Computers can learn from examples — Computing, 4–6
Some programs are given many examples instead of one rule for every case. They find repeated clues, but their result can still be wrong and is not the same as a person’s understanding.
Learning a pattern from examples
A learning program receives examples with labels, such as pictures marked “cat” or “not cat”. It looks for clues that often appear together: ears, eyes, and a certain shape. Later, it uses those clues on a new picture. It is not thinking about cats as you do; it is matching a new case with patterns found in its examples.
Why use examples?
There are too many real pictures, voices, and objects for a person to write a separate instruction for each one. Examples let a program handle new cases that were not named beforehand. This solves a problem, but it does not make the program wise. The examples and labels guide what it notices, so their quality matters.
Three pictures of apples
You show a program three apple pictures: red and round, green and round, and yellow and round. It notices that roundness appears in all three, while colour changes. Then you show a new round orange picture. The program may call it an apple because its clues fit. If the new picture is a ball, the answer is wrong: the program found a useful clue, but not the whole meaning.
It does not know like you
A program that gives the right answer can seem as if it understands the object. That is a reasonable guess because people show understanding by answering too. But the program may only be following clues from its examples, and it can fail when the light, angle, or object changes. A confident-looking answer is not proof of understanding.
Where examples train programs
You meet this idea when a photo app groups similar faces, a voice tool guesses spoken words, or a toy recognises a few sounds. Such tools can be helpful, but you should remember that they work best on examples like the ones they received. If their examples are limited, their guesses may be limited too.
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