Definition
Is learning answers by heart the same as understanding?
Memorising means storing the exact examples seen before. Learning means finding the general pattern behind them, so that new examples can be handled. A model that only memorises does well on its training examples and badly on new ones; this problem is called overfitting.
In simple words
Two students prepare for an exam. One memorises the answers to last year's paper. The other understands the chapter. When the exam has new questions, the first student is stuck and the second does well. Good AI must be like the second student.
Why it matters
Overfitting is one of the most common reasons AI fails in the real world. It is why we always test on new examples, and why more varied data usually helps more than the same data repeated.
How it works
| Memorising model | Learning model | |
|---|---|---|
| Training examples | Nearly perfect | Very good |
| New examples | Poor | Good |
| What it stored | Every example exactly | The general pattern |
Try it yourself
The memoriser and the learner
The student studies the practice questions. Move the slider to change how they study, then give the exam.
Practice questions compared with a real exam
Discuss this with a friend or think it through on your own.
- Write five maths questions and their answers on cards.
- Partner A memorises the five answers. Partner B learns the method.
- Test both with the same five questions, then with five new ones.
- Compare their scores on old and new questions.
Think about it
- Which partner is more like a useful AI model?
- How could you tell, without new questions, that someone only memorised?
Learning or memorising?
Which does each behaviour show?
Gets every training example right but fails on new ones
Recognises a cat only in the exact photos it saw
Recognises a cat in a new photo taken at night
Does about as well on new examples as on old ones
Common mistakes
- Celebrating a perfect score on training examples.
- Thinking the fix is always more of the same data. Varied examples help more.
- Forgetting to keep test examples aside.
Check yourself
Summary
- Memorising stores examples; learning finds the pattern behind them.
- A memorising model overfits: great on training data, poor on new data.
- Testing on new examples reveals the difference.
Finished reading?