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August 21st, 2013, 10:30 PM   #1
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Theres more ways Bill Clinton can appear in vision than RSA

There are more ways Bill Clinton can appear in your vision than RSA keys

Of course RSA extends to any size key, but I mean commonly used sizes like a few powers of a googol possible integer values.

If P equals or is only damn close to NP, is a question of excessive abstraction and technicality to those who prefer to get things done.

How many megapixels is average Human vision?

For each pixel, counting red, green, and blue separately, and adding a few extra bits for how accurately we can see different brightnesses (HTML's 24 bit color codes more than do it for most people), Bill Clinton's face could be slightly bent a different way, light reflected on it differently, turned a unique way, standing beside an unexpected object, and the permutations of all possible ways we may see Bill Clinton and recognize him go on...

Its safe to say there are more than a googol ways Bill Clinton can appear on our visual neurons.

So how can there exist a "bill clinton neuron" in many people which increases its ease of activation (fire together wire together, short term memory, in the simplest case) and firing rate when we see nearly any of these exceeding-googol number of neural vectors? By neural vector, I mean 1 number for "ease of activation" of all your relevant neurons, at least the megapixel visuals and the bill clinton neuron.

How can we recognize the set of features which define Bill Clinton, and be surprised if any are missing, and answer that is true at this time when this certain neuron increases, if P is not practically equal to NP except maybe in the most convoluted cases?

If our neocortex had as many layers as steps in SHA256, would you trust it to secure-hash your data, or trust RSA to hide behind factoring integers of less number of bits than we have neocortex layers? This question is theoretical, since we have only 6 layers, but there are enough paths with cycles for us to practically recognize Bill Clinton.

Maybe there is no exact answer, but I'll be happy to have an approximation that fails less often than you look at Bill Clinton and don't recognize him, given that you can keep looking again from many angles until you do.

The question of exactness is most supported by the difficulty of reversing pointers like causes most of the crashes in the C programming language, where a googol just isn't enough because 1 random bit can crash the whole system. I see it as an open and very important question where the line between these optimizations and reversible computing with pointers actually is in practice. There are bigger things to think about than the economy and politics. We live in an infinite multiverse of all possible self-consistent (paradoxes are self-defeating) math structures, which looks like rotations of integers (wave/particle duality) since you can take any pattern which is not a rotation and tensor-product (all combinations) it with the definition of a circle (derived from sets of coin flips statistically, see pascal's triangle squared), therefore the set of all possible turing machines (in balanced form more like a quantum computer) appears as waves of integer frequency. We live in a big quantum computer, is a way to think about it, which works because in total it cancels out from all angles and doesn't exist so doesn't need to be created. This comes from the "existence proof" as I described above of recursive pattern matching scaling up to a "bill clinton neuron" in P instead of NP.

Doesn't it mean something to you that we can recognize a pattern of more than a googol number of possible ways it could occur, and that the pattern has a large number of parts?
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