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 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 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 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 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 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 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Large Scale ◽  
Building Blocks ◽  
General Intelligence ◽  
Functional Decomposition ◽  
Artificial General Intelligence ◽  
Functional Models ◽  
The Impact

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.

scholarly journals The Case for General Collective Intelligence Rather than Artificial General Intelligence being the Most Important Human Innovation in the History and Future of Mankind

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Economic Impact ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
General Intelligence ◽  
Human Beings ◽  
Artificial General Intelligence ◽  
Proposed Model ◽  
History Of ◽  
Hidden Patterns ◽  
Entire Planet

Artificial General Intelligence, that is an Artificial Intelligence with the ability to redesign itself and other technology on its own, has been called “mankind’s last invention”, since it may not only remove the necessity of any human invention afterwards, but also might design solutions far too complex for human beings to have the ability to contribute to in any case. Because of this, if and when AGI is ever invented, it has been argued by many that it will be the most important innovation in the history of the mankind up to that point. Just as nature’s invention of human intelligence might have transformed the entire planet and generated a greater economic impact than any other innovation in the history of the planet, AGI has been suggested to have the potential for an economic impact larger than that resulting from any other innovation in the history of mankind. This paper explores the case for General Collective Intelligence being a far more important innovation than AGI. General Collective Intelligence has been defined as a solution with the capacity to organize groups of human or artificial intelligences into a single collective intelligence with vastly greater general problem solving ability. A recently proposed model of GCI not only outlines a model for cognition that might also enable AGI, but also identifies hidden patterns in collective outcomes for groups that might make GCI necessary in order to reliably achieve the benefits of AGI while reliably avoiding the potentially catastrophic costs of AGI.

scholarly journals Individualization of Products and Services with Artificial General Intelligence and General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Online Education ◽  
News Media ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Online News ◽  
General Intelligence ◽  
Entertainment Media ◽  
Individual User ◽  
Artificial General Intelligence ◽  
The Individual

INTRODUCTION: With advances in big data techniques having already led to search results and advertising being customized to the individual user, the concept of an online education designed solely for an individual, or the concept of online news or entertainment media, or any other virtual service being designed uniquely for each individual, no longer seems as far fetched. However, designing services that maximize user outcomes as opposed to services that maximize outcomes for the corporation owning them, requires modeling user processes and the outcomes they target.OBJECTIVES: To explore the use of Human-Centric Functional Modeling (HCFM) to define functional state spaces within which human processes are well-defined paths, and within which products and services solve specific navigation problems, so that by considering all of any given individual’s desired paths through a given state space, it is possible to automate the customization of those products and services for that individual or to groups of individuals.METHODS: An analysis is performed to assess how and whether intelligent agents based on some subset of functionality required for Artificial General Intelligence (AGI) might be used to optimize for the individual user. And an analysis is performed to determine whether and if so how General Collective Intelligence (GCI) might be used to optimize across all users.RESULTS: AGI and GCI create the possibility to individualize products and services, even shared services such as the Internet, or news services so that every individual sees a different version.CONCLUSION: The conceptual example of customizing a news media website for two individual users of opposite political persuasions suggests that while the overhead of customizing such services might potentially result in massively increased storage and processing overhead, within a network of cooperating services in which this customization reliably creates value, this is potentially a significant opportunity.

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 Artificial General Intelligence: A New Perspective, with Application to Scientific Discovery

2019 ◽  
Author(s):  
A. M. Khalili
Keyword(s):  
Problem Solving ◽  
Scientific Discovery ◽  
General Intelligence ◽  
Discovery Process ◽  
Artificial General Intelligence ◽  
Alternative Perspective ◽  
New Knowledge ◽  
New Perspective ◽  
High Level ◽  
Open The Doors

The dream of building machines that have human-level intelligence has inspired scientists for decades. Remarkable advances have been made recently; however, we are still far from achieving this goal. In this paper, I propose an alternative perspective on how these machines might be built focusing on the scientific discovery process which represents one of our highest abilities that requires a high level of reasoning and remarkable problem-solving ability. By trying to replicate the procedures followed by many scientists, the basic idea of the proposed approach is to use a set of principles to solve problems and discover new knowledge. These principles are extracted from different historical examples of scientific discoveries. Building machines that fully incorporate these principles in an automated way might open the doors for many advancements.

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