Tune (experimental) Chrome Extension

Tune (experimental) chrome extension

Tune (experimental) for Chrome Download

Tune (experimental) chrome extension is Tune is a Chrome extension that helps people control the volume of the conversation they see..

Tune is a chrome extension that helps people control the volume of the conversation they see..
Tune is an experimental chrome extension from Jigsaw that lets people customize how much toxicity they want to see in comments across the internet. tune builds on the same machine learning models that power Perspective (https://www.perspectiveapi.com) to let people set the “volume” of conversations on a number of popular platforms, including YouTube, Facebook, Twitter, Reddit, and Disqus.

tune lets you turn the volume of toxic comments down for “zen mode” to skip comments completely, or turn it up to see everything—even the mean stuff. Or you can set the volume somewhere in between to customize the level of toxicity (e.g. attacks, insults, profanity, etc) you’re willing to see in comments.

The machine learning powering tune is experimental. It still misses some toxic comments and incorrectly hides some non-toxic comments. We’re constantly working to improve the underlying technology, and users can easily give feedback right in the tool to help us improve our algorithms. Tune isn’t meant to be a solution for direct targets of harassment (for whom seeing direct threats can be vital for their safety), nor is Tune a solution for all toxicity. Rather, it’s an experiment to show people how machine learning technology can create new ways to empower people as they read discussions online.

Tune is part of the Conversation-ai research project (https://conversationai.github.io/) and is completely open source, so you can learn more, explore the code, or contribute directly on github (https://github.com/conversationai/perspective-viewership-extension).

The extension currently works on english language comments only. All non-english comments will be shown above and hidden below a pre-defined threshold.

FAQs

Q: Are the comments I read being associated with my account or saved by Tune?

A: No. Comment text is sent to Perspective API for scoring then is automatically deleted after the score is returned. The initial sign-in for Tune is used to provide access to Perspective API.

Q: What data is stored when I submit my feedback or corrections through Tune?

A: If you submit feedback via clicking About>Feedback, the google feedback report will contain the user id of the account you are signed into with Chrome at that time. Separately, if you submit a correction to how a comment is scored (i.e. answering “Should this be hidden? Yes /No”) the text of the comment and your answer is stored for future training, but the submission is not linked to your user ID.

How to install Tune (experimental) chrome extension in chrome Browser

You can Follow the below Step By Step procedure to install the Tune (experimental) Chrome Extension to your Chrome Web browser.

  • Step 1: Go to the Chrome webstore https://chrome.google.com/webstore or download the extension Tune (experimental) Chrome Extension Download from https://pluginsaddonsextensions.com
  • Step 2: Now search for the Tune (experimental) in Chrome Webstore Search and click on the search button.
  • Step 3: click on the Tune (experimental) Chrome Extension Link
  • Step 4: in the next page click on the Add to Chrome button to Download and Install the Tune (experimental) extension for your Chrome Web browser .

Tune (experimental) Chrome extension Download

Looking for a method to Tune (experimental) Download for Chrome then this download link is for you.

It is the Tune (experimental) Chrome extension download link you can download and install Chrome Browser.

Download Tune (experimental) chrome extension (CRX)

Tags: Machine Learning Models , Lets People , Submit Feedback , Experimental Chrome Extension , Conversationai Research Project , Google Feedback Report , Tune Solution , People Control , Comments Completely , Machine Learning Technology , Machine Learning , Comments Read , English Language Comments ,

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