The most impressive magic trick AI's learned in the modern era is the one where it conjures people out of thin air. And there's no better machine learning-powered wizardry than Nvidia's.
Nvidia is a company most lauded for its impressive graphics cards. But in the world of machine learning, it's one of the most ingenious companies using deep learning today. A couple of years back TNW reported on a new generative adversarial network (GAN) the company developed. At the time, it was an amazing example of how powerful deep learning had become.
This was cutting edge technology barely a year ago. Today, you can use it on your phone. Just point your web browser to "thispersondoesnotexist.com" and voila : the next time your grandmother asks when you're going to settle down with someone nice, you can conjure up a picture to show them.  A GAN is a neural network that works by splitting at AI's workload into separate parts. One set of algorithms (a generator) tries to create something – in this case a human face – while another set (a judge) tries to determine if it is a real image or a fake one. If the judge figures out it's fake, we have two more weeks of AI winter.
That's not true. Just making sure you were still with me. Actually, if the judge figures out the image is a fake the generator starts over. Once the judge is fooled, AI developer checks the results and determines if the algorithms need tweaking. Nvidia did not invent the GAN – the GANfather, Google's Ian Goodfellow, did that.
Nvidia's recent effort – you can read the paper here – is not just the same old network with a fancy web interface. Its layers have been upgraded, tweaked, and given to increase their self esteem. According to the research team:
Motivated by style transfer literature, we re-design the generator architecture in a way that exposes novel ways to control the image synthesis process.
What does it mean: you can press refresh all you want and it's going to keep spitting out the eerily convincing faces of people who do not exist. Damn that's creepy.
We'll take a deep dive into the research to learn more about the future of fake people, once we've had a chance to hit it with our mouths agape a few thousand more times.