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.
When a model, instead of answering from memory, asks to run a specific tool — look up a price, check stock — and uses the result. It is what turns a chat assistant into something that can act.
AI that produces something new — text, images, code, audio — rather than only classifying or predicting. The generative part is why it can write an answer about your business instead of just linking to it.
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.
When an assistant names your website as the source of something it said, usually with a link. A citation is the AI-era equivalent of a first-page ranking — it is the thing worth competing for.
Visitors who arrive from an AI assistant rather than a search engine. Usually small in number and unusually well-qualified, because they clicked after reading a recommendation.
Machine learning using neural networks with many layers. The depth is what lets a system learn complicated things — recognising a face, translating a sentence — without anyone specifying how.