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 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 Approximating an Artificial General Intelligence or a General Collective Intelligence

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
Complex Systems ◽  
General Problem ◽  
Collective Intelligence ◽  
Complex Problem ◽  
General Intelligence ◽  
Functional Modeling ◽  
Artificial General Intelligence ◽  
Over Time

Human-Centric Functional Modeling (HCFM) has recently been used to define a model of Artificial General Intelligence (AGI) believed to have the capacity for human-like general problem-solving ability (intelligence), as well as a model of General Collective Intelligence (GCI) with the potential to combine individuals into a single collective intelligence that might have exponentially greater general problem-solving ability than any individual in the group. Functional modeling decouples the components of complex systems like cognition through well-defined interfaces so that they can be implemented separately, thereby breaking down the complex problem of implementing such a system into a number of much simpler problems. This paper explores how a rudimentary AGI and a rudimentary GCI might be implemented through approximating the functions of each, in order to create systems that provide sufficient value to incentivize more sophisticated implementations to be developed over time.

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 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 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 General Problem-Solving Ability in Natural Systems as a Model for Solving Problems from Sustainable and Self-Financing Development in Africa to Artificial General Intelligence

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Sustainable Development ◽  
Problem Solving ◽  
General Problem ◽  
Tree Seedlings ◽  
General Intelligence ◽  
The Sustainable Development ◽  
Natural Systems ◽  
Artificial General Intelligence ◽  
Single Leaf ◽  
Wide Range

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.

scholarly journals Human-Centric Functional Modeling and Evolutionary Biology

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Climate Change ◽  
Problem Solving ◽  
General Problem ◽  
Biological System ◽  
Evolutionary Biology ◽  
Functional Modeling ◽  
State Spaces ◽  
Wide Range ◽  
Definition Of ◽  
Biological Organisms

The newly emerging science of Human-Centric Functional Modeling provides an approach towards modeling biological and other systems that is hypothesized to maximize human capacity to understand and navigate complexity in those systems. This paper provide an overview exploring how Human-Centric Functional Modeling might be applied in evolutionary biology, and how this increase in capacity to understand the complexity that organisms have evolved into might be achieved. The broader usefulness of Human-Centric Functional Modeling is that it provides a simple mathematical definition of what constitutes a biological system, defines the problem-solving domain of any biological system in terms of abstract mathematical spaces, and provides an expression defining general problem-solving ability in any such domain. This enables it to be seen that all systems with general problem-solving ability in their own domain are potentially an abstraction of a single mathematical pattern of adaptive problem-solving that might apply to all domains. From this perspective nature has already potentially solved problems in biological organisms that can be represented in some abstract functional state spaces as the same general problem that must be solved to address problems in a wide range of other systems, including existential challenges from poverty to climate change, where Human-Centric Functional Modeling enables it to be seen that not only can nature’s solutions be copied, but that nature has demonstrated its solutions to have worked for hundreds of millions of years.

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.

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