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

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Peer To Peer ◽  
Cognitive Computing ◽  
Social Fabric ◽  
Exponential Increase ◽  
Computing Platforms ◽  
High Level

INTRODUCTION: General Collective Intelligence or GCI has been defined as a platform that combines individuals into a collective intelligence with the potential for exponentially greater general problem-solving ability (intelligence) than that of any individual in the group. Cognitive computing applications are executed by intelligent agents on a user’s behalf to optimize individual user outcomes. Cognitive computing platforms are executed by individuals organized by a GCI to achieve greater collective outcomes. The Peer to Peer Social Fabric (P2PSF) is an infrastructure platform proposed to enable the execution of cognitive computing applications or platforms.OBJECTIVES: To explore the functionality required by an infrastructure platform with the capacity to enable the operation of cognitive computing applications or platforms. And to determine whether the functionality of the proposed Peer to Peer Social Fabric is sufficient.METHODS: The requirements of collective cognitive computing were assessed, including the requirement to increase capacity for complexity, capacity to scale number of processes, and capacity to sustain processes. The proposed high-level specifications of the Peer to Peer Social Fabric were compared to those requirements to determine whether that functionality is sufficient.RESULTS: The proposed Peer to Peer Social Fabric appears to meet the requirements of both the operating system for GCI, as well as the requirements of a client enabling individuals and organizations to access either AGI or GCI functionality.CONCLUSION: All of the functionality required by he Peer to Peer Social Fabric might already exist, but an understanding of how that functionality must be combined in order to achieve the exponential increase in general problem-solving ability potentially possible through AGI or GCI is a new and important contribution.

scholarly journals Discovering and Implementing Self-Sustaining Networks of Cooperation with General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Iterative Process ◽  
Collective Intelligence ◽  
Exponential Increase ◽  
Growing Networks ◽  
First Time

Leveraging General Collective Intelligence or GCI, a platform with the potential to achieve an exponential increase in general problem-solving ability, a methodology is defined for finding potential opportunities for cooperation, as well as for negotiating and launching cooperation. This paper explores the mechanisms by which GCI enables networks of cooperation to be formed in order to increase outcomes of cooperation and in order to make that cooperation self-sustaining. And this paper explores why implementing a GCI for the first time requires designing an iterative process that self-assembles continually growing networks of cooperation.

scholarly journals Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Functional Modeling Perspective

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Computing Methods ◽  
Modeling Technique ◽  
Human Cognition ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Semantic Modeling ◽  
Advanced Computing

Recent advances in modeling human cognition have resulted in what is suggested to be the first model of Artificial General Intelligence (AGI) with the potential capacity for human-like general problem-solving ability, as well as a model for a General Collective Intelligence or GCI, which has been described as software that organizes a group into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. Both this model for GCI and this model for AGI require functional modeling of concepts that is complete in terms of meaning being self-contained in the model and not requiring interpretation based on information outside the model. This definition of a model for cognition has also been suggested to implicitly provide a semantic interpretation of functional models created within the functional modeling technique defined to meet the data format requirements of this AGI and GCI, so that the combination of the model of cognition to define an interpretation of meaning, and the functional modeling technique, together result in fully self-contained definitions of meaning that are suggested to be the first complete implementation of semantic modeling. With this semantic modeling, and with these models for AGI and GCI, cognitive computing is far better defined. This paper explores the various computing methods and advanced computing paradigms from the perspective of this cognitive computing.

scholarly journals Bridging the Left - Right Political Divide with Artificial Intelligence and Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Cognitive Bias ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Optimal Solution ◽  
Functional Modeling ◽  
Political Debate ◽  
The Individual ◽  
Political Divide

A model of cognition suggests that the left vs right political debate is unsolvable. However the same model also suggests that a form of collective cognition (General Collective Intelligence or GCI) can allow education, health care, or other government services to be customized to the individual, so that individuals can choose services anywhere along the spectrum from socialized services if they desire, or private services if they desire, thereby removing any political stalemate where it might prevent any progress. Whatever services groups of individuals choose, GCI can significantly increase the quality of outcomes achievable through either socialized or private services today, in part through using information regarding the fitness of any services deployed, to improve the fitness of all services that might be deployed. The emerging field of General Collective Intelligence (GCI) explores how platforms might increase the general problem-solving ability (intelligence) of groups so that it is significantly higher than that of any individual. Where Collective Intelligence (CI) must find the optimal solution to a problem or group of problems, having general problem-solving ability, a GCI must also have the capacity to find the optimal problem to solve. In the case of political discussions, GCI must have the ability to re-frame political discourse from being focused on questions that have not proved resolvable, such as whether or not left leaning or right leaning political opinions are in general more “right” or “wrong”. Instead GCI must have the ability to refocus discussions, including on how to objectively determine whether a left or right bias optimizes outcomes in a specific context, and why. This paper explores the conjecture that determining whether a left leaning or right leaning cognitive bias is "optimal" (i.e. "true) based on any CI or other aggregate of individual reasoning that is not GCI, cannot reliably converge on "truth" because each individual cognitive bias leads to evaluating truth according to different reasoning types (type 1 or type 2) that might give conflicting answers to the same problem. However, through using functional modeling to create the capacity to represent all possible reasoning processes, and through using functional modeling to represent the domains in conceptual space in which each reasoning process is optimal, it is possible to systematically categorize an unlimited number of collective reasoning processes and the contexts in which execution of those reasoning processes with a right leaning or left leaning bias is optimal for the group. By designing GCI algorithms to incorporate each bias in its optimal context, a GCI can allow individuals to participate in collective reasoning despite their biases, while collective reasoning might still converge on "truth" in terms of functioning to optimize collective outcomes. And by deploying intelligent agents incorporating some subset of AGI to interact on the individual's behalf at significantly higher speed and scale, collective reasoning might gain the capacity to consider all reasoning and all "facts" available to any individual in the group, in order to converge on that truth while significantly increasing outcomes.

