
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CONTINUED Input data
Weight
Explanation
Genre: Is the song Indie-Rock? Time of day: Is it currently evening?
Song length: Is the song 3 minutes long? Album art: Is the album cover blue?
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Instruments: Does the song have heavy drums?
By repeating the process with many examples, the network learns and adjusts these weights over time until it can make more accurate predictions. It learns which features matter most for specific tasks. This makes neural networks particularly good at tasks that involve pattern recognition, such as recognising images and understanding speech and language.
Fairness and bias in AI
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Fairness in AI means ensuring that the system’s decisions and outcomes are not discriminatory. Bias is when a system wrongly favours or is prejudiced against certain groups of people or ideas, leading to unfair results. This bias usually comes from one of two places: the data or the algorithm.
ENGLISH UNLOCKED The language of fairness
Fairness means being fair and equal to everyone. A fair system treats every person the same way, no matter who they are. A bias is a preference for one thing over another. Prejudice is a type of bias where someone forms an opinion without knowing all the facts. Discrimination is the unfair treatment of different groups of people.
Data bias
Data biases happen when training data does not properly represent the real world or includes old data that represents historical biases. For example, imagine an AI that recommends new video games or in-game purchases to players. If this AI is mostly trained on data from older players or those who prefer a certain genre, it might consistently recommend games that are not popular with younger people or might overlook new trends that appeal to a younger audience. This is because the data did not fully represent the diverse tastes of all players.
KEY TERM data bias: when the information used to train an AI does not include a wide enough range of examples, leading to unfair results
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Algorithmic bias
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The word genre describes a specific type or category of something. For example, in video games, the genre could be ‘action’, ‘racing’ or ‘puzzle’. If you have a diversity of tastes, it means you like lots of different things, for instance lots of different game genres. AI data needs to be diverse so that it can understand different types of people and ideas.
Algorithmic bias is when the system has been designed or coded to produce an unfair result, even if the data is unbiased. (See Chapter 9 to learn about algorithms.) For example, an AI in a smart speaker might be programmed with an algorithm that is less accurate at recognising higher-pitched voices or certain accents. If most of the developers testing the system had deeper voices, the algorithm might accidentally be less effective for people with different speech patterns, leading to frustration and an unfair user experience. To address these issues and ensure that AIs are equitable, it is important that AI companies and developers who train the systems: use diverse data
•
practice transparent algorithm design
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do thorough bias testing
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continue to monitor the systems.
algorithmic bias: when the code or instructions of an AI system are designed in a way that creates unfair results
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Equitable is another way of describing being fair and equal to everyone. An equitable system treats every person the same way, no matter who they are.
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ACTIVITY 7.3
b
In small groups, discuss the two following scenarios and explore how fairness in AI is challenged by bias. i
Imagine a generative AI tool that creates images of people for various professions (e.g. doctor, engineer, businessperson). If the AI was mostly trained on historical data showing people from one gender or ethnicity in high-ranking roles, what do you think the resulting images would look like?
ii
Consider a smart home assistant that is very reliable for one user in the house but frequently misunderstands the commands of another user. If the manufacturer’s testing only used a specific range of accents and speaking volumes and tones, what would be the impact on users?
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Design a pitch for an AI company about how they can address bias in one of the above scenarios. Your pitch can be a short presentation or a poster. It must include: •
a description of one potential data bias or algorithmic bias that could lead to unfair results
•
a recommendation for two steps the company should take to fix the bias and improve fairness.
AI ethics and society
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The rapid advancement of AI, and specifically generative AI, has led to several ethical and societal challenges.
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Ethics are rules that help us decide what is right and wrong. Ethical questions ask whether what we are doing is fair or right. Societal challenges are problems that affect many people at once such as how we work or how we stay safe.
Job displacement
Job displacement is a major societal challenge. As AI becomes more capable of automating repetitive (repeating) tasks, there is a risk to job roles in various sectors, such as factory work, data entry and customer service.
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Displacement happens when something is moved out of its usual place. Job displacement means that a human is moved from a job they would usually do, because an AI is doing it instead. These people need to find new and different jobs because their current roles are now carried out by an AI.
