Cycle 2 of the discussion is now closed. Please discuss the draft strategic direction (link coming soon).
By 2030, the Wikimedia movement will actively use technological innovations to help volunteers be much more creative and productive. We will use machine learning and design to make knowledge easy to access and easy to use. To greatly increase the quality and quantity of content in more languages, volunteers will, for example, have access to better machine translations. We will present and organize knowledge in ways that improve the way people learn and contribute — beyond the browser, the app, and the encyclopedia.
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.
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
- 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
- "機械学習入門編" による「導入と資源」: 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
- "機械学習の仕組み", The Economist (経験して覚えるんです！): http://www.economist.com/blogs/economist-explains/2015/05/economist-explains-14
- "人工知能の明白な経済性" Harvard Business Review: https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence
- 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.