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Cambridge IGCSE Computer Science (0478/0984) · Topic 6 Automated and emerging technologies
Mini-Lesson

Automated and emerging technologies

3 missions. Guess, learn, then check. Your place is saved.

  1. Mission 1Nobody touches the greenhouse. So what turns the water on?
  2. Mission 2Robot vacuum or remote-control car: which one is the robot?
  3. Mission 3A spam filter blocks a scam it has never seen. How?
Mission 1

Nobody touches the greenhouse. So what turns the water on?

Make a guess: Nobody touches the greenhouse. So what turns the water on?

Automated & emerging technologies · automated systems

Sensor, microprocessor, actuator

Greenhouse control systemGreenhousereadings(data)signalMoisturesensorMicro-processorActuator(waterpump)
  • An automated system does a task with no human controlling it.
  • A sensor measures a physical quantity, such as moisture, temperature or light, and sends the reading as data.
  • A microprocessor processes that data and decides what to do.
  • An actuator receives a signal from the microprocessor and causes a physical action, such as a motor turning or a valve opening.
Automated & emerging technologies · automated systems

The control loop

Control loop flowchartYesNoSTARTRead moisture sensorMoisture <stored value?Send signal:pump ONSend signal:pump OFF
  • The sensor continually sends its readings to the microprocessor as digital data.
  • The microprocessor compares each reading with a stored (preset) value.
  • If the reading is out of range, it sends a signal to the actuator, for example to switch the pump on.
  • If it is in range, the actuator is switched off or left off.
  • The loop repeats continuously, so the system reacts as conditions change.
Quick check
?In an automated greenhouse, which component decides that the soil is too dry?
Automated & emerging technologies · automated systems

Automated systems in different scenarios

  • The same three parts appear in every scenario. Only the sensors and actuators change.
ScenarioExample systemSensor(s)Actuator / action
LightingStreet lightsLightSwitch lamps on at dusk
AgricultureGreenhouseMoisture, temperature, humidityPump, valves, heater, window motor
IndustryChemical plantLevel, pressure, temperatureValves, conveyor motors
TransportAutomatic train doorsProximity, pressureDoor motors
WeatherWeather stationTemperature, humidity, pressureReadings logged and sent automatically
GamingMotion controllerAccelerometerVibration motor (feedback)
ScienceLab experimentTemperature, pHHeater; readings logged at set intervals
Calculate
?The greenhouse microprocessor checks the soil once an hour for 6 hours. The readings (%) are stored in Moisture[1] to Moisture[6]: 52, 41, 38, 40, 35, 47. What is output?
Cambridge pseudocode
HoursOn ← 0
FOR Hour ← 1 TO 6
  IF Moisture[Hour] < 40
    THEN
      HoursOn ← HoursOn + 1
  ENDIF
NEXT Hour
OUTPUT HoursOn
Automated & emerging technologies · automated systems

Advantages and disadvantages

AdvantagesDisadvantages
Works 24 hours a day without breaksHigh set-up cost
Reacts faster and more consistently than a personNeeds maintenance; a faulty sensor can cause wrong actions
Keeps people away from dangerous placesCannot deal with situations it was not programmed for
Uses resources only when needed (less waste)Fewer jobs for manual workers; skilled technicians needed
Lower running costs over timeIf networked, it could be hacked
  • In an exam, link each point to the given scenario, not just a general list.
Quick check
?A town replaces timer-controlled street lights with lights controlled by a light sensor and a microprocessor. Which is a valid advantage?
Quick check
?An automated system must keep a fish tank at 25 °C. Which sensor and actuator should it use?
Mission check

?Nobody touches the greenhouse. So what turns the water on?
Mission 2

Robot vacuum or remote-control car: which one is the robot?

Make a guess: Robot vacuum or remote-control car: which one is the robot?