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This raises ethical questions about how governments and companies support the people affected by retraining them. While AI creates new jobs, these roles often need different skills. This means that if the people who lost their jobs cannot access the right training, they may find it difficult to do these new roles.
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Figure 7.14: As AI and automation advance, the nature of work changes. This raises questions about job displacement and the need for new skills
Accountability
Accountability is an ethical concern about who is responsible when things go wrong. If AI systems make mistakes, such as a self-driving car causing an accident or a medical AI providing an incorrect diagnosis, there are questions around who is to blame. Is it the fault of the programmer who wrote the code, the company that owns the AI system, the user who interacted with it, or the AI system itself ?
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To be accountable (or to have accountability) means that you are responsible for your actions and must be able to explain why something happened. If you are accountable, you accept responsibility if a mistake is made.
Figure 7.15: When AI systems make mistakes there are ethical questions around who is to blame
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Privacy and security AI systems often require vast amounts of personal data to function. This raises two main issues: privacy is an ethical concern about how your personal data is collected and used by AI. Unethical use of data can lead to people being tracked or tricked without their knowledge.
•
Data security is a societal concern that questions how your data is protected. Large databases of personal information can be a target for hackers. Systems must be very strong to prevent unauthorised access to data. (See Chapter 4 to be reminded about hackers and cybersecurity.)
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Figure 7.16: AI systems rely on large amounts of data, so they need to have very strong security measures in place to protect privacy
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Privacy is your right to keep your personal information to yourself. Security refers to measures, such as passwords and firewalls, that keep your private information safe from others.
Decision making
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Decision making is also an ethical challenge of using AI. As AI systems become more autonomous, their ability to make decisions without human input raises ethical questions. A lack of human supervision in some decisions could lead to negative outcomes. For example, a medical AI could provide incorrect diagnoses to patients, without human oversight (Figure 7.18). This could lead to incorrect treatments and potentially put patients at risk.
Figure 7.17: Without human oversight, AI could make decisions that have negative outcomes
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ACTIVITY 7.4 A top university receives 50 000 applications for only 500 spots. To save time, they use an AI ‘Admissions Officer’ to scan applications and automatically reject 90% of them based on grades, extracurricular activities and writing style. In pairs, choose one of the roles in the table. Each student should prepare three points for a two-minute pitch to the University board. Role B: The student representative
In your view, the AI is faster and more consistent than using a human Admissions Officer. It does not get tired or bored after reading 1000 essays. You believe that the AI uses a clear rule base to ensure that every application is judged using the same high standards.
In your view, a computer cannot understand a student’s passion or the story behind a drop in grades (for example, through illness).
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Role A: The time-saving expert
You believe the AI may be biased if it was trained on data from students who all come from similar, wealthy backgrounds.
Misinformation and deepfakes
A major societal challenge of generative AI is the spread of misinformation (false information). AI can be used to create deepfakes, which are realistic fake images, audio or video.
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This is an ethical issue because deepfakes can be used to trick people. It is also a societal issue because if we cannot tell what is real and what is fake, it reduces our trust in news, science and the media.
CASE STUDY: GENERATIVE AI ART
In 2022, an American man named Jason Allen created a piece of art called ‘Théâtre D’opéra Spatial’ using a generative AI tool called Midjourney, as shown in Figure 7.18. He created the image by prompting Midjourney with descriptive, text-based prompts before editing it with digital editing software.
KEY TERM
deepfake: media, such as an image, audio or video, that has been altered or generated to look or sound like someone; often saying and / or doing something that the real person has not said / done
Figure 7.18: ‘Théâtre D’opéra Spatial’ by Jason Allen using Midjourney, a generative AI tool
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CONTINUED He entered the piece into the Colorado State Fair’s annual fine art competition. It was up against 17 other images in the Emerging Artist (Digital Arts) category and it won first prize. After the event, many artists criticised his win on social media, claiming that it was unfair and plagiarised the work of other people because of the data that the AI had been trained on.
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Allen claimed that he had explained how he created the image when it was submitted and that he did not break any rules. However, examples like this raise ethical questions around copyright and the impact that AI will have on human artists and creators in the future.
