Explainable AI: Viewing the world through the eyes of neural networks

One of the most intriguing artificial intelligence techniques was conceived when a few computer scientists where discussing deep learning and photorealistic images at a Montreal pub in 2014. Called generative adversarial networks (GAN), the concept has enabled the AI industry to take huge leaps toward creativity, generating images and sounds that are very close to their natural counterparts. However, like other AI techniques that use deep learning and neural networks, GANs are opaque, which means there's very little visibility or control on how they work. As a result, engineers find it hard to troubleshoot them, and users find it hard to trust them. To overcome these limitations, researchers at IBM and MIT have developed a technique called "GAN Dissection" that helps explore the inner workings of GANs and better understand the reasoning that results in their output.