scholarly journals Does Creating an Artificial General Intelligence Require General Collective Intelligence in Order to be Reliably Achievable?

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Cognitive Functions ◽  
Collective Intelligence ◽  
Functional Model ◽  
General Intelligence ◽  
Artificial General Intelligence ◽  
Exponential Increase ◽  
Group A ◽  
Complete Set

General Collective Intelligence or GCI has been predicted to create the potential for an exponential increase in the problem-solving capacity of the group, as compared to the problem-solving capacity of any individual in the group. A functional model of cognition proposed to represent the complete set of human cognitive functions, and therefore to have the capacity for human-like general problem-solving ability has recently been developed. This functional model suggests a methodical path by which implementing a working Artificial General Intelligence (AGI) or a working General Collective Intelligence might reliably be achievable. This paper explores the claim that there are no other reliable paths to AGI currently known, and explores why this one known path might require an exponential increase in the general problem-solving ability of any group of individuals to be reliably implementable. And why therefore, AGI might require GCI to be reliably achievable.

scholarly journals General Collective Intelligence and the Cloud

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Cloud Computing ◽  
Collective Intelligence ◽  
Cloud Service ◽  
Cloud Services ◽  
Cognitive Computing ◽  
Computing Platform ◽  
On Demand ◽  
Exponential Increase ◽  
Functional Models ◽  
Computing Platforms

The resources that can be made available on-demand through cloud computing are continually increasing. One potential addition is General Collective Intelligence or GCI, which has been defined as a platform that combines individuals into a single intelligence with the potential for exponentially greater general problem-solving ability (intelligence) than any individual. The concept of a cognitive computing platform involves leveraging GCI to orchestrate cooperation between any entities that are required in order to create the capacity to maximize any collective outcome targeted. Rather than executing programming code, a cognitive computing platform must execute functional models in which each of the functional operations composing that model is implemented in some programming language. All services that run on the cloud, as well as the cloud itself, can potentially be offered as cognitive computing platforms. Where current cloud computing limits customers to a particular cloud service vendor, cloud computing as a cognitive computing platform has the potential to completely decouple users from any such dependencies, while at the same time creating the potential for an exponential increase in demand for cloud services from those vendors that participate by decoupling their services in this way.

scholarly journals Assessing the Potential Impact of General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Second Order ◽  
Human History ◽  
First Order ◽  
Exponential Increase ◽  
Potential Impact ◽  
Near Term

The concept of General Collective Intelligence or GCI is summarized, and the potential for GCI to exponentially increase the general problem-solving ability of the group so that it is far larger than that of any individual in the group, and therefore the potential for GCI to exponentially increase the ability of groups to impact collective outcomes are explored. GCI is represented as a repeating pattern, beginning with a first order GCI, then progressing to an Nth order one, where N might be limited by the resources available, and where each order is suggested to create the potential for an exponential increase in general problem-solving ability. Finally, the claim that such an exponential increase in potential for impact on any general problem makes GCI the most important innovation in human history, and the most important innovation in the near term future, until the transition to second order GCI, is explored.

scholarly journals Functional Modeling and General Collective Intelligence as the Basis for Pervasive Healthcare

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Intelligent Agents ◽  
Collective Intelligence ◽  
Health And Wellness ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Pervasive Healthcare ◽  
Computing Platforms ◽  
Definition Of ◽  
Universal Mechanism ◽  
Gathering Data

