Created by Dries Depoorter in collaboration with Max Pinckers, Trophy Camera is a photo camera that can only make award winning pictures. Just take your photo and check if the camera sees your picture as award winning.
Latest in the series of critical design projects by Shanghai design and research studio Automato, TraiNNing Cards is a set of 5000 training images, physically printed and handpicked by humans to train any of your machines to recognise first and favorite item in a house: a dog.
Created by Sebastian Schmieg, ‘Decision Space’ explores how new datasets can enable new experiments in teaching computers how to understand images within a set of meaningful and complex categories.
Created by Bjørn Karmann at CIID, Objectifier empowers people to train objects in their daily environment to respond to their unique behaviours. Interacting with Objectifier is much like training a dog – you teach it only what you want it to care about. Just like a dog, it sees and understands its environment.
In the final week of the last year’s fall 10-week program at the School for Poetic Computation (SFPC), students presented their work in progress and its underly ideas in a public showcase. Here is a selection of projects that were presented.
At its best, creative inquiry offers intellectual nourishment, empowerment and solace. At the end of 2016, we need all of those, which is why remembering – and celebrating – the outstanding work done this year is all the more important. Over the past twelve months we’ve added more than 100 projects to our archive – and with your help we’ve selected the favourite ones!
Earlier this year SFPC in NYC was the host to alt-AI, a conference organised by Lauren Gardner and Gene Kogan to highlight and question artificial intelligence through the lens of artistic practice.
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Created by a Golan Levin, David Newbury, and Kyle McDonald, with the assistance of Golan’s students at CMU, Terrapattern is a visual search tool for satellite imagery that provides journalists, citizen scientists, and other researchers with the ability to quickly scan large geographical regions for specific visual features.
The following is a documentation of a new course ran by Gene Kogan on Machine Learning for Artists at ITP-NYU in spring 2016.