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  2. Perplexity AI - Wikipedia

    en.wikipedia.org/wiki/Perplexity_AI

    Perplexity AI is an AI-powered research and conversational search engine that answers queries using natural language predictive text. It is based in San Francisco, California . Founded in 2022, Perplexity generates answers using sources from the web and cites links within the text response. [ 2 ]

  3. Perplexity AI to launch ads on search platform by fourth quarter

    www.aol.com/news/perplexity-ai-launch-ads-search...

    August 22, 2024 at 11:24 AM. (Reuters) - Nvidia and Jeff Bezos-backed Perplexity AI said on Thursday that it plans to introduce advertising on its artificial intelligence-powered search platform ...

  4. SoftBank to invest in search startup Perplexity AI at $3 ...

    www.aol.com/news/softbank-invest-search-startup...

    (Reuters) -Japanese technology investor SoftBank Group Corp's Vision Fund 2 is investing between $10 million and $20 million in U.S. search startup Perplexity AI at a valuation of $3 billion ...

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

    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 ...

  6. Daniel Gross (businessperson) - Wikipedia

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

    Daniel Gross. Daniel Gross is an American businessperson 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. [2][3][4]

  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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