Clarify backend-agnostic storage type readiness

Co-authored-by: Dominique Hazael-Massieux <[email protected]>

Author: anssiko

index.bs

@@ -22,7 +22,8 @@ Markup Shorthands: css no
 Logo: https://webmachinelearning.github.io/webmachinelearning-logo.png
 Deadline: 2023-10-01
 Status Text: <p>
-  Since the <a href="https://www.w3.org/TR/2023/CR-webnn-20230330/">initial Candidate Recommendation Snapshot</a> the Working Group has gathered further <a href="https://webmachinelearning.github.io/webnn-status/">implementation experience</a> and added new operations and data types needed for well-known <a href="https://github.com/webmachinelearning/webnn/issues/375">transformers to support generative AI use cases</a>. In addition, informed by this implementation experience, the group removed <code>MLCommandEncoder</code></a>, support for synchronous execution, and higher-level operations that can be expressed in terms of lower-level primitives in a performant manner. The group has also updated the specification to use modern authoring conventions to improve interoperability and precision of normative definitions and is developing a new feature, a <a href="https://github.com/webmachinelearning/webnn/issues/482">backend-agnostic storage type</a>, to improve performance and interoperability between the WebNN, WebGPU APIs and purpose-built hardware for ML.
+  Since the <a href="https://www.w3.org/TR/2023/CR-webnn-20230330/">initial Candidate Recommendation Snapshot</a> the Working Group has gathered further <a href="https://webmachinelearning.github.io/webnn-status/">implementation experience</a> and added new operations and data types needed for well-known <a href="https://github.com/webmachinelearning/webnn/issues/375">transformers to support generative AI use cases</a>. In addition, informed by this implementation experience, the group removed <code>MLCommandEncoder</code></a>, support for synchronous execution, and higher-level operations that can be expressed in terms of lower-level primitives in a performant manner. The group has also updated the specification to use modern authoring conventions to improve interoperability and precision of normative definitions.
+  The group is developing a new feature, a <a href="https://github.com/webmachinelearning/webnn/issues/482">backend-agnostic storage type</a>, to improve performance and interoperability between the WebNN, WebGPU APIs and purpose-built hardware for ML and expects to republish this document as a Candidate Recommendation Snapshot when ready for implementation.
 
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   updated at any time. Some parts of this document are work in progress and