Illustration redesigned by Devin Thorpe

Nvidia, a company known for designing graphic processing units, began making waves in AI towards the end of 2017 when they published the results for their new image generation technology. Visually, the results varied from photo-realism to bad Photoshop jobs.
Nvidia’s methodology is made up of, and arguably the next step in, what’s called a General Adversarial Network, or GAN. Instead of a single machine learning algorithm receiving data, evaluating it, and producing a result, GAN places two neural networks in a zero-sum competition with one another. Basically, one creates and the other discerns. The one that creates tries to trick that which discerns into thinking its creation is real. And because they both learn and improve, the results continue to get better. GAN works because, it turns out, competition is a powerful motivator not just for humans, but also for robots.
In just a few short years, we’ve gone from blurry black and white images (that seemed so stunning at the time) to Nvidia’s updated, GAN-driven test results that have produced digitally-rendered faces that are just about indistinguishable to the human eye.
