What you'll learn
This overview covers automated and emerging technologies and the ethical issues that come with them, as studied in CIE IGCSE Computer Science (0478). It explains automated systems — how sensors, microprocessors and actuators work together — and their use in industry, transport, agriculture, weather, gaming, lighting and science; robotics, including the characteristics of a robot and the uses of robots; artificial intelligence (AI), including expert systems and machine learning; and the wider ethical, legal and privacy issues raised by technology, including intellectual property and the impact on jobs. The dedicated guide on ethics, privacy and intellectual property goes into further depth on those issues.
Questions in this area often ask you to describe how an automated system works, identify the characteristics of a robot or AI system, or discuss the advantages and disadvantages of using a technology in a given scenario. Balanced, specific answers are rewarded.
Key terms and definitions
Automated system — A combination of software and hardware designed to work without human intervention.
Sensor — An input device that measures a physical quantity, such as temperature or light.
Microprocessor — A processor that receives sensor data, compares it with stored values and makes decisions.
Actuator — An output device that carries out a physical action, such as a motor, valve or pump.
Robotics — The branch of computer science concerned with designing, building and operating robots.
Robot — A mechanical device that has a physical structure, an electrical component and is programmable.
Artificial intelligence (AI) — A branch of computer science that deals with the simulation of intelligent behaviour by computers.
Expert system — An AI system that uses a knowledge base and rules to mimic the decision-making of a human expert.
Knowledge base — A store of facts about a specialist area.
Rule base — A set of rules used by the inference engine to reason with the facts.
Inference engine — The part of an expert system that applies rules to facts to reach conclusions.
Machine learning — A type of AI in which a program can automatically adapt its own processes and data, improving its performance with experience.
Core concepts
Automated systems
Automated systems combine sensors, a microprocessor and actuators:
- Sensors continually measure physical quantities and send readings to the microprocessor (via an analogue-to-digital converter if needed).
- The microprocessor compares the readings with stored, pre-set values.
- If action is needed, the microprocessor sends signals to actuators, which make a physical change.
- The process repeats continuously.
Examples of uses:
- Industry — controlling production lines, chemical processes and nuclear power stations; consistent and able to run continuously, but expensive to set up and potentially vulnerable to faults or cyber attacks.
- Transport — self-driving cars, autonomous trains and automatic parking systems; may reduce human error but raise safety and liability questions.
- Agriculture — automated irrigation and greenhouse control using moisture, light and temperature sensors; saves water and labour.
- Weather — automated weather stations log data continuously, even in remote places.
- Gaming — sensors detect movement to control games; AI controls non-player characters.
- Lighting — light and motion sensors switch lights on and off automatically, saving energy.
- Science — automated experiments and data logging measure precisely over long periods, in hazardous conditions if necessary.
General advantages: faster, more accurate and consistent than people; can operate continuously and in dangerous environments; reduce labour costs; can save energy and resources.
General disadvantages: expensive to set up and maintain; may fail if a sensor or component breaks; vulnerable to hacking; can reduce jobs; may not cope with unexpected situations.
Robotics
A robot has three characteristics:
- a mechanical structure or framework (physical body);
- electrical components — sensors, microprocessors and actuators — that allow it to sense and act;
- it is programmable.
Some robots are independent (they operate without human control, often with AI), while others are dependent (they need a human to control them, for example a remotely operated bomb-disposal robot).
Uses of robots: manufacturing (welding, painting, assembling cars), warehouses (picking and moving goods), medicine (assisting surgeons with precise operations), domestic tasks (robot vacuum cleaners and lawnmowers), agriculture (harvesting and weeding), exploration (space rovers, deep-sea and disaster zones), and entertainment.
Advantages: can work continuously without breaks; consistent quality and precision; can work in hazardous environments; reduce costs over time.
Disadvantages: expensive to buy, programme and maintain; can only do what they are programmed for and may struggle with unexpected situations; can replace human workers; may need specialist staff.
Artificial intelligence
AI is the simulation of intelligent behaviour by computers. Characteristics of AI include the ability to collect data and rules, the ability to reason, and in some systems the ability to learn and adapt.
Expert systems contain:
- a knowledge base of facts about a specialist area;
- a rule base of if–then rules;
- an inference engine, which applies the rules to the facts and the user's answers to reach conclusions;
- a user interface, which asks questions and displays results and explanations.
Expert systems are used in medical diagnosis, fault diagnosis for cars and machinery, financial advice, mineral prospecting and chess. They give consistent answers, can explain their reasoning and capture the knowledge of experts, but they are only as good as the knowledge and rules they contain, cannot use common sense, and are expensive to build.
