The WebNN Polyfill has been published to NPM.

It is a JavaScript implementation of the WebNN API, based on TensorFlow.js that supports multiple backends for both Web browsers and Node.js.

With this polyfill, Web developers are able to experience the WebNN API early before the native implementations are shipped. Meanwhile, it can be treated as an independent implementation to help validate the feasibility and stability of the WebNN specification.

🌱 This W3C Community Group started incubating work in 2018 for a possible Web Neural Network API, in response to encouraging feedback from a TPAC breakout session. Starting October 2018, this Community Group identified key use cases working with diverse participants including major browser vendors, key ML JS frameworks, interested hardware vendors, web developers, and started drafting the Web Neural Network API specification.

🚀 Following the two-year incubation period in this Community Group, the W3C has launched the Web Machine Learning Working Group to standardize the Web Neural Network API, now graduating from its incubation stage. This Community Group continues its incubation function for new machine learning capabilities working in parallel with the newly formed Working Group, similarly to e.g. W3C’s WebAssembly and WebGPU efforts.

Introduction

Machine Learning (ML) is a branch of Artificial Intelligence. A subfield of ML called Deep Learning with its various neural network architectures enables new compelling user experiences for web applications. Use cases range from improved video conferencing to accessibility-improving features, with potential improved privacy over cloud-based solutions. Enabling these use cases and more is the focus of the newly launched Web Machine Learning Working Group.

Progress

While some of these use cases can be implemented in-device in a constrained manner with existing Web APIs (e.g. WebGL graphics API or in the future WebGPU), the lack of access to platform capabilities such as dedicated ML hardware accelerators and native instructions constraint the scope of experiences and leads to inefficient implementations on modern hardware.

Today W3C Advisory Committee Representatives received a Proposal to review a draft charter for the Web Machine Learning Working Group.

As part of ensuring that the community is aware of proposed work at W3C, this draft charter is public during the Advisory Committee review period.

W3C invites public comments through 03:59 UTC on 2021-03-27 (23:59, Eastern time on 2021-03-26) on the proposed charter. Please send comments to public-new-work@w3.org, which has a public archive lists.w3.org/Archives/Public/public-new-work.

The Machine Learning for the Web Community Group has been launched.

The mission of the Machine Learning for the Web Community Group (WebML CG) is to make Machine Learning a first-class web citizen by incubating and developing a dedicated low-level Web API for machine learning inference in the browser. Please see the charter for more information.

The group invites browser engine developers, hardware vendors, web application developers, and the broader web community with interest in Machine Learning to participate.