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.

AI models capable of reliably guiding driverless cars typically require endless testing and fine-tuning, not to mention computational power out the wazoo. In an effort to bolster AI algorithm training effectiveness and efficiency, Google parent company Alphabet's Waymo is collaborating with DeepMind on techniques inspired by evolutionary biology, the two companies revealed in a blog post this morning. As Waymo explains, AI algorithms self-improve through trial and error. A model is presented with a task that it learns to perform by continually attempting it and adjusting based on the feedback it receives. Performance is heavily dependent on the training regimen -- known as a hyperparemeter schedule -- and finding the best regimen is commonly left to experienced researchers and engineers. They handpick AI models undergoing training, culling the weakest performers and freeing resources to train new algorithms from scratch.