Combining the best of both worlds
by taking advantage of performant numerical computation capabilities and the reach of the web
- Low Latency
- In-browser inference enables novel use cases with local media sources.
- Privacy Preserving
- User data stays on-device and preserves user-privacy.
- High Availability
- No reliance on the network after initial asset caching for offline case.
- Low Cost
- Computing on client devices means no server farms needed.
- Take advantage of the native OS services for machine learning
- Get capabilities from the underlying hardware innovations
- Implement consistent, efficient, and reliable AI experiences on the web
- Benefit web applications and frameworks including TensorFlow.js, ONNX.js
Web Machine Learning Working Group Launch
quotes
Ping Yu
TLM for TensorFlow.js at Google
Having access to the native ML accelerators, machine learning frameworks such as
TensorFlow.js can greatly improve model execution efficiency and truly democratize ML for web
developers.
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Ningxin Hu
Principal Engineer, WPE at Intel
The early empirical results from the Web Neural Network API implementations demonstrate tremendous power
&
performance improvements of the Web AI workloads. Through access to the full native AI capabilities of
the
modern heterogeneous hardware, the Web Neural Network API enables a whole new transformative class of
intelligent user experiences on the Open Web Platform across a variety of hardware, software, and device
types.
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Chai Chaoweeraprasit
PE Lead, MLCP at Microsoft
The Web Neural Network API is a very important step toward the future of the Intelligent Web where AI is
infused into the user’s daily web experiences. With the current advances and the pace of innovations in
the
AI hardware landscape, it’ll help connect those experiences from the clouds and make them personal to
the
users through seamless native hardware performance on the edge devices across the entire web. That’s the
future worth dreaming about!
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