Make Your Own A.I. Powered Nightmare Fuel

Artificial intelligence can do some pretty surprising things. Today we present one of them: it’s called Fotogenerator, and it uses a next-level machine learning technique called “generative adversarial networks.” In its essence, the system uses a batch of images of actual faces as a reference. It interprets the drawing you supply and keeps tweaking what it produces until it thinks it could pass for a real human face.

Machine learning is probably the most common platform for artificial intelligence networks. The idea is that an AI can be taught to reach its own conclusions about the world through exposure to vast bodies of information. By seeing a few hundred thousand pictures of cars, for example, an AI can learn the basics of what makes a car by observing the characteristics shared by subjects of all the photographs. Then, when you show it a new picture that it has never seen before, it can compare the image to what it knows about cars to determine whether or not what it’s looking at is, in fact, a picture of a car.

The site is part of the pix2pix project, an artificial intelligence experiment written in Python (which means that yes, if you’re so inclined, you can download the code and play with it).  The results aren’t very beautiful – yet – but they are close enough to being photo-real to be deeply disturbing.

Here are a few of our doodles, and the results we got back. The more detailed the visual cues you give it, the better it does. The more abstract the face, the more horrifying the results.

Like so much of the internet, the pix2pix project started with cats. The same mechanics applied: a user drew an image, and the algorithm transformed it into a (relatively) more realistic-looking cat.

Obviously, the system will require more training to generate picture perfect images, but the transition from cats to human faces reveals an already considerable improvement. Eventually, generative networks could be used to create realistic-looking images or even videos from crude input. They could pave the way for computers that better understand the real world and how to contribute to it.

Tweet us your creations at @kryptonradio.  What can you make it do?

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SCIFI Radio Staff

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