SYNTHESIZING MIND:
A PERSPECTIVE
A PERSPECTIVE
This continuing post, which will always begin with the identifier {Re:SYN5-}, will be the only entry point inviting open participation in the evolving Community of Relational Researchers focused on Synthesizing Mind. To participate, begin by Adopting-a-Gnome as per the procedure laid out in the post herein titled, Adopt-a-Gnome. Read the section entitled “What is the Adopt-a-Gnome Program” and then follow the directions under “How does one Adopt-a-Gnome?” As a TrueThinker, having subscribed through www.AHAInstitute.TrueThinker.com, one immediately becomes a participating Relational Researcher simply by way of the incorporation of their personal MindClone (MyKnowledgeBank) in the AHA! Institute-SYNMind CommunityKnowledgeBank; this latter serves as a resource knowledge-base for this research. More specifically, however, each open-entry participant is free to engage actively in this research at whatever level of Pragmatic Knowledge Value they competently offer as assessed by the AHA! Community–Builder.
PROLOGUE:
The problem of understanding Mind is considered by many to rank in importance with two of the other great cosmichaotic mysteries—the “origin” and nature of the Universe and the “origin” and nature of Life. However, understanding any part or the whole (holomereological relations) of human experience ultimately depends on understanding the nature of understanding itself which is generally acknowledged to be the province of Mind. Hence, understanding the “origin” and nature of Mind must arguably come first among these three as the ground of inquiry.
The last century saw the application of Boolean algebra (the algebra of logical statements) to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of Information theory and the generalization of Boolean algebra to Bayesian inference (a branch of mathematics that tries to factor in common beliefs and discount surprising results in the face of contrary historical knowledge) have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn (a proposed approach to Artificial Intelligence (AI)) by making inferences from data, specifically as systems which are able to automatically recognize patterns. The networking of computers has in turn led to Networks (Webs) of virtually unlimited data/information.
Although there are numerous positive technological impacts of these many conventional specifically engineered computer-based approaches to AI, there is also the potential threat of a concomitant emergence of a theory-glut (a wealth of foundationally unrelated theories/models with varied levels of applications successes/failures, but without a commonly-explicatable conceptual foundation) since there is no standard theory of Mind. To respond to this unanswered need in this digital (computer) revolution in information/communication technology, the methods of inquiry themselves must be opened to form a new method of quasi-whole inquiry/inference/intuition. In particular, the methodology of inquiry-- i.e. form hypotheses, devise ways to test them, analyze the data/information collected and then decide whether the results support or undermine the hypotheses-- must be concomitantly applied, not just to the degree of confirmation of the theoretical assertions, but also to both the applications results as well as the foundations notions (philosophically, formally, and theoretically) themselves which subtend the initiating hypotheses. This process in Relational Systems is referred to as the Pragmatic Action Methodology, an inquiry process which, by analogy, applies the principles of the scientific method to all levels of abstraction in the inquiry, from the philosophical through the theoretical to the applied, by iteratively changing the levels until the signified expectations are consistent with experience.
To ground new understanding in any area of research, the foundations need to be continuously iteratively re-formed so as to condense as much information (Extensively Inclusive) as is needed into the least necessary (Occam Simplicity) notions still consistent with experience. Hence, Relational Systems foundations presume only experience of things (Systems), connections (Relations), things or connections within other things or connections (Subsumption), and things or connections taking the place of other things or connections (Images). It is also a tenet of Relational Systems that any semiotic act must, of necessity, express the Form of experience as the inseparable conjunction of:
- Ordered (i.e. determined or certain) experience: a formal algebra/logic of semiosis
- DisOrdered (indeterminate or uncertain) experience: a theory of probable inference/inquiry
- ReOrdering DisOrdered experience: via a generalized probabilistic optimization principal
Relational Systems applications then begin from what is common to all inquiry, including the variety of specific AI approaches, i.e. they ultimately all have a formal foundation (‘language’) resting on images (signs). Indeed, One’s Experiences, ranging from common sense to the highly abstract, when expressed (i.e. “communicated”) ultimately rest in–and-on signs (Semiotic Systems). The ultimate goal is to develop semiotic systems with Intellectual, Emotive and Purposeful characteristics/capabilities inclusive of, but not necessarily limited to, those of humans, i.e. a Synthetic Mind. Hence, Project SYNMind is directed at the development of a Semiotic Relational Systems candidate for a Sandard Theory of Intelligence/Mind and its technological implementations as Synthetic Intelligence/SYNMind.
The American logician and philosopher Charles S. Peirce first conceived of and published results
in Boundary Logic (BL) in 1898. Peirce called his work Existential Graphs, and considered it to be among his greatest accomplishments. Existential graphs were sufficiently foreign an idea that both historians and biographers have omitted substantive portions of this work, considering them to be senile ravings. The next major contribution to BL was George Spencer-Brown's 1969 book Laws of Form, a mathematics text that caused considerable excitement at the time by introducing void-based logic within an algebraic framework. Influential logicians who studied Laws of Form failed to appreciate the fundamental nature of the concepts, and again BL was suppressed as "without content". Recognizing that Boundary Logic lacked a convincing application, William Bricken developed computational implementations of BL from 1978 through 2002. This patent pending work became the intellectual property basis of Bricken Technologies.
Noting only this prior work on BL, it should be obvious to all that Project SYNMind rests on a plethora of contributions of uncountable others, most of which will from time to time be cited in this blog, but inadvertently or by ignorance of the author (this latter being the most likely predominating cause) there may occur oversights for which it is expected there will be an adaptive correction through input from open-participation contributors to this AHAInstitute Blog. There are two profound contributors, however, to the foundations of mathematical/logical/theoretical developments during the 20th century (usually overshadowed by the like of Peirce, Gödel and Mandelbrot), i.e. Richard Threlkeld Cox, The Algebra of Probable Inference (Inquiry) and George Spencer-Brown, Laws of Form: Evolution of Consciousness, who will command center stage in grounding the formalization effort in this Project.
The Strategic Plan for creating SYNMind is being effected in two distinct R&D paths – the first being that of the Peircian-inspired/Pendegraft-specified/Pragmatic System of the AutoGnome; the second is to explicitly formalize a Semiotic Relational System (called the GnosTek) implementing multiboundary mathematics with the algebra of probable inference, inquiry and intuition, this being both a distinct approach to SYNMind and a formally rigorous re-specification of the AutoGnome.
