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MLCommons’ David Kanter, NVIDIA’s Daniel Galvez on Bettering AI with Publicly Accessible Datasets

In deep studying and machine studying, having a big sufficient dataset is essential to coaching a system and getting it to provide outcomes.

So what does a ML researcher do when there simply isn’t sufficient publicly accessible information?

Enter the MLCommons Affiliation, a worldwide engineering consortium with the purpose of creating ML higher for everybody.

MLCommons lately introduced the final availability of the Folks’s Speech Dataset, a 30,000 hour English-language conversational speech dataset, and the Multilingual Spoken Phrases Corpus, an audio speech dataset with over 340,000 key phrases in 50 languages, to assist advance ML analysis.

On this episode of NVIDIA’s AI Podcast, host Noah Kravitz spoke with David Kanter, founder and govt director of MLCommons, and NVIDIA senior AI developer know-how engineer Daniel Galvez, in regards to the democratization of entry to speech know-how and the way MLCommons helps advance the analysis and growth of machine studying for everybody.

The AI Podcast · MLCommons’ David Kanter, NVIDIA’s David Galvez on Publicly Accessible Datasets – Ep. 167

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The publish MLCommons’ David Kanter, NVIDIA’s Daniel Galvez on Bettering AI with Publicly Accessible Datasets appeared first on NVIDIA Weblog.

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