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

In deep finding out and equipment discovering, having a substantial ample dataset is important to schooling a method and getting it to deliver results.

So what does a ML researcher do when there just isn’t adequate publicly available info?

Enter the MLCommons Association, a world engineering consortium with the aim of building ML greater for everybody.

MLCommons a short while ago announced the normal availability of the People’s Speech Dataset, a 30,000 hour English-language conversational speech dataset, and the Multilingual Spoken Words and phrases Corpus, an audio speech dataset with about 340,000 key terms in 50 languages, to support 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 technology engineer David Galvez, about the democratization of entry to speech know-how and how ML Commons is supporting advance the investigate and development of device understanding for everybody.

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