This week, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
… Continue readingThe 2019 Fairness, Accountability, and Transparency Conference
Charles Earl shares what he learned from this year's conference.
… Continue readingThis Week In Data Reading: MOOCs, Collusion in Artificial Intelligence, and Fake News
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
… Continue readingHow to Increase Retention and Revenue in 1,000 Nontrivial Steps
Yanir reflects on how data scientists at Automattic work to improve customer retention.
… Continue readingBuilding Thousands of Reproducible ML Models with pipe, the Automattic Machine Learning Pipeline
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
… Continue readingMissed Opportunities for Using Text in Data Visualization
Richard Brath talks to Automattic data visualization enthusiasts about the power of text in conveying the results of data science.
… Continue readingLinks Worth Sharing: What Makes People Successful
Boris shares a paper and a podcast all about the science of success.
… Continue reading