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Teaching Computers to Laugh

1 points by denkern 4 hours ago · 0 comments · 2 min read


Teaching computers to laugh (Enlil vs Enki dilemma – Fire to the people)

This is the very concatenated brief on how to teach computers to laugh. Definitions: Amtsschimmel: Iteration exceeds task solving and produces tool self serving behaviour (see admin) Complex – iterative integrity is given. Complicated – iterative integrity is compromised by another ordering protocol. F-act: Clothes itself as a fact. I.e an OP_n^x that produces a datum = a Fixed Act. Ordering Protocol (OP_n^x) – the simple rule set, applied iteratively to a data set to solve a task. Its iteration builds complex tools. It develops order for evaluation but does not change the data set. Paradox: Two rule sets (truths) arriving at the same point. RALP-H: Recursive Algorithm for soLving Problems, including the use of Heuristics. Is used to maintain, correct and check for integral iteration of OP_n, i.e. complexity. Reality: Any given data set Truth: Only ever lives within a rule set. Two rule sets – two truths, Worklihood: Evaluative result produced from applying OP_n^x to Reality (= work) Alternative Worklihood: Created with a differing OP_n^x from the same reality. Dimension: Iterated Potential (^x)

Math: is a definition. It cannot prove anything. OP_math :: +1= (very simple, very powerful, not the only tool to produce a worklihood) OP_electricalcircuit :: power source, wire, switch, load OP_computer :: OP_electricalcircuit^(10^10^10).

The science of our time is data centric. All importance is given to the generated datum. What it is ignoring is the infrastructure that brings about this datum.

The infrastructure builds from the iterated (^x) application of OP_n, which extracts any given order (the f-act) from a data set. The build of the infrastructure is imbedded within the rule set OP_n and will produce a datum, i.e. the fact. Several OP_n can be applied to the same data set and will produce different facts.

Why do we, as humans, laugh? We laugh when we recognize that two (or more) different infrastructure paths have been used to arrive at a fact. The same thing can be seen in two or more different ways.

This handling also gives an exit from any pre-determined path with a logical outcome.

As this has been mapped, it can be programmed. Wherefore computers can learn to laugh.

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