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WalesComputer ScienceSyllabus dot point

How are computing techniques such as artificial intelligence and machine learning applied, and what are their capabilities and limits?

Describe applications of computer science including artificial intelligence, machine learning, automation and modern computing applications.

A focused answer to WJEC A-Level Computer Science Unit 4 applications, covering artificial intelligence and machine learning, neural networks, expert systems, automation, and modern computing applications with their capabilities and limits.

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Reviewed by: AI editorial process; not yet individually human-reviewed

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  1. What this dot point is asking
  2. The answer
  3. Examples in context
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What this dot point is asking

WJEC wants you to describe applications of computer science, including artificial intelligence, machine learning, automation and modern computing applications, with an understanding of their capabilities and limits. This is the applications strand of Unit 4, where the technical content meets real-world uses. Expect questions defining machine learning and contrasting it with traditional programming, and questions on the benefits and risks of automation, which reward balanced, specific answers.

The answer

Artificial intelligence

AI is an umbrella term: machine learning, neural networks and expert systems are all approaches within it, each suited to different kinds of problem.

Machine learning

The key contrast with traditional programming is that the rules are inferred from data, not written by hand, which makes machine learning powerful for problems too complex to specify with explicit rules.

Expert systems and automation

An expert system captures the knowledge of human experts as a set of rules in a knowledge base, with an inference engine that applies them to give advice (for example a medical-diagnosis aid). Automation uses computers and machines to carry out tasks previously done by people, from manufacturing robots to automated trading.

Capabilities and limits

Examples in context

Example 1. Spam filtering as machine learning
An email spam filter is trained on examples labelled spam or not spam, learning the patterns that distinguish them, and then classifies new mail. No programmer wrote a rule for every spam phrase; the model inferred them from data, which is exactly why machine learning suits messy, evolving problems that defy explicit rules.
Example 2. An expert system for diagnosis
A medical expert system encodes doctors' knowledge as rules and applies them to a patient's symptoms to suggest likely conditions. Unlike machine learning, its reasoning is explicit and can be explained rule by rule, illustrating the difference between rule-based AI and data-driven learning, and why each suits different needs.
Example 3. Automation on a production line
Robots welding car bodies work faster and more consistently than people and keep humans out of a hazardous environment, but they displace manual jobs and a fault can halt the whole line. This concrete case shows the balanced benefit-and-risk judgement examiners expect for automation, rather than uncritical enthusiasm.

Try this

Q1. State the main way machine learning differs from a traditionally programmed solution. [1 mark]

  • Cue. It learns patterns from training data rather than following rules written explicitly by a programmer.

Q2. State one limitation of a machine-learning system. [1 mark]

  • Cue. It depends on the quality of its training data (and can repeat bias in it), and its decisions can be opaque, so human oversight is needed.

Exam-style practice questions

Practice questions written in the style of WJEC exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.

WJEC 20204 marksExplain what is meant by machine learning, and explain how it differs from a traditionally programmed solution.
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Define machine learning, then contrast it with explicit programming.

Machine learning is an approach in which a system improves its performance on a task by learning patterns from data (training data) rather than following explicitly programmed rules. The system builds a model from examples and uses it to make predictions or decisions on new, unseen data.

In a traditionally programmed solution, a programmer writes explicit rules that specify exactly how to produce the output from the input. In machine learning, the rules are not written by hand; instead the system infers them from the training data, which is powerful for tasks too complex to specify by rules, such as recognising images.

Markers reward learning patterns from data to improve performance, and the contrast that traditional programming uses hand-written explicit rules while machine learning infers them from data.

WJEC 20224 marksGive one benefit and one risk of using automation to replace tasks previously done by people.
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Give a genuine benefit and a genuine risk, each briefly justified.

Benefit: automation can carry out repetitive or dangerous tasks faster, more consistently and without tiring, improving productivity, reducing human error, and removing people from hazardous work.

Risk: automation can displace workers from their jobs, causing unemployment and the need for retraining, and an over-reliance on automated systems can be dangerous if the system fails or behaves unexpectedly without human oversight.

Markers reward a valid benefit such as speed, consistency or safety, and a valid risk such as job displacement or over-reliance on a system that may fail.

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