This week is Copyright Week, and every day this week, the Electronic Frontier Foundation and its allies in the fight for a fair, balanced copyright will be posting new article explaining how to fix copyright. The week is organized around six principles: transparency, a robust public domain, open access, "you bought it, you own it," fair use and balance. They're asking for you to sign onto the principles and support the campaign. Here's the kick-off post:
It's no longer the case that copyright is only a concern if you run the kind of company that has its own theme parks. Instead, copyright policy can have an effect on any user posting to her favorite sites, sharing videos she's captured or photos she's taken. It can affect your basic freedom to tinker, make, and repair your stuff. And it gives content owners, and governments, a powerful censorship tool, with far too little oversight.
Copyright is supposed to embody a balanced incentive system, encourage authors and inventors to create new things by helping them receive some compensation for that investment. At the same time, copyright law puts limits on authors, such as fair use and limited terms of protection, to help make sure that IP rights don’t unfairly inhibit new creativity. When the system works, it can be an engine for creativity, innovation and consumer protection. When it doesn’t, IP rights have the opposite effect, giving IP owners a veto on innovation and free speech.
Copyright Week: Taking Copyright Back
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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