Automated & emerging technologies · robotics

What robotics is

  • Robotics is a branch of computer science that incorporates the design, construction and operation of robots.
  • Examples include factory equipment (robot arms), domestic robots (robot vacuums, lawn mowers) and drones.
  • A robot does not have to look like a person. It is a robot because of its characteristics, not its shape.
Automated & emerging technologies · robotics

Characteristics of a robot

Robot vacuum: three characteristicsFront bump sensorBrushesCliff sensors(underneath)MicroprocessorWheel motorsWheelsDirt sensorBrush motorBody shellSuction fan motorStored program: cleaningroute, scheduleMechanical structureElectrical components:sensorsmicroprocessoractuatorsProgrammable
CharacteristicWhat it meansRobot vacuum example
Mechanical structure or frameworkA physical body that can move or hold partsBody, wheels, brushes
Electrical componentsSensors, a microprocessor and actuatorsBump and dirt sensors; microprocessor; wheel and brush motors
ProgrammableIt follows a stored program, which can be changedCleaning routes and schedules set by its program
  • Sensors detect, the microprocessor decides, actuators move, just as in an automated system.
Quick check
?A factory robot arm has a camera that spots faulty parts. Which characteristic of a robot does the camera belong to?
Automated & emerging technologies · robotics

Roles robots perform

Roles robots performIndustryRobot armwelding a cardoorTransportDelivery droneAgricultureMilking robot ata cowMedicineRobot arm;surgeon incontrol at aconsoleDomesticsettingsRobot lawnmowerEntertainmentAnimatronicdinosaur
AreaExample roles
IndustryWelding and spray-painting car bodies; moving goods in warehouses
TransportDriverless vehicles; delivery drones
AgricultureMilking cows; picking fruit; drones spraying crops
MedicineAssisting surgeons with very precise movements; dispensing medicines
Domestic settingsVacuuming floors; mowing lawns
EntertainmentAnimatronic figures in theme parks; robot toys; camera drones filming events
Automated & emerging technologies · robotics

Advantages and disadvantages

AdvantagesDisadvantages
Work 24 hours a day without breaks or tirednessExpensive to buy, set up and maintain
Consistent, precise work, so fewer errorsOnly do the tasks they are programmed for
Can do dangerous, dirty or repetitive jobsStruggle with unexpected situations
Often faster than people; no wagesSome jobs lost; skilled engineers needed instead
  • Link each point to the role in the question (for example, precision matters most in medicine).
Quick check
?A supermarket uses robots to collect items for online orders in its warehouse. Which is a disadvantage of using the robots?
Quick check
?A car factory switches its robot arm from welding doors to welding bonnets without rebuilding it. Which characteristic makes this possible?
Mission check

?Robot vacuum or remote-control car: which one is the robot?
Mission 3

A spam filter blocks a scam it has never seen. How?

Make a guess: A spam filter blocks a scam it has never seen. How?

Automated & emerging technologies · artificial intelligence

What AI is

  • Artificial intelligence (AI) is a branch of computer science dealing with the simulation of intelligent behaviours by computers.
  • Its main characteristics:
    • a collection of data and the rules for using that data
    • the ability to reason: use the data and rules to reach new conclusions
    • it can include the ability to learn and adapt
Quick check
?A chess program meets a position it has never seen. Without changing its own program or data, it works out a strong move from the rules of chess and its stored data. Which characteristic of AI is this mainly showing?
Automated & emerging technologies · artificial intelligence

Expert systems

Expert system partswhen builtUserInterfaceInference engineKnowledgebasefactsRule baseIF–THEN rulesHumanexpert
  • An expert system gives advice or a diagnosis like a human expert in one area, such as medicine or car repair.
  • Knowledge base: the facts about the area, gathered from human experts.
  • Rule base: the IF–THEN rules for using those facts.
  • Inference engine: applies the rules to the facts and the user's answers to reach a conclusion.
  • Interface: asks the user questions and shows the conclusion.
Automated & emerging technologies · artificial intelligence

How an expert system works

  • The interface asks a question, such as "Is there a rash?", and the user answers.
  • The inference engine searches the knowledge base and rule base for rules matching the answers.
  • It may ask more questions.
  • The interface shows the conclusion or advice, often with how likely it is.
  • Example: MYCIN (Stanford, 1970s) used about 500 IF–THEN rules to diagnose blood infections and meningitis and suggest antibiotics.
Quick check
?Which part of an expert system matches the user's answers against the IF–THEN rules to reach a conclusion?
Automated & emerging technologies · artificial intelligence

