Description

Into the highest level of invention or discovery we find that many of humanity's advances have been achieved by eccentricities of its author, where he has tried to imagine or create something different from what can be generated by deduction, imitation or reasoning, which they are already elaborate forms of intelligence. In this work is proposed to join one last layer after the abstracting information of the data of the process, and the first level of the intelligence, like NN, any classification methods, reasoning etc. a new layer of the analysis of this information to search some matching with the eccentricities proposed, this can produce one result that can be seen as a great level of intelligence if this is according to the hypothesis. The premises and viability are also analyzed to be viable the proposed hypothesis.
The artificial intelligence have been developed to be used in many new areas, started in a automatic control then in a robot navigation and recently in a cars and in a facial recognition etc. Also the algorithms have been changed, augmenting the quantity of the neurons in a NN, and mixing different techniques, clustering, genetic, stochastic algorithms, reasoning, and learning possibilities [1]. All these give huge potential at new applications. In this work, we focus to some new paradigms, as follows.
We understand as environmental intelligence, the knowledge that the human being has in a known environment of interaction between machines, tools, materials, space, etc., with which the operator knows exactly how to act, for example, after a problem arisen suddenly and new, there exist methods for isolation and fault detection, but not for the automatic resolution of them. We know that it is possible to improve the automatic resolution of the problems that have arisen, increasing the sensing of the environment, so that artificial intelligence can contribute more knowledge to the solution of the problem [2].
Super intelligence as an agent element [3] that encapsulates information and methodology, with capacity for artificial intelligence among the cooperating elements, which can overcome the brilliance of a trained human, leads to the development of skills and abilities of an inventor or artist. It is also being discussed if a robot or artificial intelligence should be entitled to patents or copyrights, for this the machine must be able to generate new tools or artistic or scientific creations, which is where we want to get to this work. The machine by artificial intelligence aims to discover or invent something that the programmer himself cannot accurately observe and predict. With this, the process will provide the data that will be used to adjust the algorithm parameters and obtain the appropriate results, therefore, the programmer by introspection can determine the formulae or the configuration method, it seems that it is not obvious, when there are large numbers of weights adapted by the methodology and parts of the enhancer algorithm, plus the interaction between the parts and their scheme. How to invent or create something new is possible, in this paper we present an upper layer of artificial intelligence, where the result of a neural network that already makes its decisions, for example, to determine whether it is a dog or a cat the image, and through for more information and this result, for example, indicate the character of that animal. For this, it is necessary to start from a hypothesis and provide an algorithm that represents it, and adjust the parameters through learning so that the new experiments obtain favorable results, as is the case of the new scientific theories that must at least be validated by the experimentation.

Eccentricities By climbing to the highest level of invention or discovery, we find that many of humanity's advances have been achieved by eccentricities of its author, where he has tried to imagine or create something different from what can be generated by deduction or reasoning, which they are already forms of intelligence. Starting from the basic forms of learning, by discovery, imitation and deduction, as well as the stages of learning of the human being in its first states, and then together with the accumulated knowledge, it allows the human being to predict new situations from the previous knowledge, and reason to deduce behaviors using new measuring devices, raise and find new knowledge from the analogy of what happened before. At the end of extracting knowledge from the previous information and the memory of cases, the capabilities of the human being are limited, that is why it is necessary to resort to the imagination, to invent the impossible with the acquired information and the existing relationships. A process that can lead to a significant loss of time and resources, since surely they will be inadequate, it must be verified that the hypothesis cannot be solved based on accumulated knowledge, a situation that would have already been determined if it were the result of a logical inference. You should also check that the hypothesis is not negative for the use of existing knowledge [4-7].
Example: suppose a chemist who proposes a new mixture of products and processes that can produce a synthetic material. First, it is to assess whether the proposed method is positive with existing knowledge, which has not yet been experienced [8-10]. Then verify that with the existing knowledge it is possible to reach the non-hypothesis. The next step will be to try, from the existing knowledge, to suppose a new form for the realization of the hypothesis, which will surely have different possibilities, some impossible, some possible and one of them optimal, that would be the desirable result, and with that it would have expanded prior knowledge and could be considered as a new and intelligent step [11,12].

References

1. Charles RD (2015) Ambient intelligence for optimal manufacturing and energy efficiency. Assem 35: 234-248. [Crossref], [Google Scholar]
2. Tang DL, Sun AP, Qiu XM, Paul KC (2003) Interaction of electromagnetic waves with a magnetized nonuniform plasma slab.  IEEE Trans Plasma Sci 31:3. [Crossref], [Google Scholar]
3. Gurel CS, Oncu E (2009) Interaction of electromagnetic wave and plasma slab with partially linear and sinusoidal electron density profile. Prog Electromagn. Res 12:171-181. [Crossref], [Google Scholar]
4. Papadopoulos K, Chang CL (2011) The first demonstration of HF-driven ionosphere currents. Geophys Res Lett 38. [Crossref], [Google Scholar].
5. Mursula K, Ulich T (1998) A new method to determine the solar cycle length. Geophys Res Lett 25: 1873- 1840. [Crossref], [Google Scholar]
6. Leyser TB, Wong AY (2009) Powerful electromagnetic waves for active environmental research in geospace. Am Rev Geophys 47:1-33. [Crossref], [Google Scholar]
7. Herbert C, Carison Jr (1993) High power HF modification: geophysics, span of EM effects, and energy budget. Adv Space Res 13:15 â?? 24. [Crossref], [Google Scholar]
8. Deek FP (1999) The software process: a parallel approach through problem solving and program development. Comput. Sci. Educ 9: 43-70. [Crossref], [Google Scholar].
9. Mulder M, Haines JE, Prey JC, Lidtke DK (1995) Collaborative learning in undergraduate information science education. SISCSE Comp Sci Edu pp.400-401. [Crossref][Google Scholar].
10. Prey JC (1996) Cooperative learning and closed laboratories in an undergraduate computer science curriculum. Comp Sci edu pp: 23-24. [Crossref], [Google Scholar].
11. Nunamaker J (1999) Collaborative computing: The next millennium. Computer 32: 66-71. [Crossref], [Google Scholar]
12. Wilson J, Hoskin N, Nosek J (1993) The benefits of collaboration for student programmers. SIGCSE Comp Sci Edu pp: 160-164. [Crossref], [Google Scholar]