Key term
CCEA A-Level Digital Technology 2650
Systems Analysis and Development Approaches
Topic overviewThe software crisis and the need for systems developmentUser needs, time and costRoles in a systems development teamThe purpose of analysisFact-finding techniquesFunctional and non-functional requirementsData flow diagramsThe waterfall modelRapid application development and AgileResources, constraints and risks in a software projectProject management, Gantt charts and critical path analysisVersion management
Design, Development and Testing
Topic overviewThe purpose of systems designStoryboarding the user interfacePrototyping the interface and the data modelThe development stage: turning a design into working codeWhy testing runs throughout developmentLevels of testing: unit and integrationSystem testingAcceptance testingAlpha and beta testingThe purpose and components of a test planTypes of test data
Implementation and System Security
Topic overviewThe purpose of system implementationDirect and parallel changeoverPilot and phased changeoverUser documentationTechnical documentationData conversionThe purpose of system maintenanceCorrective, adaptive and perfective maintenanceArchiving dataBackup and recoveryFull, differential and incremental backupsDisaster recovery plans
Programming
Topic overviewThe purpose of a computer programThe integrated development environmentTranslation: compilers, interpreters and assemblersThe stages of compilation and linkingAlgorithms, pseudocode and syntaxVariables, constants and data typesSequence and selectionIteration: count-controlled and condition-controlled loopsFlowcharts and algorithm representationObjects, classes and methodsInheritance and evaluating the object-oriented approach
Data, Information and Data Representation
Topic overviewBits, bytes and units of storageHow many values a given number of bits can holdConverting decimal to binaryConverting binary to decimalTwo's complement and negative numbersCharacter sets: ASCII and UnicodeData, information and knowledgeFactors affecting the quality of informationValidation: range, type, length and presence checksValidation: format checks and lookup checksCheck digits and the Modulus 11 methodBatch totals, verification and the limits of checking
Computer Architecture and Processing Systems
Topic overviewThe processor, the clock and the registersThe fetch-execute cycleBuses, input and output controllers and portsMain memory: RAM, ROM and addressingCache memory and the memory hierarchyFactors that affect processing speedThe need for secondary storageMagnetic, optical and flash storage mediaChoosing secondary storage for common applicationsBatch, real-time and transaction processingAutomatic teller machines and point of sale terminalsSmartphones: input, output and processing
System Software, Application Software and the User Interface
Topic overviewUser interfaces: WIMP, command line and forms dialogueChoosing and evaluating a user interfaceInputting text, sound and videoThe purpose of an operating systemHow an operating system manages memory and processesTypes of operating systemUtility programsThe need for data compression, lossless and lossyZipping and archive filesCommon data file formatsApplication software: generic and special purposeObtaining and delivering software
Web Technology and Multimedia
Topic overviewThe internet, the World Wide Web and web standardsURLs, HTTP and the request-response cycleDomain names, DNS and IP addressesWeb browsers, search engines, cache and cookiesWeb servers, hosting, ISPs and file transferIntranets and how an organisation uses oneCreating web pages with HTMLHTML tags for text, lists, images and linksStyling pages with CSSMultimedia assets in a web pageClient-side and server-side processingSecurity over the internet: encryption, HTTPS and certificates
Networks
Topic overviewNetwork scope: LAN, MAN and WANNetwork hardware and connection devicesIP addresses and MAC addressesPeer-to-peer and server-based networksBus, star and ring topologiesThe need for communication protocolsThe OSI seven-layer modelTCP/IP, Ethernet and media access methodsWi-Fi, Bluetooth, VoIP and RFIDBandwidth and broadbandTransmission media: metal cable, fibre optic and wirelessDetecting and correcting transmission errors
Databases
Topic overviewThe relational database and its main featuresLogical and physical data modelsEntities, attributes and keysEntity-relationship modellingNormalisation to third normal formThe data dictionaryStructured Query Language: creating and changing dataStructured Query Language: retrieving dataQuery By Example and alternatives to SQLDatabase security, integrity and recovery
Artificial Intelligence, Expert Systems and Robotics
Topic overviewWhat artificial intelligence meansThe Turing test and machine intelligenceNeural network modellingThe structure of an expert systemThe knowledge base and the inference engineShells, heuristics, fuzzy logic and the knowledge engineerExpert systems in medicine, fault diagnosis and insuranceNatural language processingVoice recognition technologyEvaluating natural language and voice recognition systemsThe technology of roboticsRobotics in commercial situations
