Explainability
How well a system can show why it reached a decision. It matters most where the decision affects somebody — a loan, a diagnosis, a job application — and “the model said so” is not an answer.
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How well a system can show why it reached a decision. It matters most where the decision affects somebody — a loan, a diagnosis, a job application — and “the model said so” is not an answer.
An older way of marking up facts, written as attributes on the HTML itself rather than in a separate block. Still understood, but JSON-LD is now the safer choice.
Turning written text into spoken audio. Modern voices are convincing enough to be used for real narration, which raises its own questions about disclosure.
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.
Training a model on unlabelled data and letting it find structure by itself — grouping similar customers, say, without being told what the groups are.
The kind of AI behind ChatGPT, Claude and Gemini. It is trained on an enormous amount of text and predicts what should come next, which is enough to answer questions, summarise, translate and write.
The text, images or code a model learned from. It shapes everything the model knows and every bias it carries, and it is why what your website said two years ago may still be what an assistant repeats.