Later that year, Allen tried to apply for copyright protection on his AIgenerated artworks, but in 2023 this was declined because of the minimal human creative input involved. (If you need a reminder, look back at Chapter 5 where you first explored copyright and copyright protection.)
Questions
The creator, Jason Allen, claimed that he informed the organisers of the competition of his use of AI on his entry form. Evaluate whether the award was a fair outcome.
7
Human artists claimed that the AI tool, Midjourney, used their existing artworks for training data without permission. Outline the ethical and societal implications of using copyrighted work to train AI models without the creator’s consent.
8
The US Copyright Office refused to grant Allen copyright protection for his image because of the minimal human input. Consider why this might be a fair or unfair outcome.
9
Describe two ethical considerations that software developers should address when creating an AI system for use in healthcare.
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10 Explain how generative AI can lead to ethical concerns and societal impacts, such as the spread of misinformation. 11 Suggest ways in which individuals and societies can prepare for the increasing integration of AI into daily life.
Implications of AI
AI is changing our world. Whereas it has the potential to solve some very difficult problems, it can also have negative implications (effects or outcomes). This section summarises many of the key positive and negative implications of AI in three key areas: employment, safety and environmental sustainability.
Employment
There are several ways in which AI is having both a positive and negative impact on employment.
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Positive implications Several positive implications of AI for employment are summarised here. AI creates new jobs, such as AI developers and data scientists (Figure 7.19).
•
AI can take over routine tasks, allowing human employees to focus on more complex or creative tasks.
•
AI can take over dangerous tasks, allowing human employees to remain safe.
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Figure 7.19: While AI can automate repetitive tasks, it also creates new opportunities for humans to work developing this new technology
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Negative implications
As AI becomes capable of automating repetitive tasks, such as factory work (Figure 7.20), data entry and some customer service roles, it can lead to job displacement in various sectors, which is an important negative implication of AI for employment. Job displacement raises ethical and societal questions about how to support workers who lose their jobs. The new roles created by AI are not necessarily accessible to all; ensuring accessibility is an ethical and societal challenge.
Figure 7.20: Many modern factories use AI automation to build products; this leads to job displacement
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Safety AI raises several new safety concerns but is also already being used in lots of ways to make the world safer.
Positive implications Several positive implications of AI for safety are summarised here. AI can scan large networks for unusual activity and detect complex cyberattacks or threats much faster than humans can. (Look back at Chapter 4 for a reminder about cybersecurity.)
•
AI models process weather, satellite and sensor data to predict natural disasters, giving people time to evacuate (Figure 7.21).
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AI can model how materials might fail in extreme situations, helping engineers to design safer buildings, cars and machines.
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Figure 7.21: AI analyses large amounts of weather and climate data, enabling more accurate predictions and better planning in case of emergencies
Negative implications
Two negative implications of AI for safety are summarised here. •
Generative AI can be used to create realistic fake images, videos and text that spreads misinformation.
•
Programming errors or unexpected system failures can have a direct impact on safety in specific contexts. For example, medicine or self-driving cars (Figure 7.22).
Figure 7.22: Unexpected technical errors in self-driving cars could cause accidents
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Environmental sustainability AI is a powerful tool for making systems more efficient and sustainable; however, the growing use of AI increases the demand for resources.
Positive implications Several of the positive implications for environmental sustainability are summarised here. Predicting when and where electricity is needed, allowing renewable energy to be used efficiently and reducing wasted energy.
•
Calculating the most efficient routes for cars, reducing fuel consumption and pollution (Figure 7.23).
•
Analysing weather data and soil conditions to apply the ideal amount of water and fertiliser needed. This reduces resource use and helps keep the land healthy.
•
Analysing large amounts of climate data to predict future climate change scenarios helping governments plan for adaptation.
•
Monitoring the environment in real-time. For example, using satellite images to track deforestation.
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•
Figure 7.23: AI powered GPS systems enhance sustainability by optimising routes and reducing fuel consumption
Negative implications
Several of the negative implication of AI for environmental sustainability are summarised here. •
Training large AI models requires huge amounts of electricity. If this energy comes from burning fossil fuels, it creates a lot of carbon emissions that can contribute to and worsen the effects of climate change.
