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Showing posts from December, 2016

what I said to you in 2016

I reviewed an old book about new China . I reviewed Use of Weapons , conceptual art, and Biblical literalism . I wrote a long poem about technical wonder . I poked about Civ V 's ideology . I riffed on scientific and non-scientific knowledge . I listed a range of new and/or rare words . I was impressed by nonfiction about nonreal things . I listed some free web services for developers . I gave an algorithm for working out why your boot drive doesn't work . I talked about songs about music critics and we listened to them . I reviewed British villainy, Accelerando , and an Estonian poet . I reviewed an excellent bedroom synthpop album . I listed more new and/or rare words with no clear theme . I reviewed books on social physics, ' The Hitch ', and Japanese poetry. I reviewed the gentlest introduction to Bayesianism . I got very maternal about the former Bishop of Edinburgh . I listed more new and/or rare words, on maths and wrestling . I applauded preppers with o

Checklist for toxic algorithms

Based on comments in O'Neil's Weapons of Math Destruction . Full review here. Opacity Is the subject aware they are being modelled? Is the subject aware of the model's outputs? Is the subject aware of the model's predictors and weights? Is the data the model uses open? Is it dynamic - does it update on its failed predictions? Scale Does the model make decisions about many thousands of people? Is the model famous enough to change incentives in its domain? Does the model cause vicious feedback loops? Does the model assign high-variance population estimates to individuals? Damage Does the model work against the subject's interests? If yes, does the model do so in the social interest? Is the model fully automated, i.e. does it make decisions as well as predictions? Does the model take into account things it shouldn't? Do its false positives do harm? Do its true positives? Is the harm of false positives symmetric with the good of true po

notable wordwordword

dragon-king (n.): An extreme event among extreme events: roughly, an outlier of a Pareto distribution, even. An elaboration on Taleb's black swan metaphor for unforeseeable extreme events. Not sure if it adds much, since the black swan is distribution-independent and Taleb doesn't fixate on power laws iirc. chef's arse (n.): Painful chafing of the buttocks against each other; attends exercise in hot environments. groufie ( n.): group selfie, obvs. No less contemptible for the awkward swerve around "groupie". detaliate (mangled v.): To explain. Seen in this Quora answer by a non-native English speaker (possibly Romanian ). I want to appropriate it: to detaliate is to respond to casual comments with a fisking . consing: (n.): To save on memory allocation by comparing new values to existing allocations and just storing a hash to the existing one if it's a hit. From Lisp's cons cells, a basic key-value data structure. sadcore: ( n.)