Project:Wikimedia Strategy 2017/Cycle 2/The Augmented Age

Theme: The Augmented Age

By 2030, the Wikimedia movement will collaborate with learning machines to help our volunteers be much more creative and productive. We will use prediction and design to make knowledge easy to access and easy to use with novel, humanized, intelligent interfaces. Volunteers will collaborate with machine translators to deepen the quality and quantity of content in more languages – at a heightened pace and scale. We will curate knowledge in structured and interactive formats that enhance and reflect the way people learn and contribute — beyond the browser, the app, and the encyclopedic format. We will embrace technological innovation as the most viable path toward meeting our vision.

Sub-themes

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. The source document of the 1800+ thematic statements will be released soon. Innovation Automation 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 first discussion)

• Coming soon

Insights from partners and experts

• Coming soon

Other Research

Digital age / trends

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

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

1. ORES and recommendation systems, open, ethical, learning machines helping to fight vandals with 18,000K manually enabled users today: m: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

These are the main questions we want you to consider and debate during this discussion. Please support your arguments with research when possible. What impact would we have on the world if we follow this theme? How important is this theme relative to the other 4 themes? Why? 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? 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? Answer these questions on the talk page

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