Browsing by Author "Williams, Andy"

Now showing 1 - 20 of 63
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    A Mathematical Model for Identifying Truth in Observations Made within Individual Human Self-Awareness
    (2020-04-27) Williams, Andy
    A large body of work purports to identify the functions of the human system through observations that can be validated within human self-awareness. That is, using observations that can be made in the human self-awareness to validate what in this paper are called human-centric models of the functionality of the human system, or human-centric functional models. This body of work, accumulated over thousands of years, has been largely inaccessible to the scientific study of consciousness and other functions because it's written in terms that require a very different training, which is training in observing one's self-awareness. Making scientific or mathematical use of observations made within human self-awareness is a challenge. What can be observed to be true in one's self-awareness, and what can be proved true mathematically, have so far been two different things. Representing an experiment as a channel through which information representing truth can be transmitted, this paper explores the use of Information Theory to gauge the capacity of an experiment in self-awareness to identify a truth, such as determining the validity of any given function defined by a functional model for consciousness. In providing this mechanism to prove that an awareness exists and that it reflects truth, this paper attempts to make the entire set of observations made using human self-awareness accessible to mathematicians and scientists.
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    A Model for Artificial General Intelligence
    (2020-04-17) Williams, Andy
    A Functional Modeling Framework (FMF) for defining and comparing models of consciousness and cognition has recently been developed. This framework proposes to have the capacity to represent the complete set of the functionality of human consciousness and cognition, which if true, would suggest that all models of consciousness and cognition can be represented within the framework. The framework also proposes to define the criteria for a model of cognition to have the potential for the general problem solving ability commonly recognized as true human intelligence. The FMF provides a single framework for defining models of consciousness and cognition that is human-centric in that the functions can be validated through experiments that can be performed within innate human self-awareness rather than being dependent on assumptions made by any specific model. This human-centric functional modeling approach is intended to enable different models of AGI to be more easily compared so research can reliably converge on a single understanding, enabling the possibility of massively collaborative interdisciplinary projects to research, and implement models of consciousness or cognition where such massive collaboration has not proved possible before. The FMF defines requirements for all the functional components defined by the framework, but leaves specific models to define their own implementations. This paper summarizes a model of cognition developed within this framework that is proposed to meet the criteria of an AGI as defined within this framework. This description is expanded in a number of other papers.
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    A Model for General Collective Intelligence
    (2020-04-30) Williams, Andy
    A General Collective Intelligence or GCI is a hypothetical platform that combines groups into a virtual collective cognition with a single well-defined thread of collective reasoning. While groups might have some innate general problem-solving ability described by a general collective intelligence factor (c), any collective intelligence software platform or methodology used as a decision-making tool by groups to increase problem-solving ability has narrow problem-solving ability where it is not able to address any problem in general. As opposed to such collective intelligence methodologies or software platforms, GCI has the potential to combine individual cognition into a single virtual collective cognition with general problem-solving ability, and also creates the potential to exponentially increase this general problem-solving ability.
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    AI Safety and General Collective Intelligence
    (2020-12-31) Williams, Andy
    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.
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    Applications for General Collective Intelligence
    (2020-08-30) Williams, Andy
    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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    Approximating an Artificial General Intelligence or a General Collective Intelligence
    (2021-01-06) Williams, Andy
    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.
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    Are Mathematical Relationships Represented by Geometries in Some Functional State Space?
    (2022-02-01) Williams, Andy
    Human-Centric Functional Modeling is considered in this paper to be a branch of network theory which hypothesizes that all systems can be represented as having a set of functions through which the system might transition from one functional state to another. These states are referred to as “functional states” because they are described solely in terms of the functions available to transition to adjacent states, rather than being described in terms of any entities that might implement those functions. All possible behaviors of the system are then hypothesized to be described by a graph containing a network of nodes representing such functional states, where those nodes are connected by edges representing the processes through which those functional states might transition between each other. Modeling human cognition as moving through a “space of concepts” or “conceptual space” that forms the functional state space of the cognitive system and that has the capacity to represent all possible concepts and reasoning, it has been hypothesized that all information and therefore all mathematical proofs and other logic can be represented in this space. Modeling human conscious self-awareness as moving through an “awareness space” or “space of awareness's” that represents the functional state space of the consciousness, it has been hypothesized that awareness of any mathematical or other truth, whether or not yet identified or proven, can be represented as a potential awareness in that space. This paper outlines the hypotheses that in the limit of infinite ability to navigate logic through understanding or reasoning processes (infinite intelligence) mathematical logic is represented by a geometry in conceptual space, and that in the limit of infinite ability to be aware of truth (infinite awareness), mathematical truth is represented as a geometry in awareness space. This paper also outlines some of the questions that must be answered in order to validate these hypotheses.
