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

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

In deep learning and machine learning, having a large enough data set is key to training the system and making it deliver results.

So what does an ML researcher do when there isn’t enough publicly available data?

Join the MLCommons Association, a global engineering association that aims to make ML better for everyone.

MLCommons recently announced the general availability of the People Speech dataset, a 30,000 hour English conversational speech dataset, and the Multilingual Spoken Word Dataset, an audio dataset of more than 340,000 keywords in 50 languages, to aid the progress of ML research.

In this episode of NVIDIA’s AI Podcast, host Noah Kravitz talks with David Kanter, founder and CEO of MLCommons, and NVIDIA’s Senior AI Technology Architect Daniel Galvez, about democratizing access to speech technology and how MLCommons is helping advance research and advance learning. Automated for everyone.

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