Definition
A learning system improves through a loop. Data goes into a model, the model makes predictions, a loss measures how wrong they are, and that feedback adjusts the model. A model is just a function with adjustable numbers, called parameters.
In simple words
Learning cricket: you bat (prediction), see where the ball went (result), the coach says what was wrong (feedback), and you adjust your stance (parameters). Do this ten thousand times.
Why it matters
This one loop sits behind nearly every AI system, from a spam filter to ChatGPT. Learn it once, and every later algorithm is a variation on it.
How it works
- Data: examples with right answers.
- Model: makes a prediction with its current parameters.
- Loss: measures the error.
- Update: changes the parameters to reduce the error.
- Repeat, thousands or millions of times.
Try it yourself
Run the learning cycle
A music app learns what you like. Follow it around the loop, and notice that it never really ends.
Run the learning cycle step by step
Put the steps of the AI learning cycle in order, starting with the data.
- 1Add the new example and its correct answer to the data
- 2Train the model on the examples
- 3Compare the prediction with what really happened (feedback)
- 4Train again, so the model improves
- 5Collect labelled examples (data)
- 6The model makes a prediction for a new case
Common mistakes
- Thinking the model is told the rule. It only ever gets feedback.
- Blaming the algorithm when the real problem is bad data.
Check yourself
Summary
- Data, model, loss, update, repeat.
- A model is a function with adjustable numbers.
- Feedback drives all the learning.
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