General Collective Intelligence has the potential to combine individuals into a single collective collective intelligence with general problem-solving ability (intelligence) that might be exponentially greater than that of any individual. In every software domain, including health and wellness, General Collective Intelligence and functional modeling have the potential to enable the definition of pervasive cognitive computing applications and platforms. In such cognitive apps, intelligent agents might provide services to the user that optimize their outcomes by independently executing functional operations in each software domain on whatever software best implements those operations, and independently incorporating any possible data available to the user in the best way available. And at the same time in such cognitive computing platforms, a GCI might orchestrate the process of gathering data from all such individual uses in order to optimize collective outcomes such as significantly increasing healthcare and wellness. And these models of individual and collective cognition suggest that such optimization might not be reliably achievable otherwise. For both of these cognitive computing approaches functional modeling is required to provide a universal mechanism for representing data and processes. Therefore, to achieve significantly increased healthcare and wellness outcomes both functional modeling and GCI might be required. Functional modeling has the potential to overcome the lack of consistency in type and format of data gathered and the lack of a mechanism for universally comparing and combining that data. This paper explores why functional modeling might not only be of critical importance to pervasive healthcare, but why it also might be critical to significantly improving capacity to diagnose and to make interventions.

scholarly journals Using Systems Thinking to Exponentially Increase Impact

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Climate Change ◽  
Problem Solving ◽  
Systems Thinking ◽  
General Problem ◽  
Collective Intelligence ◽  
General Intelligence ◽  
Functional Modeling ◽  
Artificial General Intelligence ◽  
Exponential Increase

This paper explores how Human-Centric Functional Modeling might provide a method of systems thinking that in combination with models of Artificial General Intelligence and General Collective Intelligence developed using the approach, creates the opportunity to exponentially increase impact on targeted outcomes of collective activities, including research in a wide variety of disciplines as well as activities involved in addressing the various existential challenges facing mankind. Whether exponentially increasing the speed and scale of progress in research disciplines such as physics or medicine, or whether exponentially increasing capacity to solve existential challenges such as poverty or climate change, this paper explores why gaining the capacity to reliably solve such challenges might require this exponential increase in general problem-solving ability, why this exponential increase in ability might be reliably achievable through this approach, and why solving our most existential challenges might be reliably unachievable otherwise.

scholarly journals General Collective Intelligence and the Transition to Collective Super-Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
Phase Change ◽  
General Problem ◽  
Collective Intelligence ◽  
External World ◽  
Human Intelligence ◽  
Functional Modeling ◽  
Collective Cognition ◽  
Exponential Increase ◽  
Functional Models

General Collective Intelligence has been defined as a system that combines individuals into a single collective cognition with the potential for vastly greater intelligence than any individual in the group [1], [2]. A novel Human Centric Functional Modeling approach [3] has been used define a model for this collective cognition, and for individual cognition [4], as well as for the intelligence of those systems of cognition, in order to quantify this potential increase in intelligence as exponential. Where other approaches assume the functions of cognition are implemented through mechanisms that are not yet confirmed, these functional models are defined from first principles and simply reflect all observed functionality rather than assuming any implementation at all. Here we show that from the perspective of these functional models, the transition from animal intelligence to a human intelligence capable of a sufficient level of abstraction to develop science and other concepts, and capable of exchanging and accumulating the value of those abstractions to achieve exponentially greater impact on the external world, is a well-defined phase change [5]. The transition from human intelligence to GCI, the transition from GCI to second order GCI, and so forth to Nth order GCI are hypothesized to be subsequent phase changes that may or may not occur [5]. The functional modeling approach is used to clarify the fundamentally different nature of the general problem-solving ability provided by GCI as opposed to the problem solving ability of tools such as computation or computing methods [6] that can be applied to any general problem, and why even super computers without general problem-solving ability are limited to the problems their designers can define, and to the solutions those designers can envision [7]. This model suggests that entire categories of problems cannot reliably be solved without this phase change to General Collective Intelligence, and since this exponential increase in problem-solving ability applies to physics, mathematics, economics, health care, sustainable development, and every other field of human study where intelligence applies. In addition, since this model suggests that any exponential increase in ability to impact the external world possible through GCI cannot have been possible before at any time in human civilization, and since another such increase cannot be possible again until the advent of AGI or the transition to a second order GCI. the implications of GCI are profound [8].

Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Human-Centric Functional Modeling Perspective

2021 ◽  
Author(s):  
Andy E. Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Semantic Representation ◽  
Computing Methods ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Semantic Modeling ◽  
Problems And Solutions ◽  
Advanced Computing

Recent advances in modeling human cognition have resulted in what is suggested to be the first model of Artificial General Intelligence (AGI) with the potential capacity for human-like general problem-solving ability, as well as a model for a General Collective Intelligence or GCI, which has been described as software that organizes a group into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. Both this model for GCI and this model for AGI require functional modeling of concepts that is complete in terms of meaning being self-contained in the model and not requiring interpretation based on information outside the model. The combination of a model of cognition to define an interpretation of meaning, and this functional modeling technique to represent information that way together results in fully self-contained definitions of meaning that are suggested to be the first complete implementation of semantic modeling. With this semantic modeling, and with these models for AGI and GCI, cognitive computing and its capacity for general problem-solving ability become far better defined. However, semantic representation of problems and of the details of solutions, as well general problem-solving ability in navigating those problems and solutions is not required in all cases. This paper attempts to explore the cases in which it is, and how the various computing methods and advanced computing paradigms are best utilized in each case from the perspective of cognitive computing.

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