AI Bias
When a system produces unfair results because its training data reflected an unfair world. It matters commercially as well as ethically: a biased model makes confidently wrong decisions about real people.
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When a system produces unfair results because its training data reflected an unfair world. It matters commercially as well as ethically: a biased model makes confidently wrong decisions about real people.
Being recognised as a reliable source on a subject rather than on a single keyword, usually by covering it thoroughly and consistently. It is what makes a small site beat a bigger one in a narrow field.
Another attribute-based markup standard for embedding machine-readable facts in a page. Less common on business websites than JSON-LD, and largely equivalent in what it can express.
A setting that controls how predictable an AI’s output is. Low temperature gives safe, repeatable answers; high temperature gives varied, more surprising ones — useful for ideas, risky for facts.
What somebody actually wants when they type a query: to learn, to compare, to buy, or to find a specific site. Matching it matters more than matching the words.
A plain-text way of writing formatted documents using symbols like # for a heading. AI systems read it easily because the structure is unambiguous, which is why some sites now offer it alongside HTML.
A system of simple connected units, loosely inspired by the brain, that learns patterns from examples rather than following rules somebody wrote. Nearly all modern AI is built on one.