Illustrations by Me Suk Lee (aka daseotsi)
Thursday, June 18, 2015
When tasked with finding ways to publicize their small island nation of Dominica, Discover Dominica took to the internet with a filmmaker challenge. Geared towards forming new perspectives to share of the lush and volcanic island, thousands of requests from photographers, filmmakers and storytellers were sifted through. Ultimately, six filmmakers won the opportunity to travel to the island on an all-expense-paid trip. All six were tasked with exploring the nooks and crannies of the mainland, while creating six short films in the process. Featured above are photos from Sarah Lee, winner of the challenge.
War-Toys Brian McCarty
“The WAR-TOYS project seeks to explore war from the perspective of children living in its day-to-day reality. Because cognitive ability is often ahead of language development, children typically share their experiences and cope with associated feelings through indirect methods of communication, such as art and play. As a result, their personal accounts of war often go unseen, even when studying its affects. Through WAR-TOYS, I use a collaborative process to unlock and articulate children’s experiences, turning the language of play into serious dialog.”
Google sets up feedback loop in its image recognition neural network - which looks for patterns in pictures - creating hallucinatory images of animals, buildings and landscapes which veer from beautiful to terrifying
What do machines dream of? New images released by Google give us one potential answer: hypnotic landscapes of buildings, fountains and bridges merging into one.
The pictures, which veer from beautiful to terrifying, were created by the company’s image recognition neural network, which has been “taught” to identify features such as buildings, animals and objects in photographs.
They were created by feeding a picture into the network, asking it to recognise a feature of it, and modify the picture to emphasise the feature it recognises. That modified picture is then fed back into the network, which is again tasked to recognise features and emphasise them, and so on. Eventually, the feedback loop modifies the picture beyond all recognition.
Thanks Android Jones