Waymo, DeepMind Collaborate to Improve Self-Driving Cars
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.
A Waymo self-driving vehicle moves through an intersection in Chandler Arizona, U.S., December 2, 2017. (Photo: Reuters)
Alphabet-owned Waymo has partnered with UK-based computer programmes company DeepMind on a 'Population-Based Training' method for pedestrian detection that has the best neural networks like life forms do in natural selection, thus, saving time and effort. According to the company, 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. "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," Yu-Hsin Chen, Senior Software Engineer at Waymo recently wrote in a blog post.
"To make this process more efficient, researchers at DeepMind devised a way to automatically determine good hyperparameter schedules based on evolutionary competition (called 'Population-Based Training' or PBT), which combines the advantages of hand-tuning and random search," Chen added. Like random search, PBT also starts with multiple networks initiated with random hyperparameters.
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