Deep Learning
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.
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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.
Perplexity’s crawler. Perplexity cites its sources prominently, so being readable by it is one of the more direct routes to a visible citation.
An answer engine replies to a question with a written answer instead of a list of links. ChatGPT, Perplexity and Google’s AI Overviews are all answer engines. The old question was “do we rank?”; the new one is “are we the answer?”
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.
Taking a general model and training it further on your own examples so it follows your tone or handles your particular task. Useful when prompting alone keeps producing nearly-right answers.
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.
A working model of something real — a building, a supply chain — kept in step with it using live data, so changes can be tested before they are made for real.