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  2. Perplexity.ai - Wikipedia

    en.wikipedia.org/wiki/Perplexity.ai

    Website. www .perplexity .ai. 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 the sources from the web and cites links within the text response. [4] Perplexity works on a freemium model; the ...

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

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

    Perplexity is “one of the few consumer AI products to reach this major milestone of 10 million MAUs,” said Jonathan Cohen, VP of applied research at Nvidia, in the January funding announcement ...

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

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

    San Francisco, California-based Perplexity AI, which raised $25.6 million in March last year in a round led by NEA, said it has served more than 500 million queries in 2023 while spending few ...

  5. Jeff Bezos–backed AI search startup’s CEO says ‘Google is ...

    www.aol.com/finance/jeff-bezos-backed-ai-search...

    On Thursday, San Francisco–based Perplexity announced that it has raised $73.6 million from a group of investors led by venture capital firm IVP and including Bezos, the $1.2 trillion AI chip ...

  6. Daniel Gross (entrepreneur) - Wikipedia

    en.wikipedia.org/wiki/Daniel_Gross_(entrepreneur)

    Cue (search engine), AI Grant, Andromeda. Daniel Gross is an American entrepreneur who co-founded Cue, led artificial intelligence efforts at Apple, served as a partner at Y-Combinator, [1] and is a notable technology investor in companies like Uber, Instacart, Figma, GitHub, Airtable, Rippling, CoreWeave, Character.ai, Perplexity.ai, and others.

  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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