AI in Bed

How machines learn

Guess, check, adjust, repeat. Every model on Earth learns with the same simple loop.

⏱ 5 min read

Imagine learning to throw a paper ball into a bin across the room. You throw, you miss to the left, you aim a little more right. Throw again. Closer. Adjust again. Machine learning is exactly that, done millions of times very fast.

🎯 Predictmakes a guess📏 Measureloss = how wrong🔧 Adjustnudge weights📚 Next batchmore examplesrepeatmillions of times
The training loop: the model guesses, we measure how wrong it was, and nudge it to be a bit less wrong next time.

1️⃣ The model makes a guess

A model is just a big mathematical function with lots of adjustable knobs called parameters (or weights).

At the start the knobs are set randomly, so its guesses are rubbish.

2️⃣ We measure the mistake

We compare the guess with the right answer. A loss function turns "how wrong" into a single number.

Big number = very wrong. Small number = nearly right.

3️⃣ We nudge the knobs

An algorithm works out which direction to turn each knob to make the loss smaller, and turns it a tiny bit.

This is called optimisation. The most famous method is gradient descent (you'll meet it in Intermediate).

4️⃣ Repeat… a lot

One pass through all the training data is an epoch. Models often train for many epochs.

A big language model's knobs get nudged trillions of times during training.

🧩 Quick quiz

What does the loss function tell us?

🏋️ Training vs. using

Training is the slow, expensive learning phase, where the knobs change.

Inference is using the finished model. The knobs are frozen; it just answers. When you chat with an AI, you're doing inference.

🧩 Quick quiz

When you ask a chatbot a question, the model is…

✨ Before you drift off

  • A model is a function with adjustable parameters.
  • Training loop: predict → measure loss → adjust → repeat.
  • Training changes the parameters; inference just uses them.

📚 Go deeper (free & open)