# Cohesive Approach in Learning

## Probabilistic models and probabilistic reasoning

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We say that the machine could combine data for conclusion, regardless of the outcome, it would sort out the least probable options. Husking out all the ‘improbabilities’ into data pool of redundancy it faces some challenges.

## What if that data is the most probable than the selected coefficient?

And what it comes to the Bayesian analysis and Markov models, If we stipulate the data on relevancy pertinent to a particular time and not based on the closest coefficient/index only.

But what is more important to understand, is that such trimming may cause delays and linear understanding of recurrent timing.

To be more technical, it means while operating with average index probability computation the AI would disregard its current time and current actuality/pertinence to the logic of query.

### The index is not probability, the imagining of it – is

And if we stem out our further research on in imaginary number probability, we would probably understand that the solution of any cohesive learning is = imagining the solution. \Therefore the logic requires pre-mediation before it even becomes a rule.

## Number theory and the probability of AI reasoning

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The number theory respectively coincides with the probability theories existent today. We have to understand that the probability theory isn’t just a theory of number collision but an algorithm we may use in heuristic and independent reasoning models such as in AI.

## Why probability is important?

The variance of options in heuristic and in any logical reasoning, firstly devised by Greeks as syllogism combines the first robotic reasoning of compilation of probability **A:** Either the meeting is at school or at home.**B:** The meeting is not at home.**/**Conclusion: Therefore the meeting is at school.

We type the subsets for the AI to process. We have to understand that the AI isn’t the categorical value machine that thinks in syllogisms, but rather an approachable ‘puzzler’ that rather presumes than calculates.

Check out the complex number probability in physics:

http://academic.reed.edu/chemistry/alan/Research/Bond/BasicQM/PrAmp/pramp.html

and the complex number probability in maths:

http://www.mathpages.com/home/kmath309/kmath309.htm

## Communicative approach in language solutions

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We deem appropriate to stem the methodology of language usage in its linguistic consistency, nevertheless the AI would operate reasonable well in terms of formulistic approach. Would that suffice to have an AI, working by the means of formal logic?

The formulistic approach in AI communications would rather be puzzling for a commoner to perceive nevertheless the first step in ‘humanizing’ AI would be formulistic.

Formulistic → Cohesive → Communicative.

Formal logical → Reasoning → Deflecting.

The final goal is to ‘pretend’ that formulistic approach is inevitable, it actually is at the moment, and the recycle the same code we’ve done before in more coherent way of ‘polishing’ Bayesian fields by providing an AI with reasoning. Communicative approach in AI systems is reasoning. Deflecting the AI ‘tongue’ making less ‘robotic’ and less formulistic, means at the same time less ‘reasonable’ and less ‘logical’, hence more humane than ever.

## The new era of mathematical methodology

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We stem out of a new 'carcass' we have previously established in the methodology of probabilistic reasoning, therefore we've failed to understand the captivating moments of true science – the reasoning and its artificial standout.

The substantiation of what was known as cybernetics is over. We proceed to the pure understanding of the purest of sciences – to the maths; we slope down to its bottoms of arithmetic.

## Arithmetical conditioning and the conditioning of algebra

We understand certain requirements of mathematical progression in Boolean terms of logic for computational AI, therefore we have to have predicaments for certain computational niche, in where the basis of methodology would rather stems itself out of Boolean number system.

### Today’s Computer language is dead

The basis of ternary systems (Bergman-Brusentsov) nevertheless give a depiction of binary-n number allocation, which in abstract repeats the computer language we’re having today. Though the computer language we’re having today is getting absolute, we have to predetermine the ‘fail’ of Boolean algebra and logic in cohesive adaptation ‘non-linear’, non-logical and stereotyped thinking

Binary → Ternary → ‘Multi-ternary’ → Universal

The universal language of maths in and the universal language of AI, we have to determine the true connection in any ways and when we do, we have to automate the language.