This is Not a Person, But She is a Threat
GANs generate realistic fake people, and they’re raising big questions in media, the law, and AI
Take a look at the person at the top of this article. She looks friendly — perhaps she’s someone you’d connect with on Linkedin, or hire to run your social media. If you saw her at your kid’s gymnastics class, you’d say hello and make some awkward small talk. Depending on your persuasion, you might even swipe right on her Tinder profile if given the chance.
There’s only one problem — she doesn’t exist. The image above was generated using a novel Machine Learning technique called Generative Adversarial Networks (GANs). Invented in 2014, the technique has exploded in popularity and possibility. Turing award winner Yann LeCun described it as “the coolest idea in machine learning in the last 20 years”. It’s used in video gaming, astronomy, and art. And it’s taking the media and legal worlds by storm.
GANs Under the Hood
GANs works by taking two Deep Learning neural networks and pitting them against each other in a mini battle-royale. The first network is the generative network. It’s usually a Convolutional Neural Network and is trained on a set of sample images. Like all CNNs, it learns the attributes and patterns of the training…