Mobile Technology, Data Mining and Cloud Computing
Topic overviewCells, masts and frequency reuseHandoff between cellsThe base station controller, the mobile switching centre and the PSTNWhat data mining isGathering and storing data for miningProcessing and analysing large volumes of dataBig data in health, finance and retailData mining and the privacy of the individualWhat cloud computing isVirtualisation, hosted instances, hosted solutions and clusteringCloud services: storage, email, software and backupEvaluating cloud computing for business
Individual, Social and Legal Considerations
Topic overviewThe Data Protection Act 2018 and the UK GDPRThe data protection principles and the rights of the data subjectThe Copyright, Designs and Patents Act 1988The Computer Misuse Act 1990Data protection duties of an organisationHow the legislation affects employees and the publicThe ethics of automated decision makingOnline censorshipMonitoring of personal behaviourThe ethics of artificial intelligence and of personal information
The Development Project: Analysis and Design
Topic overviewScheduling the project with a Gantt chartInternal and external constraints on the solutionDetailing user requirements as inputs, processes and outputsPrioritising requirements as essential and non-essentialData flow diagrams at level 0 and level 1Specifying hardware and software requirementsThe overall system designProcess descriptions and algorithmsUser interface design and validationData modelling and normalisation for the solutionDesigning the queries and reportsDeveloping a comprehensive test plan
The Development Project: Build, Testing and Evaluation
Topic overviewChoosing software to develop the solutionBuilding the solutionTest data and the types of testingCarrying out and recording the testingDocumenting a strategy for system implementationEvaluating the solution against the user requirementsIdentifying extensions to the systemThe electronic user guideBringing the system into use and supporting itDocumentation for the completed system
Artificial Intelligence, Expert Systems and Robotics
- 12 subtopics
- 36 things to know
- A2 Unit 1
What Artificial Intelligence, Expert Systems and Robotics covers
Artificial Intelligence, Expert Systems and Robotics is examined in Unit A2 1, Information Systems. It covers what artificial intelligence means, the Turing test and machine intelligence, neural network modelling, the structure of an expert system, the knowledge base and the inference engine, Shells, heuristics, fuzzy logic and the knowledge engineer, expert systems in medicine, fault diagnosis and insurance, natural language processing, voice recognition technology, Evaluating natural language and voice recognition systems, the technology of robotics and robotics in commercial situations.
The 12 subtopics
Each one has its own page.
- What artificial intelligence meansArtificial intelligence, term machine learning and what supervised learning means.Open →
- The Turing test and machine intelligenceTuring test and how a conversational program can appear intelligent while understanding nothing.Open →
- Neural network modellingNeural network and purpose of an activation function.Open →
- The structure of an expert systemThe knowledge base, the inference engine, the user interface and the explanation facility, what each contributes, and how a shell differs from a completed system.Open →
- The knowledge base and the inference engineHeld in the knowledge base of an expert system, production rule from a plant diagnosis system and inference engine.Open →
- Shells, heuristics, fuzzy logic and the knowledge engineerExpert system shell, heuristic used in fault diagnosis and main risk of reasoning with heuristics.Open →
- Expert systems in medicine, fault diagnosis and insuranceHow an expert system is used in medical diagnosis, why a medical expert system needs continual maintenance and one kind of fault a diagnostic expert system handles badly.Open →
- Natural language processingNatural language interface, main stages of natural language processing and lexicon in a language processing system.Open →
- Voice recognition technologyVoice recognition, term phoneme and acoustic model.Open →
- Evaluating natural language and voice recognition systemsHow voice interfaces improve accessibility and why recognition accuracy falls in a noisy environment.Open →
- The technology of roboticsMain parts of an industrial robot, end effector and degrees of freedom as applied to a robot arm.Open →
- Robotics in commercial situationsHow robots are used in vehicle manufacturing, how robots are used in a distribution warehouse and role of a surgical robot in an operating theatre.Open →
What you need to know
Answer each one before you reveal it.
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Key term
Distinguish narrow artificial intelligence from general artificial intelligence.
