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.
Automatically judging whether text is positive, negative or neutral. Used to read reviews and messages at a scale nobody could read by hand.
Training a model on examples that are already labelled with the right answer. It is the most common approach when you know exactly what you want the system to produce.
The model architecture behind almost every modern AI system, introduced in 2017. Its trick is paying attention to how every word in a passage relates to every other, which is what made fluent machine writing possible.
Searching by asking a full question, often as a follow-up to a previous one, the way you would ask a person. It favours pages that answer plainly over pages stuffed with keywords.
Structured data describing the business itself: name, logo, address, contact details, social profiles. The single most useful markup for being recognised, and the one most small sites are missing.