capable self-driving car
How evolutionary selection can train more capable self-driving cars
At a high level, neural nets learn through trial and error. A network is presented with a task, and is "graded" on whether it performs the task correctly or not. The network learns by continually attempting these tasks and adjusting itself based on its grades, such that it becomes more likely to perform correctly in the future. A network's performance depends heavily on its training regimen. For example, a researcher can tweak how much a network adjusts itself after each task–referred to as its learning rate.