Google's adversarial AIs could lead to less reliance on real-world data

One of the biggest challenges facing the development of AI is that it requires a huge amount of human input, both in terms of the involvement of people when it comes to identifying and inputting data up front, and in terms of the nature of data sets required to even make training AI systems possible to begin with. Google AI research Ian Goodfellow, who recently headed back to Google Brain after a stint at the Elon Musk-backed OpenAI, hopes to address both those issues through an approach to AI that involves pitting one neural network against another. The concept isn't new: Facebook published a paper co-authored by its head of AI research Yann LeCunn and AI engineer Soumith Chintala last June, in which they describe using generative adversarial networks (GANs) to eventually enable unsupervised learning, aka machine learning that takes place without any human involvement. Goodfellow pioneered this idea, however, proving its basic viability after a heated (and boozy) debate with some University of Montreal academic colleagues, as Wired reports. In essence, the nature of the system includes two opposing neural networks that inform one another through their opposition: the first tries to create something synthetic, for instance a realistic image of a dog, and the other criticizes its attempts, trying to spot the fakes and pointing out where the first system has failed or fallen down.