Shardcore writes, "I've built a new bot to troll/delight hipsters.
It algorithmically creates post-post-ironic t-shirt designs, posts them on twitter and tumblr and offers them for sale. No human is involved in the process at all."
The pleasure in these cognitively dissonant juxtapositions comes from our recognition that they come from the same class of things, but the referent is wrong. One of the most popular examples emerged immediately after the death of Lou Reed, featuring an image of Iggy Pop. It’s delightful because it allows us a moment of smugness as we recognise the ‘mistake’ being made. We wear this pun on a t-shirt as a form of social signaling – ‘Look at this joke I’ve recognised, do you recognise it as well?’ – it allows us to show a particular aspect of our taste to strangers, displaying an glimpse of our inner mental life to the world at large. For this to work, the juxtaposition has to be from the same domain; Lou Reed/Iggy Pop works, but Lou Reed/Katy Perry would not.
If we look closely, there are two distinct outputs to the system – the actual t-shirts (which remain in potentia until someone actually clicks ‘buy’), and the ‘shareable image’ which can be used as a signal of taste inside social networks. You don’t need to actually own the t-shirt to share the joke. The image fulfills much the same function in the ‘online’ world as the actual t-shirt does in the ‘offline’ world.
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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