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Describe what a single node in a neural network does.
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Contrast fuzzy logic with Boolean logic.
Process
Describe the main stages of natural language processing.
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Describe the hardware a voice recognition system requires.
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Explain why accents and homophones cause recognition errors.
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Why does dictated text still have to be proofread?
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Give an overall judgement on where voice and natural language interfaces are best used.
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Name the main parts of an industrial robot.
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What does a machine vision system contribute to a robot cell?
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State two costs of introducing robots beyond the price of the robot itself.
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What is the Turing test?
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What is an expert system?
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What is held in the knowledge base of an expert system?
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Describe how an expert system is used in medical diagnosis.
Numbers
Who proposed the Turing test, and in what year?
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What is a neural network?
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Name the main components of an expert system.
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In what form is knowledge most commonly stored in an expert system?
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What is an expert system shell?
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Explain one benefit of a medical expert system to a clinic with no specialist on site.
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What is semantic analysis?
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Describe how robots are used in a distribution warehouse.
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What is meant by artificial intelligence?
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Explain the significance of the Turing test in defining artificial intelligence.
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Describe the three kinds of layer in a neural network.
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State the purpose of the user interface in an expert system.
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Give an example of a production rule from a plant diagnosis system.
Evaluation
Give two advantages of building an expert system from a shell.
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How does a medical expert system help with rare conditions?
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Why does a natural language system need to take account of context?
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What is voice recognition?
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Explain how voice interfaces improve accessibility.
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What is a robot?
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Give two agricultural uses of robotics.
Take Artificial Intelligence, Expert Systems and Robotics further
The same content, four other ways.
Commonly confused
Pairs that lose marks when they are blurred together.
Artificial intelligence vs Machine learning
Artificial intelligence: the whole field of building systems that behave as if they think. Machine learning: a single method inside it, where the rules come from examples instead of from a programmer.
Expert system shell vs Complete expert system
Expert system shell: software delivered empty, ready for rules to be added. Complete expert system: software that already holds the rules for a particular field.
Questions from this topic
Each one with the model answer underneath.
- 1
What is meant by artificial intelligence?
Model answer
Artificial intelligence is the branch of computing concerned with building systems that carry out tasks normally thought to need human intelligence. Reasoning, learning from experience, understanding language and recognising patterns are examples.
- 2
Name four capabilities a system is normally expected to show before it is called intelligent.
Model answer
A system called intelligent is normally expected to learn from data or experience, reason towards a conclusion, perceive its surroundings through some form of input, and adapt its behaviour when conditions change.
- 3
Distinguish narrow artificial intelligence from general artificial intelligence.
Model answer
Narrow artificial intelligence performs one specific task such as translating text or grading images, while general artificial intelligence would apply reasoning flexibly to any problem a person could tackle. Narrow systems are in everyday use and general intelligence has not been achieved.
- 4
Explain the main difference between a conventional program and an AI system in where the rules come from.
Model answer
A conventional program follows rules a programmer has written out in advance, whereas many AI systems derive their own rules by finding statistical patterns in large quantities of example data.
- 5
Define the term machine learning.
Model answer
Machine learning is an approach to artificial intelligence in which a system improves by being trained on examples. A programmer writes no explicit instructions for the task itself.
- 6
Explain what supervised learning means.
Model answer
Supervised learning trains a system on examples that already carry the correct answer as a label, adjusting the system until its output matches those labels so that it can then label cases it has not seen.
What is inside Levo
Six ways to work the same material.
Artificial Intelligence, Expert Systems and Robotics revision questions
- What does Artificial Intelligence, Expert Systems and Robotics cover in CCEA A-Level Digital Technology?
- 12 subtopics: what artificial intelligence means, the turing test and machine intelligence, neural network modelling, the structure of an expert system, the knowledge base and the inference engine, shells, heuristics, fuzzy logic and the knowledge engineer, expert systems in medicine, fault diagnosis and insurance, natural language processing, voice recognition technology, evaluating natural language and voice recognition systems, the technology of robotics and robotics in commercial situations. Each one has its own page with questions and model answers.
- How is Artificial Intelligence, Expert Systems and Robotics organised?
- By the CCEA 2650 specification, subtopic by subtopic, so it matches the order you are taught it in.
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