راهبرد/ جنبش ویکیمدیا/۲۰۱۷/دوره ۲/عصر افزوده
تا سال ۲۰۳۰، جنبش ویکیمدیا با استفاده از ماشینهای یادگیرنده به داوطلبانمان کمک میکند تا خلاقیت و بهرهوری بیشتری داشته باشند. ما با استفاده از توان پیشبینی و طراحی میتوانیم دسترسی به اطلاعات را آسانتر کرده و استفاده از آن را با رابطهایی نوین، انسان پسند و هوشمند سادهتر کنیم. داوطلبان با استفاده از مترجمهای ماشینی، کیفیت و کمیت محتوا را با سرعت و نیز مقیاس گستردهتر به زبانهای مختلف افزایش میدهند.
This theme was formed from the content generated by individual contributors and organized groups during cycle 1 discussions. Here are the sub-themes that support this theme. See the Cycle 1 Report, plus the supplementary spreadsheet and synthesis methodology of the 1800+ thematic statements.
- Adapting to technological context
- Expanding to other medias
- Quality content
- Accessibility of content
Insights from movement strategy conversations and research
Insights from the Wikimedia community (from this discussion)
Insights from partners and experts
- Summary of 20 expert interviews from India, Indonesia, Nigeria, Egypt, Brazil and Mexico (2017)
- Summaries of salons, meetings, and interviews with experts and partners
Insights from user (readers and contributors) research
- Generative research in Mexico, Nigeria, and India (2016)
- Summary of Indonesia research - Initial findings
Digital age / trends
- "The Digital Industrial Revolution," NPR / TED: http://www.npr.org/programs/ted-radio-hour/522858434/the-digital-industrial-revolution?showDate=2017-04-21
- "Introduction to Machine Learning," Introduction and Resources : https://sinxloud.com/kb/machine-learning-introduction/
- Vanity Fair: Elon Musk predicts it will take 4-5 years to develop “a meaningful partial-brain interface” that allows the brain to communicate directly with computers: http://www.vanityfair.com/news/2017/03/elon-musk-billion-dollar-crusade-to-stop-ai-space-x
- "How Machine Learning Works", The Economist (they learn from experience!): http://www.economist.com/blogs/economist-explains/2015/05/economist-explains-14
- "The Simple Economics of Machine Intelligence," Harvard Business Review: https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence
Wikimedia and machine learning
- ORES and recommendation systems, open, ethical, learning machines helping to fight vandals with 18,000 manually enabled users today: Objective Revision Evaluation Service
- Wikimedia: 90% reduction in hours spent reviewing RecentChanges for vandalism after ORES was enabled: https://docs.google.com/presentation/d/1-rmxp3GNrSmqfjLoMZYlnR55S8DKoSfG-PCHObjTNAg/edit#slide=id.g1c9c9bd2c0_1_8
These are the main questions we want you to consider and debate during this discussion. Please support your arguments with research when possible. We recognize you may not have time to answer all the questions; to help you choose where to focus, we have listed three types of questions below. The primary questions are the ones most important to answer during this discussion cycle.
- Primary questions
- What impact would we have on the world if we follow this theme?
- Note that if you already submitted key ideas that answer this question for this theme in the previous discussion, consider just adding a link to that source page versus rewriting the whole statement. (see spreadsheet). If you have something new to add to a comment you made previously, however, please do.
- How important is this theme relative to the other 4 themes? Why?
- Secondary question
- Focus requires tradeoffs. If we increase our effort in this area in the next 15 years, is there anything we’re doing today that we would need to stop doing?
- Expansion questions
- What else is important to add to this theme to make it stronger?
- Who else will be working in this area and how might we partner with them?
Remember, if you have thoughts about the strategy process or larger issues, please share those here, where they are being monitored daily!
If you have specific ideas for improving the software, please consider submitting them in Phabricator or the product's specific talkpage.