Four short links: 21 Oct 2020
Antitrust, Differential Dataflow, Multilanguage Translation, and Time
- Justice Department Antitrust Filing Against Google — Claims advertising was an artificially limited market because of their exclusivity deals, including the Android anti-forking arrangements and Browser paid-default-search deals. New York Times has a write-up. This may be more important, historically, than the election results.
- Differential Dataflow — Differential dataflow programs look like many standard “big data” computations, borrowing idioms from frameworks like MapReduce and SQL. However, once you write and run your program, you can change the data inputs to the computation, and differential dataflow will promptly show you the corresponding changes in its output. Promptly meaning in as little as milliseconds. A book by Microsoft Research, with open source.
- https://github.com/pytorch/fairseq/tree/master/examples/m2m_100 — In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively with the best single systems of WMT. Open source from Facebook.
- Falsehoods Programmers Believe About Time — I love these ‘falsehoods’ articles. We model the world in our code, and we find it easy to forget how complicated the world is. (Especially with artificial constructs like calendars and clocks)
