Empowering Cognition by Precisation of Numeric Words

Introduction

For the ICBS (Institute for Cognitive and Brain Sciences) seminar given September 12, 2008 at UC Berkeley, Lotfi Zadeh concluded that to make significant progress toward achievement of human level machine intelligence a paradigm shift is needed (Zadeh, 2008). More specifically, what is needed is an addition to the armamentarium of AI of two methodologies:

• A.

  A nontraditional methodology of computing with words (CW); and

• B.

  A counter-traditional methodology which involves a progression from computing with numbers to computing with words. The centerpiece of these methodologies is the concept of precisation of meaning.

Since then, accordingly, many top-down (TD) attempts have been developed to satisfy Zadeh's point (a) requirement. Among them, Cognitive Informatics (CI), a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing (CC), has been gathering a lot of attention and consensus by advanced researcher groups. The LRMB (Layered Reference Model of the Brain) (Wang, 2012; Wang et al., 2006) provides a TD integrated framework for modeling the brain and the mind. LRMB also enables future extension and refinement of the CPs (Cognitive Processes) within the same hierarchical framework. LRMB can be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly the relationships and interactions between the inherited and the acquired life functions, those of the subconscious and conscious CPs, as well as the dichotomy between two modes of thought: “System 1”, fast, instinctive and emotional; “System 2”, slower, more deliberative, and more logical (Kahneman, 2011).

Nevertheless, the current performance of artificial systems is still very far from the requirements of effectiveness, robustness, compactness and autonomy, necessary for a meaningful and skilled interaction with the external world as auspicated by Zadeh in 2008. The human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine today (Fiorini, 2016a). The unavoidable conclusion is that we have something fundamental to learn from the human brain about a new and much more effective form of computation (Resconi, 2012), with a convenient, effective, efficient and reliable bottom-up (BU) approach (Fiorini, 2016b, 2016c).

At the 15^th IEEE International Conference on Cognitive Informatics & Cognitive Computing, Rodolfo Fiorini presented a paper introducing neuromorphic ALS (anticipatory learning system) (Fiorini, 2016a), where he suggested that to satisfy Zadeh’s point (a) and (b) requirements and to achieve reliable system intelligence outstanding results, current computational system modeling and simulation has to face and to overcome two orders of issues at least, immediately:

• 1.

  To minimize the traditional limitation of current digital computational resources that are unable to capture and to manage even the full information content of a single Rational Number Q exactly, leading to information dissipation and opacity.

• 2.

  To develop stronger, more effective and reliable correlates by correct arbitrary multiscale (AMS) modeling approach to complex system.