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As of April 2024, Perplexity has raised $165 million in funding, valuing the company at over $1 billion. [2] As of December 2024, Perplexity closed a $500 million round of funding that elevated its valuation to $9 billion. [13] [18] [19] In July 2024, Perplexity announced the launch of a new publishers' program to share ad revenue with partners ...
Of course, even Perplexity does hit a $1 billion valuation, it has a long way to go to truly challenge Google, which has enormous resources and AI talent at its disposal—and whose parent ...
The CEO of Perplexity AI shared some principles that guided him as a startup founder.. Aravind Srinivas talked about having "an extreme bias for action" in a recent talk at Stanford. He also said ...
The news company demanded details about how Perplexity had been accessing Times content and told the company to "immediately cease and desist all current and future unauthorized access."
For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. [56] For Transformer-based LLM, training cost is much higher than inference cost.
Yet, the perplexity is 2 −0.9 log 2 0.9 - 0.1 log 2 0.1 = 1.38. The inverse of the perplexity, 1/1.38 = 0.72, does not correspond to the 0.9 probability. The perplexity is the exponentiation of the entropy, a more straightforward quantity.
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.
Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.