Artificial Intelligence
Automated and Emerging Technologies · 4 question types
Exam Frequency Analysis
Past paper frequency (2018 to 2024)
This topic accounts for approximately 4% of your exam marks.
AI applications and machine learning concepts are growing in exam prominence.
Machine learning (ML) is a method used to build AI systems in which the machine learns from data rather than being told every rule explicitly.
The relationship between AI and machine learning matters in exam answers:
- AI is the broader goal: building machines that behave intelligently.
- Machine learning is one technique for achieving AI, where the machine improves with more data instead of being hand-programmed.
So is part of AI, not a separate thing. Be careful with this distinction; the syllabus penalises answers that treat them as completely independent.
How machine learning works
The process at the conceptual level the exam expects:
- The system is given training data: a large collection of examples, often labelled (e.g. thousands of photos tagged "cat" or "dog").
- An algorithm examines the data and looks for patterns or relationships that connect the inputs to the labels.
- The algorithm builds a that captures what it has found.
- The model can then be given new, unseen data and make a prediction about it.
- As more data is processed, the system continues to improve its accuracy.
Explaining machine learning in context
What comes up: "Explain how [a device] can make use of machine learning." (3 marks). The device changes each paper (robot vacuum, plough, car), but the credited structure is the same.
Write (three marks): (1) the device collects data as it operates (e.g. on its environment or the task); (2) it uses that data to adapt or update its own processes; (3) so that future performance improves (for example, it learns efficient routes or avoids problem areas).
Watch out: saying only "it learns" without explaining what it gathers data about and how that changes behaviour. Each mark needs a distinct point.
Common applications of machine learning:
- Image recognition (face unlock, photo tagging, medical scan analysis)
- Speech recognition (voice assistants, automatic captions)
- (translation, chatbots, summarisation)
- Recommendation systems (Netflix, Spotify, YouTube suggestions)
- Spam filters
- Fraud detection in banking
- Self-driving cars
Advantages of machine learning
| Advantage | Why it matters |
|---|---|
| Saves time and effort | Reduces the need for manual rule-writing, since the system learns the rules itself from data |
| Detects patterns humans miss | Can spot subtle correlations in huge datasets that no human could read through |
| Improves with more data | Performance continues to get better as more examples are seen, without needing to rewrite the program |
| Handles huge scale | Can process millions of inputs per second, far beyond human capability |
Disadvantages of machine learning
| Disadvantage | Why it matters |
|---|---|
| Needs very large amounts of high-quality data | Without enough data, or with biased or noisy data, the model performs poorly |
| Requires high processing power | Training a modern model can use vast computing resources and electricity |
| "Black box" decisions | It can be hard to explain why a learned model made a particular decision, which matters in medicine, law and finance |
| Bias from training data | If the training data reflects existing human biases, the model will reproduce them, sometimes in an amplified form |
| Vulnerable to adversarial inputs | Specially crafted inputs can trick a model into the wrong answer with high confidence |