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    Assessing the Potential Impact of General Collective Intelligence
    (2020-08-25) Williams, Andy
    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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    Automating the Process of Generalization
    (2022-03-11) Williams, Andy
    As various scholars have noted, the behavior of potentially any system can be represented in terms of network graphs called state spaces. Assuming that machine learning solutions can be represented as the automation of cognitive processes, it is noteworthy that in the case of the cognitive system it has been hypothesized that all thoughts (concepts and the reasoning that connect them) can be represented in terms of one proposed state space. In the case of the physical world up to and including the entire physical universe it is hypothesized that systems can be represented in terms of another proposed state space. Biological and other systems have also been hypothesized to be representable this way. Through simple, easily verifiable geometrical arguments, this paper explores how general problem-solving ability relates to the volume of this cognitive state space an intelligent system is able to navigate, or the volume it can navigate in the state space of any system the intelligent system might be targeted at solving problems about. Through those same simple geometrical arguments in state space this article explores why modeling problems in state space, in addition to increasing the ability to generalize until it spans the entire state space, is predicted to lead to an exponential increase in general problem-solving ability for intelligent systems, and this paper explores the patterns through which this increase in ability to generalize have been automated so that they can be more broadly replicated to increase narrow problem-solving ability in a sufficient range of areas for an exponential increase in general problem-solving ability to be achieved.
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    Bridging the Left - Right Political Divide with Artificial Intelligence and Collective Intelligence
    (2020-11-17) Williams, Andy
    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.
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    Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Functional Modeling Perspective
    (2020-08-30) Williams, Andy
    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.
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    Comment in Reply to "On the complexity of extending the convergence region for Traub’s method"
    (2021-10-28) Williams, Andy
    This comment is in reply to the paper “On the complexity of extending the convergence region for Traub’s method” [1]. In complexity science, from the mathematical perspective, discussions of complexity often concern algorithmic complexity such as in the paper responded to here [2]. But this is only one of the kinds of complexity that exists even in the mathematical domain. There is also the complexity in the behavior of a system of equations; there is the complexity of the reasoning or algorithm required to understand a system of equations (“understand” interpreted here as defining the problem needing to be solved); and, as mentioned, there is the complexity of the reasoning or algorithm required to solve a system of equations.
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    Deducing the Properties Required by General Collective Intelligence Platforms
    (2021-01-23) Williams, Andy
    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.
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    Defining a General Collective Intelligence Based Renewable Energy Solution Development Program
    (2020-12-31) Williams, Andy
    The choice of which problem renewable energy programs target is one that might benefit from greater General Collective Intelligence or GCI. Where a collective intelligence uses the intelligence of crowds to maximize impact on a given problem, a GCI is defined as having the capacity for general problem solving ability, and therefore the capacity to increase outcomes by choosing the optimal problem to solve. Collectively intelligent development aims to solve the problems required to create the ability for groups to reliably explore all of the currently possible solution space and to reliably converge on the best available solution in that space, so that developing solutions which facilitate a significantly improved impact on targeted outcomes as compared to other development processes is reliably achievable. Where conventional development processes have known cognitive or other biases that may prevent certain categories of solutions from being selected by groups even where optimal, collectively intelligent development aims to create the capacity to reliably converge on the optimal overall solution. A proposed Collective Intelligence Based Renewable Energy Program aims to leverage human-centric functional modeling to provide groups with a common model of the problem being solved. This proposed program then leverages a newly developed model of General Collective Intelligence to collectively reason in terms of those common functional models as required to develop a solution that optimizes impact on the targeted problem, such as the problem of achieving a significantly lower cost of access to sustainable renewable energy, or achieving a significant increase in environmental sustainability of that renewable energy, as defined by metrics that might be related to carbon emissions.
