Translate to multiple languages

Subscribe to my Email updates

https://feedburner.google.com/fb/a/mailverify?uri=helgeScherlundelearning
If you enjoyed these post, make sure you subscribe to my Email Updates

Saturday, August 25, 2018

What algorithmic art can teach us about artificial intelligence | Artificial Intelligence - The Verge

Ceci n’est pas un algorithme, says James Vincent, cover machines with brains for The Verge, despite being a human without one.
White stood next to his prints (including the cello, in orange) at the Nature Morte gallery.
Photo: Ramesh Pathania
We live in a world that’s increasingly controlled by what might be called “the algorithmic gaze.” As we cede more decision-making power to machines in domains like health care, transportation, and security, the world as seen by computers becomes the dominant reality. If a facial recognition system doesn’t recognize the color of your skin, for example, it won’t acknowledge your existence. If a self-driving car can’t see you walk across the road, it’ll drive right through you. That’s the algorithmic gaze in action.

This sort of slow-burning structural change can be difficult to comprehend. But as is so often the case with societal shifts, artists are leaping headfirst into the epistemological fray. One of the best of these is Tom White, a lecturer in computational design at the University of Wellington in New Zealand whose art depicts the world, not as humans see it, but as algorithms do.

White started making this kind of artwork in late 2017 with a series of prints called “The Treachery of ImageNet.” The name combines the title of René Magritte’s famous painting of a pipe that isn’t a pipe, and ImageNet, a database of pictures that’s used across the industry to train and test machine vision algorithms. “It seemed like a natural parallel for me,” White tells The Verge. “Plus, I can’t resist a pun.”...

Kalyanaraman suggests that art made with AI demonstrates that computers may deserve credit as creative actors. The type of machine learning used by White and his peers works by sifting through large amounts of data and then replicating the patterns it finds. Kalyanaraman suggests that this is similar to the process by which humans learn art, but that our “mysticism” surrounding the notion of creativity stops us from seeing the parallels. “If a machine can make humanly surprising, stylistically new kinds of art, I think it is foolish to say well it’s not really creative because it doesn’t have consciousness,” he says.  

Source: The Verge