Google’s AI-building AI-based program, AutoML, has created its own AI offspring, which can outperform a human-built AI system in video recognition tasks. Using a technique called reinforcement learning, AutoML (human-built) would tell NASNet when it struck gold, in a fully automated process. It’s a big step forward for machine-learning optimization, but still a long way from Skynet. If this process can be expanded to other machine-learning systems, then Google will have solved one of the biggest problems with building these AI systems – the huge amount of human intervention needed to tweak them until they produce a satisfactory output. It’s a very complex process, and one that needs a lot of tinkering, but if the AutoML approach can be ported…