Waymo learning from Darwin for autonomous driving

Google subsidiary Waymo has been working alongside its AI cousin DeepMind to develop a technique called ‘Population Based Training’, based on Darwin’s concepts of evolution.

Although we plan on dumbing down the explanation here, we do also hope to remain true to the work Google’s autonomous driving subsidiary Waymo and AI unit DeepMind are doing to advance self-driving algorithms. It’s an incredibly complicated field, but it does seem like the duo is making progress.

“Training an individual neural net has traditionally required weeks of fine-tuning and experimentation, as well as enormous amounts of computational power,” a blog post stated. “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.”

The easy part of autonomous driving is almost finished. Sensors are almost up-to scratch and prices will come down quickly when economies of scale kicks in, while the chip giants are making progress also. The trickiest part of the equation is the ‘intelligence’ aspect, the AI components which control all of the decisions.

The simplest way to explain training algorithms is through trial and error. The algorithm performs a task, then grades its performance depending on the outcome. Depending on the ‘grades’ the algorithm will adjust how it performs the task to create a more likely positive outcome.

The challenge which engineers and data scientists face is how much freedom the algorithms are given to adjust with each trial. Too little variance and the fine-tuning takes too long, too much and the results vary wildly. Most of the time, engineers will monitor the tests, manually culling the poorest performing results.

The new approach from Waymo and DeepMind is an interesting one. Population Based Training starts with multiple different tests, before the poorest performing ones are culled from the population. Out of the ‘survivors’, copies are made with slightly mutated hyperparameters. This process goes on and on until the algorithms become more reliable, resilient and safe.

It might sound like a simple solution, but not many companies like Waymo are fortunate to have such smarts as DeepMind living in the same corporate family. Its almost unfair, and we’ve quite surprised its taken so long for Waymo to cosy up to its smarter cousin.