The Peer to Peer Social Fabric as a Platform for General Collective Intelligence

2021

Preprint

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A functional modeling approach is used to derive the properties that must be possessed by a platform with the capacity to significantly increase the general collective intelligence or c factor of groups. Such platforms have been termed “General Collective Intelligence” or GCI platforms. Having general problem-solving ability, a GCI potentially enables groups to execute any collective reasoning process, including abstracting (generalizing) a reasoning process so it might be reused in any other domain where it applies. A GCI can be shown to have the potential to exponentially increase the capacity of a group to create generalizations and other relationships, and capacity to store and exchange those relationships. Since relationships are concepts, and since the number of relationships between concepts better specify the location of any concept in conceptual space and therefore increases the density of conceptual space as a whole, GCI represents a phase change in collective cognition at which the collective conceptual space can expand exponentially in size and density. Each reasoning process connecting this far larger space of concepts has outcomes, making it potentially possible through these additional concepts to accumulate far greater impact on any outcome in the world. Because this phase change is not believed to have been possible at any point before in history, and is believed cannot occur again until the advent of another system with general problem-solving ability, such as a second order GCI or an Artificial General Intelligence (AGI), and because both AGI and second order GCI are believed to require GCI, GCI is proposed here to be the most important innovation in the history and immediate future of human civilization.

Section: Functional Adaptationmentioning

confidence: 99%

Section: Functional Adaptationmentioning

confidence: 99%

Deducing the Properties Required by General Collective Intelligence Platforms

2021

Preprint

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“…A virtual platform with the capacity to orchestrate the collective execution of processes by such functional components must be able to virtualize every attribute of such processes so that each functional component can exchange a virtual representation of that attribute. One such platform has been proposed [6].…”

Section: Figure 8: In Human-centric Functional Modeling General Problem-solving Ability Is Depicted As the Capacity To Potentially Form Amentioning

confidence: 99%

Human-Centric Functional Modeling of Biocomputers

2021

Preprint

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This paper explores how the technique of Human-Centric Functional Modeling might potentially be used to represent a broad subset of proposed implementations of biocomputing with anywhere from narrow to general problem-solving ability within a given domain, or across multiple domains, and how such functional models might be implemented by libraries of biological computing mechanisms. This paper also explores the insights to be gained from modeling biocomputers this way, and how Human-Centric Functional Modeling might significantly accelerate research and increase the impact of research in biocomputing through significantly increasing capacity for reuse of both biocomputing hardware and software.

“…It requires virtualization of all properties of the GCI platform so they can be changed by the group within the processes of the platform itself. For example, a GCI should be able to adopt any user interface that is best suited to manage a given collaboration so that the collaboration isn't restricted to the process envisioned by a single UI designer or single application owner [19]. Furthermore, the reasoning or understanding processes of any system of cognition might be based on intuition or on rational methodical reasoning.…”

Section: Centralized Processes Solve Problems For Different Parties Than Decentralized Processesmentioning

confidence: 99%

General Problem-Solving Ability in Natural Systems as a Model for Computation

2021

Preprint

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Natural systems have demonstrated the ability to solve a wide range of adaptive problems as well as the ability to self-assemble in a self-sustaining way that enables them to exponentially increase impact on outcomes related to those problems. In the case of photosynthesis nature solved the problem of harnessing the energy in sunlight and then leveraged self-assembling and self-sustaining processes so that exponentially increasing impact on that problem is reliably achievable. Rather than having to budget a given amount of resources to create a mature tree, where those resources might not be reliably available, tree seedlings self-assemble in a self-sustaining way from very few resources to grow from having the capability of photosynthesis accompanying a single leaf, to the capability of photosynthesis accompanying what might be millions of leaves. If the patterns underlying this adaptive problem-solving could be abstracted so that they are generally applicable, they might be applied to social and other problems occurring at scales that currently are not reliably solvable. One is the Sustainable Development Goals (SDGs) funding gap. The funding believed to be required to address the SDGs is difficult to estimate, and may be anywhere between $2 trillion and $6 trillion USD per year. However, bridging the gap between the funding required to meet these goals and the funding available to do so is universally acknowledged to be a difficult and unsolved problem. This paper explores how abstracting the pattern for general problem-solving ability that nature has used to solve the problem of exponentially increasing impact on collective problems, and that nature has proven to be effective for billions of years, might be reused to solve “wicked problems” from implementing an Artificial General Intelligence (AGI) to funding sustainable development at the scale required to transform Africa and the world.