http://www.mathworks.com/matlabcentral/fileexchange/40928-generate-gray-code-disk
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http://anthony.liekens.net/index.php/Misc/TrueBinaryTime
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http://vision.middlebury.edu/~schar/papers/structlight/p1.html
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http://www.jeffreythompson.org/blog/tag/gray-code/
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http://www.fachlexika.de/technik/mechatronik/sensor.html
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I am reminded of QR Squares and fractals. I’m sure there is a connection or association but it’s way beyond my pay grade. The graphic display of quantitative information and verification of the process that supports its conclusions in large scale, visually recognized chunks, almost points to the kind of neural processing we use in our typical hand/eye action/feedback/action loops that constitute our basic motor skills. By extension, I’m guessing, bigger chunks and faster processing can lead to evaluation of emotional cues and appropriate response that bring AI closer to real. I do not know what I’m talking about but analog is linear and digital is circular.
Sentence 1: right on. QR resemblance is just a coincidence. Fractal resemblance is significant because of hierarchical structure. Sentence 3: Yea. Let’s call it a Theory of Chunks. Sentence 4: Interesting to relate chuck size to emotion (fractal aesthetics), but I’m not sure if it has much to do with AI.
If a thing can read and process chunks faster; store, refer to and build on a foundation of various results of interaction and posit the optimal response to that exchange dynamic (for further analysis and trial), wouldn’t there be a sooner crossing of that threshold between mind/machine that AI is always looking to achieve?
If I’m seeing this correctly (and I doubt it) the fractal nature of the coding allows the processor to only have to return to a point in the structure of the decision’s logic rather than reinvent the wheel every time. This is exactly what developing infants do in the course of figuring out how one action leads to a pattern of response.