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  2. Perplexity AI - Wikipedia

    en.wikipedia.org/wiki/Perplexity_AI

    Perplexity AI is a conversational search engine that uses large language models (LLMs) to answer queries using sources from the web and cites links within the text response. [ 3 ] [ 4 ] Its developer, Perplexity AI, Inc., is based in San Francisco, California .

  3. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.

  4. Perplexity - Wikipedia

    en.wikipedia.org/wiki/Perplexity

    Perplexity is sometimes used as a measure of the difficulty of a prediction problem. It is, however, generally not a straight forward representation of the relevant probability. For example, if you have two choices, one with probability 0.9, your chances of a correct guess using the optimal strategy are 90 percent.

  5. Perplexity AI’s challenge to Google hinges on something ...

    www.aol.com/finance/perplexity-ai-challenge...

    Perplexity’s CEO thinks he can take them on by being better. Google’s market cap is nearing $2 trillion. Perplexity AI’s challenge to Google hinges on something simple but tough—being the best

  6. What to know about Perplexity, the buzzy — and controversial ...

    www.aol.com/news/know-perplexity-buzzy-mdash...

    Perplexity did not immediately respond to a request for comment from Business Insider. Here's everything you need to know about the company's rise and the allegations it's facing. Big-name investors

  7. Wikipedia : Using neural network language models on Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Using_neural...

    Experienced editors may ask an LLM to improve the grammar, flow, or tone of pre-existing article text. Rather than taking the output and pasting it directly into Wikipedia, you must compare the LLM's suggestions with the original text, and thoroughly review each change for correctness, accuracy, and neutrality. Summarizing a reliable source.

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