Automated and Emerging Technologies · 4 question types
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:
So machine learning is part of AI, not a separate thing. Be careful with this distinction; the syllabus penalises answers that treat them as completely independent.
The process at the conceptual level the exam expects:
Common applications 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 |
| 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 |