How DeepMind and Waymo are Using Evolutionary Competition to Train Self-Driving Vehicles

Training deep neural networks regularly is a never-ending challenge in artificial intelligence(AI) projects. If training can be overwhelming for simple machine learning scenarios, can you imagine the efforts for systems such as self-driving vehicles that need to perform a large variety of tasks in real time in constantly-changing environments? Recently, Alphabet's subsidiaries Waymo and DeepMind partnered to find a more efficient process to train self-driving vehicles algorithms and their work took them back to one of the cornerstones of our history as species: evolution. Self-driving vehicles can be categorized as some of the most complex AI systems ever built. They need to operate safely in highly populated cities, they work in incomplete environments in which unknown factors appear real time, they need to perform a large number of intelligence tasks cohesively as a single system and the list of challenges never seems to end.