My thoughts exactly

Where We See Shapes, AI Sees Textures

When you look at a photograph of a cat, chances are that you can recognize the pictured animal whether it’s ginger or striped — or whether the image is black and white, speckled, worn or faded. You can probably also spot the pet when it’s shown curled up behind a pillow or leaping onto a countertop in a blur of motion. You have naturally learned to identify a cat in almost any situation. In contrast, machine vision systems powered by deep neural networks can sometimes even outperform humans at recognizing a cat under fixed conditions, but images that are even a little novel, noisy or grainy can throw off those systems completely.
A research team in Germany has now discovered an unexpected reason why: While humans pay attention to the shapes of pictured objects, deep learning computer vision algorithms routinely latch on to the objects’ textures instead.
This finding, presented at the International Conference on Learning Representations in May, highlights the sharp contrast between how humans and machines “think,” and illustrates how misleading our intuitions can be about what makes artificial intelligences tick. It may also hint at why our own vision evolved the way it did.

We learned how to repeat muscle impulses to represent sequences, in exactly the same way we make Huffman trees in the sand box.  Looking for the complete sequence that uniquely identifies  a generator.  Thought is pretend motion, the eye movements are great at it, do not over rate the human brain, it ain't all that complex.

The bots do this, and the bots assume we do this. The result is that the bots will be keeping us busy, not idle. This method is the same way trade theory works, and Keynesian theory doesn't. In economics it needs a name, and that name is Baumol effect, the ability to reach equal scale ratios everywhere in the value chain.