Generative adversarial networks could be most powerful algorithm in AI

A novel approach for pitting algorithms against each other, called generative adversarial networks, or GANs, is showing promise for improving AI accuracy and for automatically generating objects that typically require human creativity. Yann LeCun, director of AI research at Facebook and professor at New York University, wrote that GANs and the variations now being proposed are "the most interesting idea in the last 10 years in machine learning." The reason for the excitement is the technique promises to automate the process of training algorithms in AI applications. The use of GANs makes it possible to improve the internal representation in AI algorithms for detecting important data features in the face of noise and other variations. In the long run, GANs could also help improve the algorithms used for discriminating fake objects from the real thing and improve results for detecting objects in poor images or speech in noisy environments.