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05 08 2019 01

Waymo’s self-driving vehicles employ neural networks to perform many driving tasks, from detecting objects and predicting how others will behave, to planning a car's next moves. Training an individual neural net has traditionally required weeks of fine-tuning and experimentation, as well as enormous amounts of computational power. Now, Waymo, in a research collaboration with DeepMind, has taken inspiration from Darwin’s insights into evolution to make this training more effective and efficient.

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. The higher the learning rate, the more dramatic the adjustments. The goal is to find a learning rate high enough that the network gets better after each iteration, but not so high that the network's performance fluctuates wildly.

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