Statement:37: Difference between revisions
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|lastname=Sarabadani | |lastname=Sarabadani | ||
|tags=Machine Learning | |tags=Machine Learning | ||
|primarysession=Applying Machine Learning | |primarysession=Applying Machine Learning and Artificial Intelligence | ||
|statement=Machine learning and scaling the knowledge | |statement=Machine learning and scaling the knowledge | ||
There are lots of ways to contribute to Wikimedia movement and all are tedious and time-consuming, and sometimes frustrating. Let's use the classic example of fighting vandalism, Wikimedians are frustrated by flow of vandalism but at the same time they enjoy fighting it. By using scalable machine learning platform for Wikis we are moving towards giving more power to our users without making them tired of the work needed. To come back to our example, ORES is filtering out most good edits, leaving the rest to the community to take care of (so they enjoy the gratification of doing the job) at the same time handling the huge backlog of edits needing review. But if we use a bot to revert bad edits, it damages the motivation of the users. We need to continue moving at this direction in other areas like creating articles, improving quality, categorization, and so on. | There are lots of ways to contribute to Wikimedia movement and all are tedious and time-consuming, and sometimes frustrating. Let's use the classic example of fighting vandalism, Wikimedians are frustrated by flow of vandalism but at the same time they enjoy fighting it. By using scalable machine learning platform for Wikis we are moving towards giving more power to our users without making them tired of the work needed. To come back to our example, ORES is filtering out most good edits, leaving the rest to the community to take care of (so they enjoy the gratification of doing the job) at the same time handling the huge backlog of edits needing review. But if we use a bot to revert bad edits, it damages the motivation of the users. We need to continue moving at this direction in other areas like creating articles, improving quality, categorization, and so on. | ||
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Revision as of 09:24, 19 November 2017
| Tags | Machine Learning |
|---|---|
| Primary Session | Applying Machine Learning and Artificial Intelligence |
| Secondary Sessions |
Machine learning and scaling the knowledge
There are lots of ways to contribute to Wikimedia movement and all are tedious and time-consuming, and sometimes frustrating. Let's use the classic example of fighting vandalism, Wikimedians are frustrated by flow of vandalism but at the same time they enjoy fighting it. By using scalable machine learning platform for Wikis we are moving towards giving more power to our users without making them tired of the work needed. To come back to our example, ORES is filtering out most good edits, leaving the rest to the community to take care of (so they enjoy the gratification of doing the job) at the same time handling the huge backlog of edits needing review. But if we use a bot to revert bad edits, it damages the motivation of the users. We need to continue moving at this direction in other areas like creating articles, improving quality, categorization, and so on.
