iConceptStore™ capabilities within AI context


Although knowledge bases, iConceptStore’s focal point, naturally constitute the backend foundation of the highly hyped nowadays Artificial Intelligence (AI) systems (see also ‘Why Cognitive Technology May Be A Better Term Than Artificial Intelligence’), the knowledge engineering technology has been largely neglected in favour of the fixed algorithms based agent and neural networks approach, mostly processing “big data” by means of machine learning statistical techniques for the benefit of natural language analysis and image recognition. Nowadays every data analysis company with statistical skills enthusiastically jumps on the "new AI" bandwagon, reducing the AI scope to those fixed algorithms based paradigms. Yet, fixed algorithms, however clever, are just fixed algorithms with fixed internal data representations. While historically they constitute the foundation of computer applications, they lack the main characteristics of knowledge-driven human thinking – extremely flexible and universally applicable general-purpose problem-solving abilities such as context-aware comprehensive problem analysis, setting of relevant attainable goals, goal-directed planning of alternative courses of action and conscious conclusion drawing. Without any prejudice towards their frontend (user-interface) and games applicability, those (once again) fashionable fixed algorithms based schemes seem quite simplistic and inadequate to explain or model any of the professional person’s intelligent activities.


For instance, today’s AI developers claim that their systems function through acquiring knowledge by learning on-the-fly. It took millennia for the mankind as a whole to acquire and refine (through goal-directed activities and related scientific reflection/reasoning) the expert knowledge (e.g., in the field of chemical or electrical engineering), accumulated in numerous scientific and technology books, journals, etc. and learned through multi-year education. The expectation that any artificial autonomous system, starting from scratch and employing fixed algorithms, could be “trained” (i.e., obtain expert-level knowledge of similar comprehensiveness and quality) in a reasonable period of time is naïve at best. Using trial and error based or statistical algorithms to find averaged patterns of correlation between data points (though useful) does not amount to expert knowledge acquisition as no real understanding of any situation can be derived without subsequent reflection/reasoning within a wider (e.g., cause-effect) context.


Intelligent behaviour without knowledge (based on deep understanding) of your surrounding is absurd (unstructured data, however big, is not knowledge). How could one understand (as claimed) any natural language (spoken or written) sentence without understanding the universe of discourse (totality of related objects, events, attributes, relations, ideas, etc, implied or assumed)? Dialogue reduced to exchange of seemingly well-formed shallow syntactic structures (plausible combinations of pre-stored phrases about a given topic) rather than thoughts is just an imitation of conversation (between stupid people).


This does not mean that verbal (instead of written SQL) requests for data and corresponding speech responses are not useful. Yet, they only belong to data retrieval request-response dialogues (using a more human-like interface), not conversations. Likewise, presenting every computer-based routine (conventional automation or remote control task) as AI application with vague promises (AI will increasingly replace repetitive jobs, not just for blue-collar work, but a lot of white-collar work” Kai-Fu Lee) for the distant future merely borders fake news. In most cases the recent AI hype is just a marketing ploy, aimed at securing more government or private funding (give me the money now and I will deliver wonders in 2050).


By contrast, the iConceptStore Cognitive Architecture is designed to provide to application systems dedicated CML feeds of well-structured relevant expert-level knowledge, attained by means of knowledge engineering techniques through prior comprehensive analysis and generalisation of similar problem situations and related decision-making methods applied by human experts (a common requirement in software development, anyway). At the same time, any other technique is also applicable as needed since the iConceptStore flexible dynamic architecture can easily accommodate any fixed algorithm based component (“AI” or otherwise) as a custom DLL/EXE extension, working either in isolation or in accordance with the iConceptStore built-in mechanisms within the context of relevant custom-defined expert knowledge.


There is nothing strange in having different aspects of AI (cognitive functions, such as thinking, spatial orientation, hearing, language, memory, attention, visual perception, etc.) developing in relative isolation just as most of them are located in separate parts of the brain (cerebral lobes). However, a complex refined interaction between these cognitive functions (in conjunction with respective body motor functions) is what makes the mind and body function as an integral whole. A similar degree of close integration is needed between all AI constituents.


In conclusion, the current one-sided view of AI systems represents a clear market advantage for iConceptStore – at some point the expert knowledge/reasoning deficiency will inevitably be realised and a research & development rush to integrate all AI components (with the expert knowledge base and reasoning mechanisms as its core again) will follow. Unlike most research organisations, seeking new sources of financial support under the umbrella of “new  AI” fixed algorithms based paradigms after their government-funded AI projects failed to live up to expectations (remember the 5th and 6th generation computers promised 30 years ago?), we did not stop working in the knowledge engineering field after its climax in the mid 1980s. The result is the iConceptStore underlying methodology, supported by language and software tools, which in combination fill some vital parts of that gap. While 30 years ago modelling human expert knowledge and reasoning was a mere research topic with distant practical prospects, nowadays it is much closer to becoming mainstream technology thus holding promise for huge investment returns.


With regard to its applicability, iConceptStore can be deployed widely as means of rationalisation and automation of any intellectual human activity, especially if ill-structured problem-solving and/or decision-making processes are involved. Of course, one should bear in mind that iConceptStore is a general-purpose software and related information base development tool, not an end-user application. Hence, the complexity and quality of any iConceptStore based system developed depends largely on the expertise and ingenuity of the developers involved. This aspect mirrors the use of natural languages – some humans speak very well, others no so well.


Furthermore, in pure practical terms iConceptStore is more than just a development tool – out-of-the-box its advanced multi-tear architecture seamlessly becomes a run-time feature of any particular application developed. Although its full potential can be only unlocked in developing intelligent expert knowledge-based systems, there is nothing in its architecture and tools preventing its use for developing ordinary software and information systems. This is a valuable practical proposition since no real world problem-solving and decision support tool can exist in a vacuum – often surrounded by conventional software and information components it must function within the infrastructure and context of such legacy application environments.



Copyright © 2005-2020 Dr Vesselin I. Kirov