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10 | 10 | >
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11 | 11 | >✅ **Goal:** Determine what are the unique opportunities of browser-based ML, what are the obstacles hindering adoption
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12 | 12 |
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13 |
| -- 👋 Introduction to the workshop |
14 |
| -- [💡 "Opportunities and Challenges" discussion topics](https://github.com/w3c/machine-learning-workshop/issues?q=is%3Aissue+is%3Aopen+label%3A%22Opportunities+and+Challenges%22+sort%3Acomments-desc) |
| 13 | +👋 Introduction to the workshop - @anssiko @dontcallmedom |
15 | 14 |
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| 15 | +[💡 "Opportunities and Challenges" discussion topics](https://github.com/w3c/machine-learning-workshop/issues?q=is%3Aissue+is%3Aopen+label%3A%22Opportunities+and+Challenges%22+sort%3Acomments-desc): |
| 16 | + |
| 17 | +ℹ️ WebGPU fitness for ML frameworks #66 - @jasonmayes @Kangz @grorg |
| 18 | +- ❓ Does WebGPU expose the right API surface to support ML frameworks interactions with GPUs? |
| 19 | +- ✔️ Proposal: New WebGPU extensions for subgroups, cooperative matrix multiply. |
| 20 | + |
| 21 | +ℹ️ Applicability to non-browser JS environments #62 - @jasonmayes @phoddie @huningxin @WenheLI |
| 22 | +- ❓ Pay attention to the applicability of the browser-targeted work to non-browser JS environments, in particular Node.js. |
| 23 | +- ✔️ Proposal: Extend W3C coordination to TC53 and non-browser projects. |
| 24 | + |
| 25 | +ℹ️ Protecting ML models #67 - @jasonmayes @tidoust @pyu10055 @jbingham |
| 26 | +- ❓ Some ML providers need to ensure their ML models cannot be extracted from a browser app. |
| 27 | +- ✔️ Proposal: Investigate existing access control mechanisms for video, learnings from 3D assets. |
| 28 | + |
| 29 | +ℹ️ Support for Float16 in JS & Wasm environments #64 - @cynthia @jasonmayes |
| 30 | +- ❓ Lack of support for float16 in JS and Wasm environments problematic for quantized models. |
| 31 | +- ✔️ Proposal: TBD |
| 32 | + |
| 33 | +ℹ️ In-browser training #82 and Training across devices #83 - @irealva @cynthia |
| 34 | +- ❓ The current in-browser efforts are focused on inference rather than training. |
| 35 | +- ✔️ Proposal: Understand successful real-world usages (e.g. Teachable Machine) and target transfer learning as the initial training use case for related browser API work. |
| 36 | + |
| 37 | +ℹ️ Permission model for Machine Learning APIs #72 - @cynthia @dontcallmedom @anssiko |
| 38 | +- ❓ How to design a forward-looking permission model for ML APIs? |
| 39 | +- ✔️ Proposal: TBD |
16 | 40 |
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17 | 41 | ## September 22, 2020, 2pm UTC [🗓️](https://www.timeanddate.com/worldclock/fixedtime.html?iso=20200922T14)
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18 | 42 |
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