# Cohesive Approach in Learning

## 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.

## The coordination of AI and the basic human understanding of language interpretation

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We may confuse ourselves when we rely on AI as an authoritative powers of logic construction nevertheless, the mathematical presumption of C and R numbers in basics of algebra may say so. An understanding of language solution to the current state of mind is a definition of logical subtraction human brain provides, however the mathematical exclusion stands for its definition in AI reasoning as static.

Simply saying what is a code for a machine is a dynamical principle for a human:

Static data – dynamic data

Therefore, the simulation of simultaneous information in human brain comprehension needs to be verified in between static’s and dynamic’s data in order to proceed to a new level of learning.

An Elephant is an animal; therefore a Zebra is an animal.

An Elephant is and animal, how often it could be defined as an animal?

The scientific solution and an academic approach of human brain language interpretation may be puzzled further as a solution ‘X’, further applied in the following formulas.