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.
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.
WebML's Privacy and Performance Benefits
Executing ML models in web browsers enhances user privacy by avoiding cloud data transfers, vital in healthcare and GDPR-related applications. Additionally, WebML's lowered latency, allowing high-frame-per-second processing, outperforms traditional server-side approaches. The cost-effectiveness of running models on user devices saves expenses, particularly beneficial for startups using TensorFlow.js for efficient and cost-saving operations.
WebML's Creative and Practical Applications
WebML enables creative and practical ML applications, exemplified by Include Health in remote physiotherapy provision. By using pose estimation models in browsers, inclusive health checks are achievable globally with medical-grade accuracy. WebML's browser-based approach facilitates scalability and accessibility, enabling impactful real-world implementations in healthcare and beyond.
Jason Mays' Journey and Future Outlook
Jason Mays' transition from aspiring Air Force pilot to WebML leader demonstrates the evolving landscape of technology. His mission to democratize ML through accessible tools like TensorFlow.js highlights advancements in making AI more user-friendly. Reflecting on the past challenges in tech, Jason envisions a future filled with creative ML applications, expanding the boundaries of innovation and accessibility.
Meet Jason Mayes, the public face of Web ML at Google and host of Made With TensorFlow.js. Join us as we talk about Jason’s journey and mission to make machine learning easy, fun and accessible on the web and how getting into the field of machine learning has never been easier.
Jason Mayes is the public face of Web ML at Google. He helps web engineers around the globe take their first steps with machine learning in JavaScript, pushing the boundaries of what's possible in web-based machine learning which has grown exponentially. He also combines his knowledge of the technical and creative worlds to develop innovative prototypes for Google's largest customers and internal teams with over 15 years experience working within web engineering and investigating emerging technologies.
#AI #ML #MadeWithTFJS #WebML
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