Quicksilver can also help editors keep existing Wikipedia articles up to date. An early version was tested in New York City this spring at an edit-a-thon aimed at improving entries on women scientists hosted by the American Museum of Natural History. Quicksilver provided facts it had scraped from the web, including links to the sources, on women scientists with sparse Wikipedia bios. Maria Strangas, the museum researcher who organized the event, says it helped the 25 first-time editors update the pages for roughly 70 women scientists in just two hours. “It magnified the effect that event had on Wikipedia,” Strangas says. Quicksilver is a spinoff from tools and data that Primer uses to serve clients including US intelligence agencies and large finance companies. The startup offers software that ingests internal or external data—think news feeds or internal documents—and generates graphics or written reports. Primer’s project began when Bohannon met Wade and others trying to improve the representation of women on Wikipedia at a conference last year, and began to wonder if algorithms could help. He later took advice from Wikimedia Foundation, the nonprofit that hosts Wikipedia. The first step was to collect 30,000 Wikipedia articles about scientists to train algorithms to detect the signals in news articles that correlate with a researcher having an entry on the site. Quicksilver uses that knowledge to find notable missing names by cross-referencing existing Wikipedia entries against a list of 200,000 scientific authors drawn from an academic search engine called Semantic Scholar. The software sources the facts needed to write missing entries from a collection of 500 million news articles and feeds them into a system trained to generate biographical entries from past examples. Quicksilver is far from the first attempt to have machines make Wikipedia’s ambitious aims more tractable. Bots already automatically fix typos or vandalism, for example. Wikimedia Foundation is investigating how to automatically fill out Wikipedia by drawing on articles that exist in one language but not in others. Primer is working to make Quicksilver multilingual too, initially expanding into Russian and Chinese, and to expand it to cover other subjects, such as politicians. But it doesn’t plan to ever let Quicksilver autonomously add to the site. “There are always humans in the loop,” says Sean Gourley, Primer’s CEO. “This project is about asking, How do you best use the finite number of engaged humans that you have?” Wikipedia’s notoriously punctilious community will likely keep a close eye on content generated with Quicksilver’s help. One question is whether this tool aimed at fixing blind spots has any blind spots of its own. Wade has already noticed that the tool’s suggestions seem skewed towards Americans, echoing a shortcoming of Wikipedia itself. “We need to be super careful that we’re not passing on whatever biases are in that machine-learning system,” says Wade. More Great WIRED Stories
San Francisco-based start-up Primer has used its machine learning algorithm Quicksilver to create 40,000 summaries for Wikipedia, aiming to rebalance its gender bias. Research has revealed only 18% of the biographies on the site are about women, while between 84% and 90% of contributors are men. Primer's John Bohannon said the algorithm scours existing news reports and other forms of data to compile summaries, seen as a jumping-off point for Wikipedia editors to then clean up or expand them. The company aims to make Quicksilver multilingual and expand to profile politicians.