Four short links: 23 August 2019

Open Source Economics, Program Synthesis, YouTube Influence, and ChatBot Papers

By Nat Torkington
August 23, 2019
Four Short Links
  1. The Economics of Open Source (CJ Silverio) — I’m going to tell you a story about who owns the Javascript language commons, how we got into the situation that the language commons is *by* someone, and why we need to change it.
  2. State of the Art in Program Synthesis — conference, with talks to be posted afterwards, run by a YC startup. Program Synthesis is one of the most exciting fields in software today, in my humble opinion: Programs that write programs are the happiest programs in the world, in the words of Andrew Hume. It’ll give coders superpowers, or make us redundant, but either way it’s interesting.
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  4. Alternative Influence (Data and Society) — amazing report. Extremely well-written, it lays out how the alt right uses YouTube. These strategies reveal a tension underlying the content produced by these influencers: while they present themselves as news sources, their content strategies often more accurately consist of marketing and advertising approaches. These approaches are meant to provoke feelings, memories, emotions, and social ties. In this way, the “accuracy” of their messaging can be difficult to assess through traditional journalistic tactics like fact-checking. Specifically, they recount ideological testimonials that frame ideology in terms of personal growth and self-betterment. They engage in self-branding techniques that present traditional, white, male-dominated values as desirable and aspirational. They employ search engine optimization (SEO) to highly rank their content against politically charged keywords. And they strategically use controversy to gain attention and frame political ideas as fun entertainment.
  5. Chatbot and Related Research Paper Notes with ImagesPapers related to chatbot models in chronological order spanning about five years from 2014. Some papers are not about chatbots, but I included them because they are interesting, and they may provide insights into creating new and different conversation models. For each paper I provided a link, the names of the authors, and GitHub implementations of the paper (noting the deep learning framework) if I happened to find any. Since I tried to make these notes as concise as possible they are in no way summarizing the papers but are merely a starting point to get a hang of what the paper is about, and to mention main concepts with the help of pictures.
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