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    Defining and Quantifying an Exponential Increase in General Problem-Solving Ability Within Groups
    (2022-02-22) Williams, Andy
    The concept of collective super-intelligence has been described by various authors in the field of collective intelligence, some of whom have estimated the qualitative properties of a collective super-intelligence and have attempted to determine the types of problems that a collective super-intelligence might excel at. This work departs from those opinions in leveraging a Human-Centric Functional Modeling approach able to represent the behaviour of complex human-observable systems like individual or collective cognition. With this approach, for the first time models have been developed that allow the impacts of collective intelligence to potentially be predicted or even simulated with far greater accuracy and understood more reliably. This article explores how a collective super-intelligence might be defined and quantified in terms of the general collective intelligence factor (c), and also explores how such a collective super-intelligence might be expected to differentiate itself from current forms of collective intelligence.
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    Defining Functional Models of Artificial Intelligence Solutions to Create a Library that an Artificial General Intelligence can use to Increase General Problem Solving Ability
    (2020-04-27) Williams, Andy
    The AI industry continues to enjoy robust growth. With the growing number of AI algorithms, the question becomes how to leverage all these models intelligently in a way that reliably converges on AGI. One approach is to gather all these models ingo a single library that a system of artificial intelligence might use to increase it's general problem solving ability. This paper explores the requirements for building such a library, the requirements for that library to be searchable for AI algorithms that might have the capacity to significantly increase impact on any given problem, and the requirements for the use of that library to reliably converge on AGI. This paper also explores the importance to such an effort of defining a common set of semantic functional building blocks that AI models can be represented in terms of. In particular, how that functional decomposition might be used to organize large scale cooperation to create such an AI library, where that cooperation has not yet proved possible otherwise. And how such collaboration, as well as how such a library, might significantly increase the impact of each AI and AGI researcher’s work.
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    Defining Functional Models of Collective Intelligence Solutions to Create a Library a General Collective Intelligence can use to Increase General Problem Solving Ability
    (2020-08-20) Williams, Andy
    With the great and growing number of collective intelligence models and algorithms to implement those models, the task of developing a single understanding of which model is optimal may steadily become more and more untractable. However, rather than competing to determine which model is best, a more productive approach might be cooperating to create a collective repository to store information about how each model performs in each context. This paper proposes a methodology for defining functional models of CI solutions, so those CI solutions might be added as functions to a library that a General Collective Intelligence might use to increase its general problem solving ability. Utilizing such a library might require information to be stored about which inputs, targeted outputs, and contexts of execution in which each solution or given category of solution might be optimal. Functional modeling of collective intelligence solutions and the context in which they operate facilitates this.
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    Defining the Genome and Gametes of a General Collective Intelligence Based Smart City
    (2020-12-24) Williams, Andy
    Applying General Collective Intelligence or GCI creates the potential to greatly increase the complexity of problems that might be navigated through collective reasoning processes, and to greatly increase capacity for cooperation in so that collective reasoning might be executed at much greater speed and scale. GCI also aligns cooperation so those outcomes can be sustained. Applying GCI to design processes creates the potential for designs for products and services, as well as manufacturing methods for products and delivery methods for services, that are coherent on a scale that might not have been considered before. Where smart city initiatives look to introduce sustainability into housing and city design, GCI based design aims to gain the capacity to self-assemble projects in a way that optimizes sustainability across all initiatives. This paper explores how the full implementation of GCI in design defines a genome with the capacity to store collectively optimized processes, and how the full implementation of GCI in manufacturing and construction defines gametes with the capacity to be collectively optimized in this way.
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    Discovering and Implementing Self-Sustaining Networks of Cooperation with General Collective Intelligence
    (2020-12-15) Williams, Andy
    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.
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    Does Creating an Artificial General Intelligence Require General Collective Intelligence in Order to be Reliably Achievable?
    (2020-08-17) Williams, Andy
    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.