Machine learning

Expert system vs machine learningExpert systemMachine learningRules written bypeopleFixed rulesOutputspamnot spamspammany labelled examplesProgramOutputnew dataFeedback adapts its ownprocesses and/or data
  • Machine learning is when a program has the ability to automatically adapt its own processes and/or data.
  • It is trained on many examples, such as emails marked "spam" or "not spam", and finds patterns.
  • As new data arrives it updates itself, so its results can improve without a programmer rewriting the code.
  • Compare: an expert system's rules are written by people and stay the same until someone edits them.
Your answer
?A car-fault expert system has this rule base:

R1: IF engine will not start AND lights are dim THEN "battery is flat" R2: IF engine will not start AND lights are bright THEN "check starter motor" R3: IF engine starts AND engine overheats THEN "check coolant level" Through the interface, the user answers: the engine will not start; the lights are bright. What conclusion does the inference engine reach?

Quick check
?Which of these systems shows machine learning?
Mission check

?A spam filter blocks a scam it has never seen. How?
Recap

Big ideas

  • Sensors measure and send data; the microprocessor compares the data with stored values and decides; actuators make the physical change.
  • The loop repeats continuously, so the system reacts without a person.
  • Advantages and disadvantages must be linked to the scenario given.
  • Robotics is the branch of computer science covering the design, construction and operation of robots.
  • A robot has a mechanical structure, electrical components (sensors, microprocessor, actuators) and is programmable.
  • Robots work in industry, transport, agriculture, medicine, homes and entertainment; judge their pros and cons for the role given.
  • AI simulates intelligent behaviour: data plus rules, the ability to reason, and sometimes the ability to learn and adapt.
  • An expert system has a knowledge base, a rule base, an inference engine and an interface; its rules are fixed by people.
  • Machine learning automatically adapts its own processes and/or data.
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  1. Mission 1Nobody touches the greenhouse. So what turns the water on?
  2. Mission 2Robot vacuum or remote-control car: which one is the robot?
  3. Mission 3A spam filter blocks a scam it has never seen. How?

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Specification points and sources for this lesson are listed at the bottom of the page.

Sources (7)

The specification points and sources behind this lesson.

  1. 1 Cambridge IGCSE 0478 syllabus 2026–2028 (v6), 6.1.1–6.1.2, p. 23
  2. 2 Cambridge IGCSE 0478 syllabus 2026–2028 (v6), 6.2.1–6.2.3, p. 24
  3. 3 Cambridge IGCSE 0478 syllabus 2026–2028 (v6), 6.3.1–6.3.3, p. 24
  4. 4 Same syllabus, 3.2.2–3.2.3 (actuator as output device; sensor list), p. 17
  5. 5 Same syllabus, section 4 pseudocode (FOR…NEXT, IF…THEN…ENDIF, OUTPUT, 1-based array)
  6. 6 Google Cloud blog, N. Kumaran, "Ridding Gmail of 100 million more spam messages with TensorFlow", 7 Feb 2019 (Japanese edition checked: https://cloud.google.com/blog/ja/products/gcp/ridding-gmail-of-100-million-more-spam-messages-with-tensorflow); also Dark Reading, 6 Feb 2019, https://www.darkreading.com/endpoint-security/google-tackles-gmail-spam-with-tensorflow
  7. 7 Buchanan & Shortliffe (1984), Rule-Based Expert Systems, ch. 4, https://people.dbmi.columbia.edu/shortliffe/Buchanan-Shortliffe-1984/Chapter-04.pdf (about 500 rules) and ch. 5, https://people.dbmi.columbia.edu/shortliffe/Buchanan-Shortliffe-1984/Chapter-05.pdf (bacteremia, meningitis, antimicrobial therapy); Stanford, early 1970s: Yale CPSC 458 notes, https://zoo.cs.yale.edu/classes/cs458/lectures/old2015/ExpertSystems.html