scholarly journals Functional Modeling and General Collective Intelligence as the Basis for Pervasive Healthcare

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Intelligent Agents ◽  
Collective Intelligence ◽  
Health And Wellness ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Pervasive Healthcare ◽  
Computing Platforms ◽  
Definition Of ◽  
Universal Mechanism ◽  
Gathering Data

General Collective Intelligence has the potential to combine individuals into a single collective collective intelligence with general problem-solving ability (intelligence) that might be exponentially greater than that of any individual. In every software domain, including health and wellness, General Collective Intelligence and functional modeling have the potential to enable the definition of pervasive cognitive computing applications and platforms. In such cognitive apps, intelligent agents might provide services to the user that optimize their outcomes by independently executing functional operations in each software domain on whatever software best implements those operations, and independently incorporating any possible data available to the user in the best way available. And at the same time in such cognitive computing platforms, a GCI might orchestrate the process of gathering data from all such individual uses in order to optimize collective outcomes such as significantly increasing healthcare and wellness. And these models of individual and collective cognition suggest that such optimization might not be reliably achievable otherwise. For both of these cognitive computing approaches functional modeling is required to provide a universal mechanism for representing data and processes. Therefore, to achieve significantly increased healthcare and wellness outcomes both functional modeling and GCI might be required. Functional modeling has the potential to overcome the lack of consistency in type and format of data gathered and the lack of a mechanism for universally comparing and combining that data. This paper explores why functional modeling might not only be of critical importance to pervasive healthcare, but why it also might be critical to significantly improving capacity to diagnose and to make interventions.

scholarly journals The Peer to Peer Social Fabric as a Platform for General Collective Intelligence

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Intelligent Agents ◽  
Collective Intelligence ◽  
Peer To Peer ◽  
Cognitive Computing ◽  
Social Fabric ◽  
Exponential Increase ◽  
Computing Platforms ◽  
High Level

INTRODUCTION: General Collective Intelligence or GCI has been defined as a platform that combines individuals into a collective intelligence with the potential for exponentially greater general problem-solving ability (intelligence) than that of any individual in the group. Cognitive computing applications are executed by intelligent agents on a user’s behalf to optimize individual user outcomes. Cognitive computing platforms are executed by individuals organized by a GCI to achieve greater collective outcomes. The Peer to Peer Social Fabric (P2PSF) is an infrastructure platform proposed to enable the execution of cognitive computing applications or platforms.OBJECTIVES: To explore the functionality required by an infrastructure platform with the capacity to enable the operation of cognitive computing applications or platforms. And to determine whether the functionality of the proposed Peer to Peer Social Fabric is sufficient.METHODS: The requirements of collective cognitive computing were assessed, including the requirement to increase capacity for complexity, capacity to scale number of processes, and capacity to sustain processes. The proposed high-level specifications of the Peer to Peer Social Fabric were compared to those requirements to determine whether that functionality is sufficient.RESULTS: The proposed Peer to Peer Social Fabric appears to meet the requirements of both the operating system for GCI, as well as the requirements of a client enabling individuals and organizations to access either AGI or GCI functionality.CONCLUSION: All of the functionality required by he Peer to Peer Social Fabric might already exist, but an understanding of how that functionality must be combined in order to achieve the exponential increase in general problem-solving ability potentially possible through AGI or GCI is a new and important contribution.

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 Towards Cognitive Communication and Other Applications of Cognitive Computing

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Communication Networks ◽  
Intelligent Agents ◽  
Communication Systems ◽  
Collective Intelligence ◽  
Cognitive Computing ◽  
Cognitive Communications ◽  
Individual Outcomes ◽  
Collective Cognition ◽  
Cognitive Power ◽  
Individual Cognition

The term cognitive communications has been used to describe “human-centric” communication systems that adapt to different behaviors, expectations and preferences. This paper explores a more general use of the term by attempting to enumerate all communication functions that might benefit through being executed by systems of individual or collective cognition. Systems of individual cognition might be represented by intelligent agents (based on some subset of the functionality suggested to be required for Artificial General Intelligence) with the capacity to change any property of communication. Communication functions executed by such systems optimize individual outcomes. Systems of collective cognition might be represented by collective intelligence solutions (based on some subset of functionality suggested to be required for General Collective Intelligence) with the capacity to enable such intelligent agents to self-assemble into communication networks using any combination of network topology, protocols, spectrum or other properties. Communication functions executed by such systems optimize collective outcomes. From this perspective, cognitive communication is explored as a specific case that might be generalized to apply to any number of other sectors, such as cognitive power generation and distribution, cognitive agriculture, cognitive healthcare, etc.

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 Collective Intelligence and the Cloud

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Cloud Computing ◽  
Collective Intelligence ◽  
Cloud Service ◽  
Cloud Services ◽  
Cognitive Computing ◽  
Computing Platform ◽  
On Demand ◽  
Exponential Increase ◽  
Functional Models ◽  
Computing Platforms

The resources that can be made available on-demand through cloud computing are continually increasing. One potential addition is General Collective Intelligence or GCI, which has been defined as a platform that combines individuals into a single intelligence with the potential for exponentially greater general problem-solving ability (intelligence) than any individual. The concept of a cognitive computing platform involves leveraging GCI to orchestrate cooperation between any entities that are required in order to create the capacity to maximize any collective outcome targeted. Rather than executing programming code, a cognitive computing platform must execute functional models in which each of the functional operations composing that model is implemented in some programming language. All services that run on the cloud, as well as the cloud itself, can potentially be offered as cognitive computing platforms. Where current cloud computing limits customers to a particular cloud service vendor, cloud computing as a cognitive computing platform has the potential to completely decouple users from any such dependencies, while at the same time creating the potential for an exponential increase in demand for cloud services from those vendors that participate by decoupling their services in this way.

Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Human-Centric Functional Modeling Perspective

2021 ◽  
Author(s):  
Andy E. Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Semantic Representation ◽  
Computing Methods ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Semantic Modeling ◽  
Problems And Solutions ◽  
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. The combination of a model of cognition to define an interpretation of meaning, and this functional modeling technique to represent information that way together results 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 and its capacity for general problem-solving ability become far better defined. However, semantic representation of problems and of the details of solutions, as well general problem-solving ability in navigating those problems and solutions is not required in all cases. This paper attempts to explore the cases in which it is, and how the various computing methods and advanced computing paradigms are best utilized in each case from the perspective of cognitive computing.

Creating and Capturing Value through Crowdsourcing

2018 ◽  
Keyword(s):  
Information Systems ◽  
Systems Biology ◽  
Computer Science ◽  
Open Innovation ◽  
Product Innovation ◽  
Collective Intelligence ◽  
Innovation Management ◽  
Private And Public ◽  
Definition Of ◽  
The Uk

Examples of the value that can be created and captured through crowdsourcing go back to at least 1714, when the UK used crowdsourcing to solve the Longitude Problem, obtaining a solution that would enable the UK to become the dominant maritime force of its time. Today, Wikipedia uses crowds to provide entries for the world’s largest and free encyclopedia. Partly fueled by the value that can be created and captured through crowdsourcing, interest in researching the phenomenon has been remarkable. For example, the Best Paper Awards in 2012 for a record-setting three journals—the Academy of Management Review, Journal of Product Innovation Management, and Academy of Management Perspectives—were about crowdsourcing. In spite of the interest in crowdsourcing—or perhaps because of it—research on the phenomenon has been conducted in different research silos within the fields of management (from strategy to finance to operations to information systems), biology, communications, computer science, economics, political science, among others. In these silos, crowdsourcing takes names such as broadcast search, innovation tournaments, crowdfunding, community innovation, distributed innovation, collective intelligence, open source, crowdpower, and even open innovation. The book aims to assemble papers from as many of these silos as possible since the ultimate potential of crowdsourcing research is likely to be attained only by bridging them. The papers provide a systematic overview of the research on crowdsourcing from different fields based on a more encompassing definition of the concept, its difference for innovation, and its value for both the private and public sectors.

scholarly journals Good practices in collecting umbilical cord and placental blood

2016 ◽  
Vol 24 (0) ◽  
Author(s):  
Lauren Auer Lopes ◽  
Elizabeth Bernardino ◽  
Karla Crozeta ◽  
Paulo Ricardo Bittencourt Guimarães
Keyword(s):  
Best Practices ◽  
Umbilical Cord ◽  
Management Tool ◽  
Blood Collection ◽  
Placental Blood ◽  
Operational Factors ◽  
Blood Specimens ◽  
Definition Of ◽  
Gathering Data

Abstract Objective: to identify the factors related to the quality of umbilical cord and placental blood specimens, and define best practices for their collection in a government bank of umbilical cord and placental blood. Method: this was a descriptive study, quantitative approach, performed at a government umbilical cord and placental blood bank, in two steps: 1) verification of the obstetric, neonatal and operational factors, using a specific tool for gathering data as non-participant observers; 2) definition of best practices by grouping non-conformities observed before, during and after blood collection. The data was analyzed using descriptive statistics and the following statistical software: Statistica(r) and R(r). Results: while there was a correlation with obstetrical and neonatal factors, there was a larger correlation with operational factors, resulting in the need to adjust the professional practices of the nursing staff and obstetrical team involved in collecting this type of blood. Based on these non-conformities we defined best practices for nurses before, during and after blood collection. Conclusion: the best practices defined in this study are an important management tool for the work of nurses in obtaining blood specimens of high cell quality.

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.

The Maze and the Masses

2012 ◽  
Author(s):  
Hélène Landemore
Keyword(s):  
Decision Procedure ◽  
Collective Intelligence ◽  
Thought Experiment ◽  
Collective Decision ◽  
The People ◽  
Collective Decisions ◽  
Definition Of ◽  
The Masses

This chapter argues that collective intelligence offers an attractive solution to the problem of the average citizen's ignorance and irrationality. It first illustrates this point by presenting the metaphor of the maze, inspired by Descartes' thought experiment in the Discourse on Method. Next, the chapter sets out the definition of “democracy,” which gains a certain meaning and relevance within the context of this book—as, primarily, an inclusive collective decision procedure, that is, a procedure for collective decisions characterized by the fact that it is inclusive, more or less directly, of all the members of the group for whom decisions need to be made. The chapter then considers the domain of democratic reason and politics, before turning to the concept of democratic reason as the collective intelligence of the people. Finally, the chapter closes with a brief overview of the following chapters.

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