Artificial intelligence (AI) is growing in diversity of application and influence. From healthcare, education, business management, sports to space exploration; the role of AI is expanding rapidly. The current AI applications are predominantly data-driven running algorithms designed by humans. The Deep Learning (DL) / Machine Learning (ML) based AI technologies process large amounts of data and learn from the data to provide improved predictions and inferences, which are revolutionizing working and operations across a wide array of business functions. At the same time, despite being termed artificial intelligence, the current AI applications lack genuine intelligence per se. In fact, AI's intelligence capabilities are limited to human programmer's intelligence, expertise, and the quality of data fed into the system.

The situation highlights the need for more work pertaining to new thoughts on AI development and building technology that can exhibit some limited form of intelligence within the bounds of ethical and harmless usage.  Are there ways to do that simply by going back to basics, and beginning with a discussion on what human cognition is?

Human cognition

Neisser defined human cognition as "processes by which sensory inputs are transformed, reduced, elaborated, stored, retrieved and used" (cited in Mulder, 1983). By using intellectual tools, humans acquire, understand, and comprehend the knowledge and dynamically use that knowledge for decision-making and actions. As the definition suggests cognition is not entirely a process of detailed analysis of everything, it also depends on intuition and sense-making. Not surprisingly, a lot of human actions and decisions are dependent on intuition or more formally Intuitive Intelligence (II). While the human brain has the capability to process information by using intuitive intelligence, the dynamic nature of the process poses a significant challenge for developing a technology that can mimic the human brain's intuitive intelligence capabilities. In fact, this is one of the major limitations of current AI technologies.

The way forward, therefore, is to examine how can AI achieve some limited form of intuitive intelligence that can help AI machines perform tasks more efficiently while complying with ethical and harmless use conditions.

Reasons why II should be the mainstay for further developments in AI

Just as there is one set of laws governing all forces of the cosmic universe, there is one set of laws governing the microcosmic human brain, which is capable of understanding the governance of the universe.  Therefore, everything that we endeavor to create, ought to be devised with that equitable application in mind.  That is to say that there must be a fundamental parallel consensus between the governance of both AI and II.  To enter the Einsteinian age of thought, there is a pending acknowledgment awaiting us, and it is that nothing is exempt from the governing laws of the universe. Commensurability between AI and II, which must be based on genuine Emotional Cognition, is the key for advancing technology that will automatically incorporate the factors of morals, ethics, altruism, transferability, untethered connectivity, and trust in all information systems.

With this standard issue in mind, then the patterns, sequences, and formulas for advancing AI must be conducive and correlative to the processes of natural knowledge postulates, as defined by universal cosmic laws that are fully comprehensible to the microcosmic human brain.  Cognition is a mathematical function, and everything including artificial systems, is an extension of the Language of the Universe – we all hail from the Music of the Spheres.

The idea set forth here is that human intuitive intelligence is the blueprint for technological A.I. (Artificial Intelligence).  The most significantly valuable evidence that can be supplied for any field of knowledge, including science, is rooted in the fundamental principles of mathematics, which already exist as a natural resource for evidentiary proof – both numerical and non-numerical. Science itself relies on natural math for its evidence and Einstein used the Principalities of Math to conjure both his thought experiments and hypothesis for Relativity.

If the collaboration between scientific fields is to participate in the advancement of AI, then that advancement ought to include Neuroscience.  However, neuroscience needs to upgrade its fundamental analyses for Cognition. One is that there is still a prevailing assumption that Emotional Cognition is yet to be defined and is depending on the singular version of Cognition to establish its distinctive description. Yet this is an inversion, typical of reverse engineering because Emotional Cognition is the first cognitive process and therefore the primary platform for defining Cognition itself if in fact Cognition can be considered a singular process.

The most underlying foundation features of universal information processing reveal that intuitive intelligence, cognition, diversification, and the basic principles of Mathematics are one and the same, or literally identical on the most fundamental levels of knowledge and information. AI is positioned to set an example for the legitimate traits of diversified knowledge applications. As diversification is a natural process of cognition itself, even as it is infused into the earliest phases of human II development, so it must be for AI.  Creativity at the basic level is due to become a requisite feature for formulating unlimited possibilities in artificial information systems, with the same certainty that AI will be used across a vast spectrum of fields and human services applications.

Conclusion

Identifying the relationship between AI and II is one thing.  Resolving and coding the precursory elements of II is the next step.  This leads us to contend with decoding the issues that perpetuate misunderstandings about how Reasoning and Emotions are (typical of pre-Einsteinian Physics) governed by separate sets of laws — or, that reasoning is the impetus or driving logic behind intelligent Emotions.  In truth, the only reason we have advanced reasoning skills is that we possess advanced emotional skills. In other words, how can we unleash the infinite potentialities of AI, without the impending fear of doom that machines will rule over us with anything less than a robust and impervious code of moral fortitude?  Although we must make a conscious effort to employ what is innately inherent in our potential, because of a historical lack of emotional literacy, it is nonetheless possible because of its original imprint. Incorporated in the anatomy of intuition, reasoning can easily be performed as an intuitive intelligence skill, based on authentic human Emotioning — the roots of which are inherent in the compassion and optimism hardware of the natural human brain (along with other hard-wired elements endemically poised for a full spectrum of Human II skills).

References

Mulder, G. (1983). The information processing paradigm: concepts, methods, and limitations. Journal of Child Psychology and Psychiatry, 24(1), 19-35.

Co-authored by:

Carla Mahnken Wool

Professor Jiwat Ram

Carla Mahnken Woolf

Carla has spent nearly three decades doing independent research on Cognitive Development as an Intuitive Intelligence process. During that time, she worked as a Tutor and Teacher, delving into the 4-year-old stage of Brain Development, and the Cognitively Correct codes for Intuitive Language development.

Currently, she has completed four books covering this theme and culminating in outlining its effects on Cognitive impairment in Dementia and Alzheimer's. Her work was granted a Ph.D. from the Open University of Sri Lanka.

LinkedIn: https://www.linkedin.com/in/carla-mahnken-woolf-7a7b57176/

Professor Jiwat Ram

Professor Ram writes on some of the contemporary issues in technology and business management. He has a growing portfolio of publications on emergent technologies, including AI, Social media, and Big data.

Jiwat has over 120+ publications (in journals, conferences, industry outlets) and he has published his research work in top-tier high impact factor journals including Journal of Global Information Management, International Journal of Production Economics, Computers in Human Behaviour, and Enterprise Information Systems, among others.

His published work has been well received and four of his published papers have ranked in Top25 most downloaded papers from ScienceDirect. His two papers have been ranked in the Top 25 Most Cited articles as well.

LinkedIn: www.linkedin.com/in/jiwat