# Cohesive Approach in Learning

## 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.

## Future professions and the future business models

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The systems that deal with cognitive solutions aren’t merely academically based and confounded. The technological experience of speech and face recognitions are the long-term existent technologies.

Nevertheless, the academic research on computational data and logical cohesion in learning goes further to the direction of imposing a new set of levels; such as, implementing of a more casual AI observation in commonly routines than just recognizing face patterns and waves of speech in technical solutions.

The further thought-forms shall be sifted from the following branches of sciences in order to understand more on the language and cognition implementation in AI and it's comparability with human learning/reasoning:

- AI reasoning
- AI computation
- Applied mathematics
- Cognitive computation
- Computational AI
- Computational logic
- Computational mathematics
- Language systems in AI

And the other related fields divide open source materials and programmers into the spheres where and independent researcher could have a ‘lab’ of its kind in where an open source information is free literally.