Machine Learning
Building systems that learn patterns from data rather than rules.
Technical Term
Full explanation
Machine learning is a method of building AI systems where a model learns patterns from data, rather than following rules written explicitly by a person.
Compare it to a rules-based system: a rules-based spam filter is a list of if-this-then-that instructions a person wrote by hand. A machine-learning spam filter is shown thousands of examples of spam and legitimate mail and works out the distinguishing patterns itself, without a person specifying the rules in advance.
The large language models behind AI search are built this way, on enormous amounts of text. That is why there is no fixed formula to satisfy, the way there was with older, more rules-based ranking factors, a brand cannot reverse-engineer a checklist, because the model did not learn from one.
Why this matters
Because these systems learn from data rather than rules, there is no algorithm to reverse engineer. What a brand can influence is the evidence the systems learn and retrieve from.
Example
No one wrote a rule saying "if a brand's own site describes something one way and three independent sources describe it another way, favour the independent sources." A machine-learning model develops that kind of pattern from exposure to enormous amounts of text where independent corroboration has, in aggregate, correlated with accuracy.
Where this sits
Machine Learning belongs to AI & Search Infrastructure, one of the seven layers of the Aingworth Glossary. The technical vocabulary behind the interfaces. Useful for understanding how the systems work, without becoming the center of the conversation.
Continue with: AI Visibility Is a Range, Not a Number.
Related terms
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