DeepMind is helping Waymo evolve better self-driving AI algorithms | MIT Technology Review

For more details, see How evolutionary selection can train more capable self-driving cars | DeepMind blog

"Engineers at Waymo, owned by Alphabet, teamed up with researchers at DeepMind, another Alphabet division dedicated to AI, to find a more efficient process to train and fine-tune the company’s self-driving algorithms.

They used a technique called population-based training (PBT), previously developed by DeepMind for honing video-game algorithms. PBT, which takes inspiration from biological evolution, speeds up the selection of machine-learning algorithms and parameters for a particular task by having candidate code draw from the “fittest” specimens (the ones that perform a given task most efficiently) in an algorithmic population.

Refining AI algorithms in this way may also help give Waymo an edge. The algorithms that guide self-driving cars need to be retrained and recalibrated as the vehicles collect more data and are deployed in new locations. Dozens of companies are racing to demonstrate the best self-driving technology on real roads. Waymo is exploring various other ways of automating and accelerating the development of its machine-learning algorithms."

DeepMind is helping Waymo evolve better self-driving AI algorithms | MIT Technology Review