The current big thing in machine-learning are neural networks, which are vaguely based on how neurons interact (but each node in a neural net is much more rudimentary than an actual neuron) and basically these get trained with data and adjustment algorithms that try to make their outputs look more like the known correct answer for the known data sets, and often they're further trained by having people look at the outputs and say whether it was right or not.

Like, imagine a dog: the dog can be taught to do certain things based on certain stimuli by rewarding it with food when it does what you want, and that training can be anything from teaching it tricks, to teaching it useful behavior like herding or guiding someone, to turning it into an erratic weapon that will maul someone at the slightest incitement. You control the teaching process, but the actual internal mechanisms and what's been learned are entirely inscrutable because they're all encoded deep into a bafflingly complex web of nodes that we barely understand beyond "they work because they work, and there's little electrical bits and some chemicals, it's all real fiddly but it mostly does what it should."

That's what modern AI research is, just teaching really stupid fake dogs that live in computers to do useful tricks, which can nonetheless be very impressive since 100% of the fake brain is dedicated to a specific task instead of having to worry about stuff like thermal regulation, breathing, balance, what smells are, etc.