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Title : Complexity is a Hierarchy of Abstractions
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Complexity is a Hierarchy of Abstractions

Self portrait by František Kupka

One of the technologies that is rapidly emerging is neural networks, but scientists are just beginning to learn how they work.

"Neural networks aim to mimic the human brain — and one way to think about the brain is that it works by accreting smaller abstractions into larger ones. Complexity of thought, in this view, is then measured by the range of smaller abstractions you can draw on, and the number of times you can combine lower-level abstractions into higher-level abstractions — like the way we learn to distinguish dogs from birds."

This abstraction architecture is useful for training a computer network to recognize objects, such as differentiating one animal from another, or for reading handwriting.

Self portrait by Chuck Close

But the basic concept of complexity as a hierarchy of abstractions can be taken in another way by artists. Often a realistic painting is made up of abstract strokes at a micro level. They might be the little abstract pixel tiles of Chuck Close or the blobby dots of John Berkey.

Another way of looking at this idea is to recognize that as our visual system begins to interpret an image, it does so first by perceiving broadly abstract shapes and colors. As the image is decoded in higher levels of the brain, those abstractions are assembled into an representation of a higher level impression of reality that takes into account such things as personality, cause-and-effect, story, and the relationships between objects.
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Quote from Quanta Essay "Foundations Built for a General Theory of Neural Networks"

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