Carl Franzen's history of the SOPA/PIPA fight on Talking Points Memo is a fascinating account of the behind-the-scenes stuff that created the series of ever-larger protests that resulted in the bills' demise. Of particular note is his credit to Tiffiniy Cheng, who, along with Nicholas Reville, and Holmes Wilson, forms a trio of Boston-bred activists who are three of the most creative, passionate, skilled and engaged shit-disturbers I know. You may remember them as Downhill Battle, but they're also the folks behind Universal Subtitles, Miro, FreeBieber, and many other interesting and noteworthy campaigns and projects.
“There was sustained effort for the past three months,” said Tiffiniy Cheng, co-founder of Fight For the Future, an online advocacy non-profit that was founded in mid-2011 with a grant from the Media Democracy Fund, itself a fund-raising and distribution organization founded in 2006 “on the belief that freedom of expression and access to information are basic human rights.”
Fight for the Future played an early leading role in coordinating the various websites and groups opposed to SOPA and PIPA into a cohesive coalition.
That coalition, which ended up including upwards of 70 different companies and advocacy groups — From Tumblr to Demand Progress to Don’t Censor the Net — first took shape as a coalition in November 2011 under the banner “American Censorship,” just in time to rally opponents ahead of the House Judiciary Committee’s first hearing on SOPA.
How The Web Killed SOPA and PIPA
(via Michael Geist)
The Nightmare Machine is an MIT project to use machine learning image-processing to make imagery for Hallowe’en.
The Stormtrooper Decanter is on back-order, but you can pre-order one from the next batch for £22 — it’s based on Andrew Ainsworth’s original movie helmet moulds from 1976, and will provide endless opportunities to point to lowball glasses and say things like “aren’t you a little short for a Stormtrooper drink?” (via Bonnie Burton)
Yahoo has released a machine-learning model called open_nsfw that is designed to distinguish not-safe-for-work images from worksafe ones. By tweaking the model and combining it with places-CNN, MIT’s scene-recognition model, Gabriel Goh created a bunch of machine-generated scenes that score high for both models — things that aren’t porn, but look porny.
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