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Jul 26

Alphabet’s autonomous driving and robotaxi company Waymo teamed up with fellow Alphabet company and AI specialist DeepMind to develop new training methods that would help makes its training better and more efficient. TechCrunch reports: The two worked together to bring a training method called Population Based Training (PBT for short) to bear on Waymo’s challenge of building better virtual drivers, and the results were impressive — DeepMind says in a blog post that using PBT decreased by 24% false positives in a network that identifies and places boxes around pedestrians, bicyclists and motorcyclists spotted by a Waymo vehicle’s many sensors. Not only that, but is also resulted in savings in terms of both training time and resources, using about 50% of both compared to standard methods that Waymo was using previously.

What DeepMind and Waymo did with this experiment was essentially automate killing the “bad” training and replacing them with better-performing spin-offs of the best-in-class networks running the task. That’s where evolution comes in, since it’s kind of a process of artificial natural selection. Yes, that does make sense — read it again. In order to avoid potential pitfalls with this method, DeepMind tweaked some aspects after early research, including evaluating models on fast, 15-minute intervals, building out strong validation criteria and example sets to ensure that tests really were building better-performing neural nets for the real world, and not just good pattern-recognition engines for the specific data they’d been fed. Finally, the companies also developed a sort of “island population” approach by building sub-populations of neural nets that only competed with one another in limited groups, similar to how animal populations cut off from larger groups (i.e. limited to islands) develop far different and sometimes better-adapted characteristics versus their large land-mass cousins.

Read more of this story at Slashdot.

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