Machine learning programs improve their performance with experience: they analyse large amounts of data, identify patterns and adjust their own processes. Examples include recommendation systems on shopping and streaming sites, spam filters that learn from messages users mark as spam, image and speech recognition, and fraud detection in banking. Machine learning can find patterns humans would miss, but it needs large amounts of data, can reflect biases in that data, and its decisions can be difficult to explain.
Ethical issues
Using technology raises questions about what is right:
- Employment — automation and robots can replace jobs, particularly routine work, while creating new jobs in design, programming and maintenance. Workers may need retraining.
- Responsibility — if an autonomous car crashes or an AI makes a harmful decision, it may be unclear who is responsible: the manufacturer, the programmer or the user.
- Bias and fairness — AI trained on biased data can make unfair decisions about people.
- Privacy — automated systems, sensors and AI can collect large amounts of personal data, such as location, images and behaviour.
- Safety and security — automated systems can be hacked, with dangerous consequences in transport or industry.
- Environment — technology can reduce waste and energy use, but manufacturing devices uses resources and creates e-waste.
Legal issues and intellectual property
Intellectual property includes software, music, films, designs and writing. Copyright protects creators by making it illegal to copy or distribute their work without permission. Software piracy — copying software illegally — denies developers income. Methods to protect software include product keys, licence agreements, digital rights management (DRM) and online activation. Laws also protect personal data (data protection laws) and make hacking a criminal offence.
Privacy and the individual
Users should understand what data is collected about them, use privacy settings, and consider the risks of sharing personal information online. Organisations must store personal data securely and use it only for its stated purpose.
Evaluating a technology: a checklist
When asked to discuss whether a technology should be used, consider:
- Benefits — speed, accuracy, consistency, safety, cost savings over time, working continuously or in dangerous places.
- Drawbacks — set-up and maintenance cost, reliability if components fail, inability to cope with unexpected situations, vulnerability to hacking.
- People — jobs lost or created, retraining, how users and customers are affected, public trust.
- Ethics and law — responsibility for errors, privacy of data collected, bias in AI decisions.
- Environment — energy use and e-waste, or savings in energy and resources.
A strong answer covers several of these, applies them to the scenario in the question, and finishes with a reasoned judgement.
Worked examples
Example 1: Automated greenhouse
Describe how an automated system keeps a greenhouse at the right temperature. (4 marks)
Answer: A temperature sensor measures the temperature continuously (1). The reading is sent to the microprocessor, which compares it with a stored value (1). If the temperature is too high, the microprocessor sends a signal to an actuator to open a window or turn on a fan (1). The process repeats continuously to keep the temperature within range (1).
Example 2: Expert systems
Identify the components of an expert system and state the purpose of the inference engine.
Answer: Knowledge base, rule base, inference engine and user interface. The inference engine applies the rules in the rule base to the facts in the knowledge base and the user's inputs to reach a conclusion.
Example 3: Characteristics of a robot
State the three characteristics of a robot. (3 marks)
Answer: A mechanical structure (1); electrical components such as sensors and actuators (1); it is programmable (1).
Example 4: Discussion
Discuss the use of robots in car manufacturing.
Answer: Robots can work continuously with consistent precision, improving quality and productivity, and can carry out dangerous tasks such as welding and painting. However, they are expensive to buy and maintain, can only do the tasks they are programmed for, and replace some human jobs, though they create others in maintenance and programming. Overall they are well suited to repetitive, precise work on production lines.
Common mistakes and how to avoid them
- Saying the sensor makes the decision. The microprocessor compares and decides; the actuator acts.
- Leaving out "programmable" when describing a robot.
- Confusing expert systems and machine learning. Expert systems use fixed rules; machine learning adapts with experience.
- One-sided discussions. Give advantages and disadvantages.
- Vague ethical points. Name the issue: jobs, responsibility, bias, privacy.
Exam technique for "Ethics and Emerging Technologies"
- For automated systems, describe the full loop: sensor → microprocessor comparison → actuator → repeat.
- For "describe a use" questions, name a specific application and explain what the technology does in it.
- For discussions, use the scenario and finish with a conclusion.
Quick revision summary
- Automated systems: sensors → microprocessor (compares with stored values) → actuators, repeating continuously.
- Uses: industry, transport, agriculture, weather, gaming, lighting, science.
- Robots: mechanical structure, electrical components, programmable; independent or dependent.
- AI: expert systems (knowledge base, rule base, inference engine, interface) and machine learning (adapts with data).
- Ethical issues: jobs, responsibility, bias, privacy, safety, environment.
- Intellectual property and copyright; protecting software with product keys, licences and DRM.