L002 · How AI thinks · 2 min
How machines learn
A machine learns by guessing, checking how wrong it was, and adjusting itself a tiny bit, billions of times.
01 · The idea
Imagine teaching a computer to guess house prices in Nairobi. You give it thousands of past sales: size, area, number of rooms, and the real price.
At first its guesses are random. After each guess it sees the real answer and nudges its internal settings so the next guess is a little closer. These settings are called parameters. Big models have billions of them.
Repeat this enough times and the guesses become good. The finished result is called a model. Using the model later to answer new questions is called inference.
One important catch: a model only knows what was in its training data. If the examples were unfair or out of date, the model will be too. This is where bias comes from.
02 · Think of it like this
It is like learning to throw a ball into a basket. Too far, so you throw softer. Too short, so a bit harder. You never learn a formula, but after many throws your arm just knows.
03 · Do it now
04 · Quick check
Words in this lesson
See also
L054Under the hood · 2 min
Overfitting: learning the answers, missing the lesson
A model overfits when it memorises its training examples so closely that it fails on anything new.
L132Staying safe · 2 min
AI and bias
AI learns from human data, so it can repeat human unfairness about gender, age, accents or regions unless people test for it.
L005How AI thinks · 2 min
Why AI makes things up
AI is built to produce a likely answer, not a true one, so sometimes it confidently invents facts.
L036How AI thinks · 2 min
The knowledge cutoff
A model only learned from text up to a certain date, so on its own it does not know what happened after that.