Phil 9.26.18

7:00 – 5:00 ASRC MKT

  • The Publisher’s Patron: How Google’s News Initiative Is Re-Defining Journalism
    • Facebook, Twitter, Amazon and Google – many tech companies are involved in journalism. A major force, however, is Google’s News Initiative. But where does Google’s money go? We can reveal that the typical recipient of Google funding is a commercial legacy institution in Western Europe. Meanwhile, non-profit news organisations and public-service media rarely receive funding. The only question is: what is Google trying to achieve with its sponsorship?
  • Introduction to Machine Learning for Coders: Launch
    • Today we’re launching our newest (and biggest!) course, Introduction to Machine Learning for Coders. The course, recorded at the University of San Francisco as part of the Masters of Science in Data Science curriculum, covers the most important practical foundations for modern machine learning. There are 12 lessons, each of which is around two hours long—a list of all the lessons along with a screenshot from each is at the end of this post.
    • There are some excellent machine learning courses already, most notably the wonderful Coursera course from Andrew Ng. But that course is showing its age now, particularly since it uses Matlab for coursework. This new course uses modern tools and libraries, including python, pandas, scikit-learn, and pytorch. Unlike many educational materials in the field, our approach is “code first” rather than “math first”. It’s well suited to people who are writing code every day, but perhaps aren’t practicing their math chops quite as often (although we do cover all the necessary theory when appropriate). Perhaps most importantly, this course is very opinionated—rather than being a complete survey of every type of model, we focus on those that really matter in practice.
  • Been thinking about libraries being a marker for production code, and it strikes me that GitHub could be a set of “conversations”. There are markers for popularity (pulls), and markers for quality (pushes). We know how many people are contributing (plus followers and following), and there are tags. There is also a marketplace now.  There is also an API. My sense is that it should be possible to build maps of:
    • Language relationships and use (X Y Z + color?)
    • Relationships within languages?
    • Cross-linked projects across languages
    • NLP analysis of README
  • There are also other types of measures of consensus like dependency graphs (who’s actually using) and releases (more info here)
  • More iConf paper – it’s the right length. Now tweaking
  • Put Zach on JuryRoom until he is moved to A2P?
  • Reading A Sociology of Algorithms: High-Frequency Trading and the Shaping of Markets
  • Reading Antonio’s ACM Paper. Surprising alignment with our work