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Digital data is stored in large data centres that use a lot of energy and therefore get very hot (Figure 7.24). Often these data centres are cooled using fresh water, which leads to water shortages.
•
AI relies on powerful hardware that is made from rare metals extracted from the earth. There are concerns that these metals could run out and extracting them damages the earth and ecosystems.
•
Hardware can also become quickly outdated, contributing to large amounts of e-waste ending up in landfill. It is not biodegradable, meaning it will not break down by itself. It will remain there forever.
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Figure 7.24: The growing demand for AI data processing leads to increased energy use in large data centres
ACTIVITY 7.5
Now that you’ve explored automated systems, robotics and AI, let’s look ahead. (If you need a reminder about automated systems or robotics, look back at Chapter 6.) Imagine what life might be like in 10 or even 100 years from now, given the rapid advancements in these technologies. 1
In pairs, discuss: a
what new automated systems might exist in homes, schools or cities?
b
what roles might robots play in daily life, work or leisure?
c
how might AI change the way we learn, communicate or solve problems?
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7 Emerging technologies
CONTINUED In pairs, choose one area of everyday life (such as home, transport or education). Create a poster showing how these technologies would interact and affect daily life in your imagined future. It should include a short description and a simple diagram.
3
Share your ideas with another pair or in small groups. Focus on both the potential benefits and challenges of these future technologies. Consider ethical implications, societal changes and new opportunities.
Peer assessment
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2
Evaluate another pair’s poster. Consider the following questions: •
how clearly did the poster communicate their vision of the future? Were the ideas easy to understand?
•
how well did they include automated systems, robotics and AI into their future world? Were the examples imaginative and relevant?
Questions
12 Outline two implications of AI on employment.
13 Describe one positive and one negative implication of AI concerning safety.
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14 Explain a negative implication of the growing use of AI on environmental sustainability.
7.2 Quantum computers
Quantum computers are extremely powerful machines that are built using the rules of quantum physics, the science of how our world works on a sub-atomic scale. They process information in a completely different way to standard computers and have the potential to solve complex challenges very quickly, such as analysing diseases and discovering new medicines.
Quantum computers versus classical computers There are three key differences between how quantum computers work, compared to classical computers and these differences are discussed in this section.
Data representation
In a classical computer, data is represented by bits. This number system is binary, meaning a bit can only be 0 or 1 (look back at Chapter 1 to remind yourself). In a quantum computer, data is represented by quantum bits (qubits), which can be 0, 1 or both at the same time. This allows quantum computers to store and process enormous amounts of data. Qubits are very sensitive and easily affected by their surroundings. To work properly and store quantum information, they must be kept in a stable and isolated environment. This means quantum computers must be cooled to very low temperatures.
KEY TERMS
quantum computer: a type of computer that uses the rules of quantum physics to process information quantum physics: a branch of science that studies how sub-atomic particles behave sub-atomic: describes anything that is smaller than an atom qubits: the basic unit of information in a quantum computer; unlike a standard bit (0 or 1), it can hold a blend of both states at the same time
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Logic gates As you learned in Chapter 1, classical computers use standard logic gates to process bits. These logic gates can only process one possibility at a time. Quantum computers do not use regular logic gates; they use quantum logic gates. These quantum logic gates allow qubits to work together so the computer can explore many different possibilities at the same time. This means a quantum computer can perform complex calculations much faster than classical computers.
Quantum hardware ENGLISH UNLOCKED
electrons: tiny, negatively charged particles of matter found in atoms; in quantum computers, their spin is often used to create a qubit photons: individual particles of light that have no mass and can be used as qubits or as messengers to link other qubits
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To make a qubit, scientists use tiny particles called electrons and photons.
KEY TERMS
You may have heard of electrons and photons in science. •
Electrons are a key component of atoms that help them to bond together to form different materials (Figure 7.25). Electrons are also vital for electricity and are found in all electrical circuits.
•
Photons are the tiny particles that make up light.
