Erik Bernhardson
| Tags | Analytics, Collaboration, Machine Learning, Open Source, Privacy, Structured Data |
|---|---|
| Primary Session | Applying Machine Learning |
| Secondary Sessions |
Title: Empowering Editors with Machine Learning
Background: Advances in machine learning, powered by open source libraries, is becoming the foundational backbone of technology organizations the world over. Many tedious, time consuming, tasks that previously required 100% human involvement can now be augmented with human in the loop machine learning to empower editors to get more done with the limited time they have available to contribute to the sum of all human knowledge.
Advice: 1) Invest directly in applying known quantity machine learning, such as pre-trained ImageNet classifiers, to add structured data to our multimedia repositories to increase their discoverability. Perhaps via tools that provide editors with lists of appropriate items that they can easily click to add if appropriate to the multimedia.
2) Engage academia to work with Wikimedia data sets and employ developers to move the most promising results from research into production. There is already a significant amount of work being done in academia to test and evaluate machine learning with our data sets, but little to none of that work ever makes it back into Wikimedia sites. With more focus on collaboration we can encourage research that is specifically applicable to deployment goals.
3)Wikimedia has the ability to collect significant amounts of implicit user data via browsing sessions, searches, watchlists, editing histories, etc. that can be used for machine learning purposes. We need to be continuously thoughtful of the privacy implications of how we use this data.
