There is a tendency among non-technical admirers of ML to regard deep learning methods as beyond their creators: independent entities that will one day, given refined enough algorithms and enough energy, out-comprehend their human creators and overwhelm humanity with their artificial consciousnesses. The term “neural networks” is itself a misnomer that doesn’t at all reflect the complexity of how human neurons represent and acquire information; it’s simply a term for nonlinear classification algorithms that began catching on once the computing power to run them emerged.

The question of whether or not deep neural networks are capable of “understanding” is largely a theoretical concern for the ML practitioner, who spends the bulk of his or her time undertaking the hard work of curating manually labeled data, fine-tuning his or her neural classifier with methods (or hacks) such as dropout, stochastic gradient descent, convolution and recursion, to increase its accuracy by a few fractions of a percentage point. Ten or twenty years from now, I imagine we’ll be dealing with a novel set of ML tools that will evolve with the rise of quantum computing (the term “machine learning” will probably be ancient history, too), but the essence of these methods will probably remain: to train a mathematical model to perform task X while generalizing its performance to the real world.

As fascinating and exciting as this era of artificial intelligence is, we should also remember that these algorithms are ultimately sophisticated classifiers that don't "understand" anything at all.