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Figure 7.25: Electrons are the tiny particles that orbit the nucleus of an atom
In some types of quantum computer, an individual electron is used as the qubit. The electron has a property called spin, which makes it behave like a magnet, pointing either up (0) or down (1). Photons can also be used to make qubits and send quantum information between electrons or other qubits. They are ideal for communication because they travel very quickly. Photons have a property called polarisation, which is the direction the light vibrates. This can either be vertical (0) or horizontal (1). To create and use a qubit, scientists must carefully control the properties of these particles using magnets, lasers and temperature to keep them still.
spin: a property of particles such as electrons, making them act like tiny magnets pointing either ‘up’ or ‘down’; this property is used to store the 0 and 1 states of a qubit polarisation: a property of photons, of either vibrating vertically or horizontally superposition: the ability of a qubit to exist in a combination of the 0 and 1 states simultaneously, allowing a quantum computer to explore many possibilities at once
Superposition
While a standard bit can either be 0 OR 1, a qubit can be 0, 1 or both at the same time. This is called superposition. A fundamental rule of quantum computers is that observing or measuring a qubit instantly forces it to choose a definite state of either 0 or 1. This means that you can only get a final answer from a quantum calculation after it is complete as measuring the qubit too early will destroy the quantum information.
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Imagine you have two doors. Behind one of the doors is a gold coin. With a classical computer, the coin is either behind Door 1 OR Door 2 (Figure 7.26). You must open one door at a time to find out whether the coin is there or not. With a quantum computer, superposition means the coin acts as if it is behind both doors at the same time. The computer does not need to choose which door to open first as it is able to check both at the same time. The moment you open a door to look for the coin, the superposition ends and the coin will be behind just one door. This means that quantum computers can solve problems much faster than standard computers.
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A standard computer with N bits can only check one possibility at a time. A quantum computer with N qubits in superposition can check 2N possibilities at the same time.
Questions
Figure 7.26: Using the rule of superposition, the gold coin acts as if it is behind both doors 1 and 2 at the same time
15 Compare the way data is represented in a classical computer versus a quantum computer using the terms bit, qubit and binary.
16 Explain how quantum logic gates differ from traditional logic gates when solving a complex problem. 17 Identify two types of subatomic particle used in quantum computers.
Advantages and disadvantages of quantum computers
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Quantum computers have the potential to solve some of the world’s greatest challenges, but this is still an emerging field that is limited by the speed of technological advancement. Table 7.2 outlines some of the advantages and disadvantages of quantum computers compared with classical computers. Advantages
Disadvantages
Able to solve complex problems much faster than classical computers due to superposition.
Qubits can be easily disrupted by heat, noise and vibration, which can cause errors.
Useful for simulating complex models that can speed up drug and materials discovery.
They are extremely expensive to build, operate and maintain.
Able to run advanced algorithms that can quickly solve complex mathematical problems.
Require the use of specialised algorithms and programming languages.
ENGLISH UNLOCKED
In Computer Science and Mathematics, we use the letter N as a placeholder. It simply stands for ‘any number’. If we say a computer has N bits, it means it could have 8 bits, 64 bits or even millions! Using N helps us write a rule that works for a computer of any size.
Their ability to solve complex problems quickly results in challenges to digital security, as they will be able to quickly break current encryption methods.
Table 7.2: The advantages and disadvantages of quantum computers
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CAMBRIDGE IGCSE™ AND O LEVEL COMPUTER SCIENCE: COURSEBOOK
ACTIVITY 7.6 You have a new student joining your class who has not learned about quantum computers yet. Work in groups to create an engaging introduction for them, such as role play, explanation video or animation. Your performance must cover: how qubits work and store information
•
the differences between traditional and quantum logic gates
•
how electrons and photons are used to make qubits
•
one advantage and one disadvantage of quantum computers.
Self assessment
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•
As a group, discuss the following reflection questions: •
how did this activity help your understanding of quantum computers?
•
what concept did you find the most confusing or challenging to explain?
•
what is one thing you will do next to improve your own understanding of quantum computers?
