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People of AI

Machine Learning on the web

Mar 30, 2023
Join Jason Mayes, the public face of Web ML at Google, as he discusses making machine learning accessible on the web, exploring WebML advantages like data privacy and cost-effectiveness. They dive into creative applications of WebML, from remote physiotherapy to salsa dancing skeletons, emphasizing accessibility and user experience. The podcast also touches on curiosity, pushing boundaries, and the TV series Devs' exploration of predictability in human behavior.
29:17

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Podcast summary created with Snipd AI

Quick takeaways

  • Executing ML models in web browsers enhances user privacy by avoiding cloud data transfers.
  • WebML's lowered latency and cost-effectiveness are beneficial for startups embracing TensorFlow.js for efficient operations.

Deep dives

Jason Mays Introduces WebML and Its Growth

Jason Mays is a face of WebML at Google, enhancing machine learning in JavaScript. WebML's rapid growth, from 11,000 to 170,000 weekly users, shows its expanding impact. WebML allows executing ML models within the JavaScript environment, focusing on privacy benefits by avoiding cloud data transmission. Cost-effectiveness and reduced latency are major advantages, especially for startups embracing TensorFlow.js for efficient, real-time ML processes.

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