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  2. Perplexity.ai - Wikipedia

    en.wikipedia.org/wiki/Perplexity.ai

    Perplexity. Perplexity AI is an AI chatbot -powered research and conversational search engine that answers queries using natural language predictive text. [2] [3] Launched in 2022, Perplexity generates answers using sources from the web and cites links within the text response. [4] Perplexity works on a freemium model; the free product uses its ...

  3. Jeff Bezos’s investment in Perplexity AI has nearly ... - AOL

    www.aol.com/finance/jeff-bezos-investment...

    Founded in August 2022, Perplexity aims to challenge Google by offering an AI-based search engine that is “part chatbot and part search engine, offering real-time information and footnotes ...

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

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

    Perplexity AI is valued at $520 million. Google’s market cap is nearing $2 trillion. Perplexity’s CEO thinks he can take them on by being better.

  5. Search startup Perplexity AI valued at $520 million in ... - AOL

    www.aol.com/news/search-startup-perplexity-ai...

    (Reuters) -Search startup Perplexity AI has raised $73.6 million from a group of investors including Nvidia and Amazon founder Jeff Bezos, the latest example of investors hunting for AI startups ...

  6. t-distributed stochastic neighbor embedding - Wikipedia

    en.wikipedia.org/wiki/T-distributed_stochastic...

    t. e. t-distributed stochastic neighbor embedding ( t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, [1] where Laurens van der Maaten proposed the t ...

  7. Perplexity - Wikipedia

    en.wikipedia.org/wiki/Perplexity

    The perplexity is the exponentiation of the entropy, a more straightforward quantity. Entropy measures the expected or "average" number of bits required to encode the outcome of the random variable using an optimal variable-length code. It can also be regarded as the expected information gain from learning the outcome of the random variable ...

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