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Added ET headlines for this week
! colspan="2" | 2019 ET Headlines
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! colspan="2" | '''Latest''': [[#February 24thMarch 2nd, 2020|February 24thMarch 2nd, 2020]]|-| March| [[#March 2nd, 2020|2nd]]
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== March 2nd, 2020 ==
* '''Firefox Voice Public Beta''' -- Firefox Voice is an experiment from Mozilla that brings voice experiences to Firefox so you can browse and get more done with your voice—faster than ever. After some extensive internal testing we’re now in active, public beta and are looking for fearless early adopters who are willing to test the new add-on and give us feedback. You’ll find more information on our [https://community.mozilla.org/activities/test-firefox-voice/ community portal page] including a link to install Firefox Voice and become part of our beta program.
* '''Hubs for Enterprise''' -- Hubs Cloud has been [https://aws.amazon.com/marketplace/pp/B084WNGRRP soft launched] on the Amazon Marketplace! Hubs Cloud Enterprise 1.0 is a commercial product offering that allows companies and organizations to create their own private immersive collaborative spaces. Users can access those spaces on desktop, mobile phone, or VR headset. This allows third parties their biggest request in the collaborative VR space: self-hosting of the service.
* '''Servo Logo''' -- After [https://github.com/servo/servo/issues/3893#issuecomment-576283252 reviewing] with the Servo community, we’ve selected and [https://github.com/servo/servo/issues/3893#issuecomment-584864185 locked in] a new logo for Servo. You may not have been keeping up with Servo, but it’s a key component of our Firefox Reality VR browser and continues to get a lot of active attention from developers inside and outside of Mozilla.
== February 24th, 2020 ==
* '''Leveraging What You've Already Learned''' -- We landed a [https://github.com/mozilla/DeepSpeech/pull/2763 pull request] from one of our DeepSpeech community contributors that enables a capability called “Transfer Learning”, which allows us to transfer knowledge from one accent or language to another. So, for example, we could jumpstart the creation of a French language model from an existing English one, lowering the amount of data required to support new accents or languages. Transfer learning dynamically scales the learning rate while training to more easily produce high quality models.
