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Prof. Maria K. Koleva shares her content expertise on semantic intelligence.

Semantic Intelligence and Exploration of Outer Space

By Maria Koleva on Jul 12, 2017

So far, the concept that all laws of nature are the same along the entire universe and that each of them is describable by recursive means is dominant for the scientific community. Thus, it is assumed that each and every law of nature can be derived by means of a small number of simple rules each of which describes a given interaction. This renders one of the major goals to be the development of fastest and smallest computers to meet the ever increasing demands of computing performance which grows enormously when the matter concerns such ambitious tasks as exploration of outer space.

At that point, however, a dilemma arises whether the problem is only technical or it has fundamental aspects as well. I assert that the problem has not only technical aspects, but has serious fundamental background issues. The fundamental problem arises from the exclusive for the algorithmic intelligence (Turing machine) property consisting of ubiquitous match between any hardware and any algorithm (i.e. any computer can execute any algorithm). However, this property comes at the expense that neither computer can comprehend the output in an autonomous way. Thus, computers need assistance from our human mind to comprehend the results. In turn, this poses the question about the criterion for discrimination of true from false results. The problem is especially severe for unexplored events and/or incomplete knowledge for a given event. Thus, the major question becomes whether there is another type of intelligence whose exclusive property is autonomous creation and comprehension of information.

My solution to this problem is called semantic intelligence. It is exclusive for intelligent complex systems way of responding in an ever changing environment. Its major properties are autonomous creation, comprehension of information and autonomous discrimination between true and false statements. The semantic intelligence naturally arises in the setting of the concept of boundedness, where it commences from highly non-trivial interplay between structural and functional properties of a complex system. The major generic property of that interplay is that it renders the relation between structural and functional properties to be non-recursive.

Concept of boundedness is a new explanatory paradigm aimed towards explanation of the behavior of complex systems. In the setting of this paradigm, the semantic intelligence is implemented by a specific for each intelligent complex system hierarchical self-organization of the physical processes. The major generic property of this hierarchy is that it is bi-directional and non-extensive.

The concept of boundedness is the first systematic theory which considers the behavior of complex systems in non-specified non-predetermined ever-changing environment subject to mild assumption of boundedness alone. The major assumption is that a non-predetermined bounded environment is the starting point for fundamentally novel properties of complex systems behavior, semantic intelligence included, which are not available for the traditional scientific approach where the environment is statistically or deterministically pre-determined and unbounded.

The fundamental discrimination between the concept of boundedness and the traditional approach consists of the fact that under boundedness the behavior of corresponding systems, semantic intelligence included is describable by non-recursive means while in the frame of the traditional approach the behavior of the algorithmic intelligence is describable by recursive means only. In turn, this renders an exclusive property of semantic intelligence to be that partial laws in Nature are algorithmically un-reachable from one another. This makes their exploration to be automatically executed in an un-ambiguous way and with certainty only by devices set on semantic intelligence.

At this point a question arises whether the semantic intelligence is able to meet also the difficulties which the ever-increasing demands to computing performance pose. So far the major concern, commonly known as Moore`s law, is that the storage capacity achieved with existing technologies will eventually reach a plateau. At present, the attention of the scientific community is focused on establishing consensus on what type of emerging technology holds most promises to keep up the current pace of progress. Yet, the problem would be only partially set by the merits of the best technology because it is tacitly presupposed that whatever the appropriate choice would be, the computing process will follow the traditional information theory which, however, renders the computing an extensive process regardless to the technological merits of the hardware. The semantic intelligence retains a fundamental property that is non-extensivity of information organization and its processing whose major goal is to provide substantial reduction of production costs and considerable speeding up of the computing.

Outlining, the semantic intelligence turns as a fundamentally novel implement for exploration of unknown events. Next I will demonstrate that it steadily operates in unknown environment. In turn, these properties render it a promising candidate for next generation performance strategy whose most ambitious goal is the exploration of outer space.

A central for the theory of boundedness result, called decomposition theorem, is that there exists a presentation basis where the response of each and every complex system decomposes into two parts, specific and universal one, each of which has characteristics that are robust to the details of variations of the environment let alone the latter are bounded. The specific part consists of a stable pattern called by me homeostasis. Its exclusive robustness to environmental variations provides its behavior stable and predictable. Alongside, the highly non-trivial interplay between the specific and the universal part implements presence of a non-recursive component which persists additionally to the universal and the specific part. Namely the persistent presence of a non-recursive component substantiates that algorithmic un-reachability on one specific law Nature from any other. These results render another enormous advantage of the semantic intelligence, i.e. that it is capable of operating in an ever-changing environment whose characteristics are unknown or which change in the course of time.

It should be stressed on the fact that namely the homeostasis of intelligent complex systems consists of non-extensive hierarchical organization of semantic structures. Thus, its major properties are: robustness to environmental variations (even unknown); predictability up to predictability of the homeostatic pattern; autonomous creation and comprehension of information; autonomous discrimination between true and false statements.

Crucially important for the entire theory of boundedness is the fact is that probability type representation is outperformed by the decomposition theorem because it holds even when the conditions for application of major statistical theorems such as Law of Large Numbers and Central Limit Theorem do not hold.

The major setback of semantic intelligence is that it requires specific match between hardware and software. However, this setback turns advantageous because namely it serves as grounds for autonomous creation and comprehension of information. Alongside it turns advantageous when concerning enhancement of the variety of devices needed for encountering the diversity of concrete tasks. Since each and every task has its specific particularities, a properly designed device can meet the demands at substantial reduction of production and computing costs. The reward would be the certainty of obtained information especially when the matter concerns such ambitious tasks as exploration of outer space.

I see the next stage of development in artificial creation of specific ecosystems able to operate steadily at harsh and unfriendly for human beings conditions. The major challenge to this goal is finding out how to cooperate with the algorithmic intelligence to create functional circuits capable of human-like intelligence.

References

Koleva, M. K. (2013). Boundedness and Self-Organized Semantics: Theory and Applications (pp. 1-233). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-2202-9

Koleva, M. (2016) Boundedness and Applied Self-Organized Semantics. Retrieved from https://www.igi-global.com/video.aspx?ref=boundedness-applied-self-organized-semantics&titleid=137626

Koleva, M. K. (2017) Semantic Intelligence. Encyclopedia of Information Science and Technology, 4-th Edition, Ed. M. Khosrow-Pour, Hersey, PA: IGI Global. vol.1, chapter 20, pp.220-228

A sincere thanks to Prof. Koleva for taking time out of her busy schedules to collaborate with IGI Global and for sharing her thoughts on Semantic Intelligence. To read more about Prof. Koleva research on Digital Transformation, be sure to check out her article in the recently released Encyclopedia of Information Science and Technology, Fourth Edition.

Disclaimer: The opinions expressed in this article are the author’s own and do not reflect the views of IGI Global.

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