Introduction to Probabilistic Programming — a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. Probabilistic methods are a way of automating inference, and of use as we try to make software smarter.
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The Macroeconomics of Superstars (PDF download) — We describe superstars as arising from digital innovations, which replace a fraction of the tasks in production with information technology that requires a fixed cost but can be reproduced at zero marginal cost. This generates a form of increasing returns to scale. To the extent that the digital innovations are excludable, it also provides the innovator with market power. Our paper studies the implications of superstar technologies for factor shares, for inequality, and for the efficiency properties of the superstar economy. (via Hacker News)
Inform: Past, Present, Future (Emily Short) — Graham Nelson’s talk about how Inform came to be what it is, and where it’s going. Inform is the amazing compiler that lets you write Infocom adventures…but is so much more than that. Anyone interested in programming language design, literate programming, or AR/VR interactive fiction should read this.