scholarly journals Human Intelligence and General Collective Intelligence as Phase Changes in Animal Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Phase Transition ◽  
Phase Transitions ◽  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Phase Changes ◽  
Human Intelligence ◽  
Animal Intelligence ◽  
Collective Cognition ◽  
Order Pattern

The hypothesis that human intelligence represents a phase transition in animal intelligence is explored, as is the hypothesis that General Collective Intelligence (GCI), which has been defined as a system that organizes groups into a single collective cognition with the potential for vastly greater general problem-solving ability than that of any individual in the group, represents a phase transition in human intelligence. At these phase transitions, cognition can be demonstrated to gain the capacity for exponentially greater general problem-solving ability. If valid, then when generalized as an Nth order pattern, these N phase transitions represent successively more powerful super-intelligences, where each of these super-intelligences can potentially be implemented as an Artificial General Intelligence (AGI), or as a General Collective Intelligence (GCI).

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].

scholarly journals Deducing the Properties Required by General Collective Intelligence Platforms

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
Phase Change ◽  
General Problem ◽  
Collective Intelligence ◽  
Second Order ◽  
Functional Modeling ◽  
Conceptual Space ◽  
Reasoning Process ◽  
Collective Cognition ◽  
The World

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.

scholarly journals General Collective Intelligence vs the Innate Collective Intelligence Factor

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
State Space ◽  
Functional State ◽  
General Problem ◽  
Collective Intelligence ◽  
Functional Modelling ◽  
Collective Cognition ◽  
Potential Capacity ◽  
Modelling Approach

INTRODUCTION: Groups of individuals of species exhibiting collective behaviours have been suggested to have some innate general collective intelligence. General Collective Intelligence or GCI has been described as a platform that organizes individual humans into a single collective intelligence with the potential capacity for exponentially greater general problem-solving ability.OBJECTIVES: To explore whether a functional modelling approach might have the capacity to represent any system of organization resulting in a general collective intelligence factor. And to explore what functionality might be required for a GCI to exponentially increase it.METHODS: An analysis of the meaning of general problem-solving ability in the functional state space of a system of cognition or collective cognition is used to assess whether GCI has the potential to exponentially increase increase that ability.RESULTS: GCI has the potential to exponentially increase increase impact on all general outcomes where limited by general problem-solving abilityCONCLUSION: While an innate general collective intelligence factor might exist, and while conventional CI solutions might have significant impact on specific collective outcomes, a GCI is required to exponentially general problem-solving ability, and therefore to exponentially increase collective outcomes. This capacity has the potential to be disruptive.

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 AI Safety and General Collective Intelligence

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
The Other ◽  
General Intelligence ◽  
Human Beings ◽  
Artificial General Intelligence ◽  
The Future ◽  
Ai Safety

Considering both current narrow AI, and any Artificial General Intelligence (AGI) that might be implemented in the future, there are two categories of ways such systems might be made safe for the human beings that interact with them. One category consists of mechanisms that are internal to the system, and the other category consists of mechanisms that are external to the system. In either case, the complexity of the behaviours that such systems might be capable of can rise to the point at which such measures cannot be reliably implemented. However, General Collective Intelligence or GCI can exponentially increase the general problem-solving ability of groups, and therefore their ability to manage complexity. This paper explores the specific cases in which AI or AGI safety cannot be reliably assured without GCI.

scholarly journals The Necessity of General Collective Intelligence Driven Processes in Achieving Pervasive Manufacturing

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
Competitive Advantage ◽  
General Problem ◽  
Collective Intelligence ◽  
Group Problem ◽  
Group Problem Solving

General Collective Intelligence has been defined as a system that orchestrates groups to cooperate as a single collective intelligence that greatly increases the group’s general problem-solving ability. This increase in group problem-solving ability applies to any group problem. It applies to manufacturing, where GCI has the potential to facilitate decentralized processes not possible otherwise. It applies to design, where GCI has the potential to reliably enable groups to create designs far too complex otherwise. And it applies to cooperation in general, where GCI has the potential to enable cooperation to be reliably scaled, so where the value of that cooperation is positive and can therefore subsidize the cooperation itself, that value might be increased to the point that it can reliably create powerful competitive advantage for groups of local businesses that cooperate to supply local demand through pervasive manufacturing. This paper explores why for these and other reasons, GCI is a necessary component to achieving pervasive use of pervasive manufacturing.

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 Applications for General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence

General Collective Intelligence or GCI has been described as a system that organizes groups into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. This paper explores examples of the classes of problems that might be solved with GCI.

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