https://blogs.scientificamerican.com/observations/what-do-machine-learning-and-hunter-gatherer-children-have-in-common/

When DeepMind’s AlphaGo program defeated its human competitor at the ancient board game Go, it made a big splash in the AI scene. AlphaGo was not trained through a set of prewritten instructions, but rather through practice and feedback. It turns out that there are striking similarities between new-generation machine-learning technologies and how children learn skills in the absence of formal education.

Let me explain. Hunter-gatherer communities in Congo, where I do my field research, do not often give direct instructions when teaching their children. Instead, they create a learning opportunity, like providing a tool, and monitor the child’s action without interfering. The child then adjusts her behavior according to the feedback she receives based on her performance. Likewise, neural networks work by giving an opportunity for the machine to learn (i.e., input) and providing feedback based on the output obtained by the network structure.