How deep is the brain?

Recent AI advances in speech recognition, game-playing, image understanding, and language translation have all been based on a simple concept: multiply some numbers together, set some of them to zero, and then repeat. Since "multiplying and zeroing" doesn't inspire investors to start throwing money at you, these models are instead presented under the much loftier banner of "deep neural networks." Ever since the first versions of these networks were invented by Frank Rosenblatt in 1957, there has been controversy over how "neural" these models are. The New York Times proclaimed these first programs (which could accomplish tasks as astounding as distinguishing shapes on the left side versus shapes on the right side of a paper) to be "the first device to think as the human brain." Deep neural networks remained mostly a fringe idea for decades, since they typically didn't perform very well, due (in retrospect) to the limited computational power and small dataset sizes of the era.