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 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.

Entertainment Film and TV Portrayals of Climate Change and Their Societal Impacts

2017 ◽  
Author(s):  
Anthony Dudo ◽  
Jacob Copple ◽  
Lucy Atkinson
Keyword(s):  
Climate Change ◽  
News Media ◽  
Future Research ◽  
Small Subset ◽  
Long Term Effects ◽  
Comprehensive Overview ◽  
Entertainment Media ◽  
Individual Level ◽  
Social Scientific ◽  
The Individual

Although there is an abundance of social scientific research focused on public opinion and climate change, there remains much to learn about how individuals come to understand, feel, and behave relative to this issue. Efforts to understand these processes are commonly directed toward media depictions, because media represent a primary conduit through which people encounter information about climate change. The majority of research in this area has focused on news media portrayals of climate change. News media depictions, however, represent only a part of the media landscape, and a relatively small but growing body of work has focused on examining portrayals of climate change in entertainment media (i.e., films, television programs, etc.) and their implications. This article provides a comprehensive overview of this area of research, summarizing what is currently known about portrayals of climate change in entertainment media, the individual-level effects of these portrayals, and areas ripe for future research. Our overview suggests that the extant work has centered primarily on a small subset of high-profile climate change films. Examination of the content of these films has been mostly rhetorical and has often presumed negative audience effects. Studies that specifically set out to explore possible effects have often unearthed evidence suggesting short-term contributions to viewers’ perceptions of climate change, specifically in terms of heightened awareness, concern, and motivation. Improving the breadth and depth of research in this area, we contend, can stem from more robust theorizing, analyses that focus on a more diverse menu of entertainment media and the interactions among them, and increasingly complex analytical efforts to capture long-term effects.

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 Visualizing narrative patterns in online news media

2019 ◽  
Vol 79 (1-2) ◽  
pp. 919-946 ◽  
Author(s):  
Laura Koivunen-Niemi ◽  
Masood Masoodian
Keyword(s):  
News Media ◽  
Media Coverage ◽  
User Study ◽  
Online News ◽  
Illustrative Case ◽  
Narrative Patterns ◽  
Illustrative Case Study ◽  
The Individual ◽  
Over Time

Abstract News media play an important role in shaping social reality, and their multimedia narrative content, in particular, can have widespread repercussions in the public’s perception of past and present phenomena. Being able to visually track changes in media coverage over time could offer the potential for aiding social change, as well as furthering accountability in journalism. In this paper, we explore how visualizations could be used to examine differences in online media narrative patterns over time and across publications. While there are existing means of visualizing such narrative patterns over time, few address the aspect of co-occurrence of variables in media content. Comparing co-occurrences of variables chronologically can be more useful in identifying patterns and possible biases in media coverage than simply counting the individual occurrences of those variables independently. Here, we present a visualization, called time-sets, which has been designed to support temporal comparisons of such co-occurrences. We also describe an interactive prototype tool we have developed based on time-sets for analysis of multimedia news datasets, using an illustrative case study of news articles published on three online sources over several years. We then report on a user study we have conducted to evaluate the time-sets visualization, and discuss its findings.

scholarly journals OODA-RL: A REINFORCEMENT LEARNING FRAMEWORK FOR ARTIFICIAL GENERAL INTELLIGENCE TO SOLVE OPEN WORLD NOVELTY

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

scholarly journals What to expect when you’re expecting robots: Futures, expectations, and pseudo-artificial general intelligence in UK news

Journalism ◽  
2020 ◽  
pp. 146488492094753
Author(s):  
J Scott Brennen ◽  
Philip N Howard ◽  
Rasmus K Nielsen
Keyword(s):  
Artificial Intelligence ◽  
Social Integration ◽  
Critical Discourse Analysis ◽  
News Media ◽  
Critical Discourse ◽  
General Intelligence ◽  
Journalism Studies ◽  
Artificial General Intelligence ◽  
Sociology Of Expectations ◽  
The World

Drawing on scholarship in journalism studies and the sociology of expectations, this article demonstrates how news media shape, mediate, and amplify expectations surrounding artificial intelligence in ways that influence their potential to intervene in the world. Through a critical discourse analysis of news content, this article describes and interrogates the persistent expectation concerning the widescale social integration of AI-related approaches and technologies. In doing so, it identifies two techniques through which news outlets mediate future-oriented expectations surrounding AI: choosing sources and offering comparisons. Finally, it demonstrates how in employing these techniques, outlets construct the expectation of a pseudo-artificial general intelligence: a collective of technologies capable of solving nearly any problem.

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 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.

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