PROJECT: AN EQUITABLE AND SAFER FUTURE SOCIETY Work in small groups. Your task is to investigate and prepare a presentation for a debate on the following statement:
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‘Emerging technologies like AI and quantum computers will create a more equitable and safer future society.’ Your group will choose to argue either in agreement with or against the statement.
You will create a presentation that must include: 1
Two specific examples that show how AI applications will affect society to support your argument.
2
One specific example that shows how the impact of quantum computers on society supports your argument.
3
A strong, concluding statement that evaluates the overall implications of these technologies on society based on your arguments.
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7 Emerging technologies
SELF ASSESSMENT CHECKLIST After studying this chapter, think about how confident you are with each of the learning intentions. This will help you see any gaps in your knowledge and help you learn more effectively. Needs more work
Learning intention
Almost there
Ready to move on
I can understand what is meant by artificial intelligence (AI). I can explore the main features of AI.
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I can explore common applications of AI.
I can describe the structure of neural networks as the input layer, hidden layer(s) and output layer.
I can explore the purpose of machine learning including how neural networks are used. I can explore ways that AI is used in real-world scenarios.
I can evaluate fairness and bias within data and AI systems. I can evaluate ethical and societal considerations of AI.
I can evaluate the implications of AI, including its effects on employment, safety and environmental sustainability. I can describe how a quantum computer represents data using quantum hardware.
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I can explain the advantages and disadvantages of using quantum computers instead of classical computers.
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CAMBRIDGE IGCSE™ AND O LEVEL COMPUTER SCIENCE: COURSEBOOK
Practice questions 1
a
Describe what is meant by AI. [2] Describe four of the main features of AI.
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b
[4]
2
AI has the ability to learn and adapt.
Give three ways in which AI can learn and adapt. 1 2 3
[3]
3
Complete the table by writing the correct application of AI for each example.
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Example
Application
Estimate whether a machine is likely to fail.
Produce an image from a written description. Identify a defective product from an image.
Suggest a movie based on a previous viewing.
Reply to a question asked in everyday language.
4
5
[5]
Describe the structure of a neural network.
[3]
Explain the purpose of machine learning, including how neural networks are used.
[4]
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7 Emerging technologies
6
A school is considering using AI in its computer systems. There are many ways in which common applications of AI could be used in a school. Which application of AI could be used by the school to do the following? a
To suggest after school activities [1]
b
To answer student questions
c
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[1] To produce diagrams for learning materials
[1]
d
To identify objects brought into classrooms
[1]
e
To estimate predicted grades.
[1]
7
A common application of AI is generative AI.
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Explain the ethical and societal considerations of using this application to produce videos.
8
[4]
A common application of AI is generative AI.
Explain the ethical and societal considerations of using this application to produce videos.
[4]
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CAMBRIDGE IGCSE™ AND O LEVEL COMPUTER SCIENCE: COURSEBOOK
9
Traditional and quantum computers share some of the same features while also having some different features. For each feature, tick (✓) one or more type of computer that has this feature. Computer Feature
Traditional
Quantum
Stores data using qubits Uses photons to represent data Stores data in multiple states at the same time
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Uses logic gates to perform calculations Stores data using binary bits
Uses electrons to represent data
[6]
10 A quantum computer can be used instead of a traditional computer. Tick (✓) one correct statement that is an advantage of using a quantum computer. A
Less time needed to solve specialist problems
B
Less money is needed to build and maintain
C
Less specialised hardware is needed to operate
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a
D
b
Less suitable for all everyday computing tasks
[1]
Tick (✓) one correct statement that is a disadvantage of using a quantum computer. A
Processes many possible inputs at the same time
B
Processes small amounts of data at the same time
C
Less efficient at finding optimal solutions to a task
D
Errors can be caused by heat, noise and vibration
[1]
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We are working towards endorsement of this title for the Cambridge Pathway.
Original material © Cambridge University Press & Assessment 2027. This material is not final and is subject to further changes prior to publication.
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PL E
We are working towards endorsement of this title for the Cambridge Pathway.
Original material © Cambridge University Press & Assessment 2027. This material is not final and is subject to further changes prior to publication.