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Google's NotebookLM and the Future of AI-Generated Audio

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Manage episode 445222621 series 3448051
Indhold leveret af Arize AI. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Arize AI eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

This week, Aman Khan and Harrison Chu explore NotebookLM’s unique features, including its ability to generate realistic-sounding podcast episodes from text (but this podcast is very real!). They dive into some technical underpinnings of the product, specifically the SoundStorm model used for generating high-quality audio, and how it leverages a hierarchical vector quantization approach (RVQ) to maintain consistency in speaker voice and tone throughout long audio durations.
The discussion also touches on ethical implications of such technology, particularly the potential for hallucinations and the need to balance creative freedom with factual accuracy. We close out with a few hot takes, and speculate on the future of AI-generated audio.

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

59 episoder

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iconDel
 
Manage episode 445222621 series 3448051
Indhold leveret af Arize AI. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Arize AI eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

This week, Aman Khan and Harrison Chu explore NotebookLM’s unique features, including its ability to generate realistic-sounding podcast episodes from text (but this podcast is very real!). They dive into some technical underpinnings of the product, specifically the SoundStorm model used for generating high-quality audio, and how it leverages a hierarchical vector quantization approach (RVQ) to maintain consistency in speaker voice and tone throughout long audio durations.
The discussion also touches on ethical implications of such technology, particularly the potential for hallucinations and the need to balance creative freedom with factual accuracy. We close out with a few hot takes, and speculate on the future of AI-generated audio.

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

59 episoder

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