Strategy/Wikimedia movement/2017/Cycle 2/The Augmented Age

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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.

Sub-themes[edit]

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.

  • Innovation
  • Automation
  • Adapting to technological context
  • Expanding to other medias
  • Quality content
  • Accessibility of content

Insights from movement strategy conversations and research[edit]

Insights from the Wikimedia community (from this discussion)[edit]

Insights from partners and experts[edit]

Insights from user (readers and contributors) research[edit]

Other Research[edit]

Digital age / trends[edit]

  1. "The Digital Industrial Revolution," NPR / TED: http://www.npr.org/programs/ted-radio-hour/522858434/the-digital-industrial-revolution?showDate=2017-04-21
  2. 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

Machine learning[edit]

  1. "How Machine Learning Works", The Economist (they learn from experience!): http://www.economist.com/blogs/economist-explains/2015/05/economist-explains-14
  2. "The Simple Economics of Machine Intelligence," Harvard Business Review: https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence

Wikimedia and machine learning[edit]

  1. ORES and recommendation systems, open, ethical, learning machines helping to fight vandals with 18,000 manually enabled users today: Objective Revision Evaluation Service
  2. 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

Questions[edit]

Answer these questions on the talk page Answer these questions in a survey

These are the main questions we want you to consider and debate during this discussion. Please support your arguments with research when possible.

  1. What impact would we have on the world if we follow this theme?  
  2. How important is this theme relative to the other 4 themes? Why?
  3. 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?
  4. What else is important to add to this theme to make it stronger?
  5. Who else will be working in this area and how might we partner with them?

If you have specific ideas for improving the software, please consider submitting them in Phabricator or the product's specific talkpage.