scholarly journals Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 198
Author(s):  
Stephen Fox
Keyword(s):  
Artificial Intelligence ◽  
Quality Management ◽  
Social Organization ◽  
Generative Models ◽  
Industrial Engineering ◽  
Prediction Errors ◽  
Active Inference ◽  
For Profit ◽  
Different Types ◽  
Inference Theory

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

scholarly journals Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1155
Author(s):  
Stephen Fox
Keyword(s):  
Stress Management ◽  
Life Histories ◽  
Internal State ◽  
Generative Models ◽  
Calibration Model ◽  
Active Inference ◽  
Ecological Fitness ◽  
Adaptive Calibration ◽  
Stress Responsivity ◽  
Inference Theory

In this paper, the Adaptive Calibration Model (ACM) and Active Inference Theory (AIT) are related to future-proofing startups. ACM encompasses the allocation of energy by the stress response system to alternative options for action, depending upon individuals’ life histories and changing external contexts. More broadly, within AIT, it is posited that humans survive by taking action to align their internal generative models with sensory inputs from external states. The first contribution of the paper is to address the need for future-proofing methods for startups by providing eight stress management principles based on ACM and AIT. Future-proofing methods are needed because, typically, nine out of ten startups do not survive. A second contribution is to relate ACM and AIT to startup life cycle stages. The third contribution is to provide practical examples that show the broader relevance ACM and AIT to organizational practice. These contributions go beyond previous literature concerned with entrepreneurial stress and organizational stress. In particular, rather than focusing on particular stressors, this paper is focused on the recalibrating/updating of startups’ stress responsivity patterns in relation to changes in the internal state of the startup and/or changes in the external state. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action and ecological fitness to human organizations.

scholarly journals PID Control as a Process of Active Inference with Linear Generative Models

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 257 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Prediction Errors ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.

scholarly journals Predictive eye movements are adjusted in a Bayes-optimal fashion in response to unexpectedly changing environmental probabilities

2021 ◽  
Author(s):  
David Harris ◽  
Tom Arthur
Keyword(s):  
Markov Models ◽  
Sensory Information ◽  
Generative Models ◽  
Prediction Errors ◽  
Active Inference ◽  
Gaze Patterns ◽  
Future Prediction ◽  
Visuomotor Tasks ◽  
Partially Observable ◽  
Optimal Behaviour

This paper examines the application of active inference to naturalistic visuomotor control. Active inference proposes that actions serve to minimise future prediction errors and are dynamically adjusted according to uncertainty about sensory information, predictions, or the environment. We investigated whether predictive gaze behaviours are indeed adjusted in this Bayes-optimal fashion during a virtual racquetball task. In this task, participants intercepted bouncing balls with varying levels of elasticity, under conditions of high and low environmental volatility. Participants’ gaze patterns differed between stable and volatile conditions in a manner consistent with generative models of Bayes-optimal behaviour. Partially observable Markov models also revealed an increased rate of associative learning in response to unpredictable shifts in environmental probabilities, although there was no overall effect of volatility on this parameter. Findings extend active inference frameworks into complex and unconstrained visuomotor tasks and present important implications for a neurocomputational understanding of the visual guidance of action.

scholarly journals The Transnationalization of Labor Mobility: Development Trends and Selected Challenges Involved in Its Regulation

2017 ◽  
Vol 9 (2) ◽  
pp. 115
Author(s):  
Ludger Pries ◽  
Martina Maletzky
Keyword(s):  
Labor Mobility ◽  
Ideal Types ◽  
Non Profit ◽  
Labor Conditions ◽  
For Profit ◽  
Cross Border ◽  
Mass Migration ◽  
Different Types ◽  
Term Labor

Internationalization of value chains and of for-profit as well as non-profit organizations, and as a result of cheaper and safer mass migration, transnational labor mobility is of increasing importance. The article presents the development of the different types of cross-border labor mobility (from long-term labor migration over expatriats/inpatriats up to business traveling); it analyses crucial aspects of labor conditions and how the collective regulation of working, employment and participation conditions in general is affected: could local or national forms of labor regulation cope with these new conditions? What are the main challenges when it comes to collective bargaining and the monitoring of labor conditions? The article is based on a three year international and comparative research in Germany and Mexico. First, different ideal types of transnational labor mobility are distinguished that have emerged as a result of increasing cross-border labor mobility. Then potential sources of labor related social inequality and challenges in the regulation of the working, employment and participation conditions for transnational workers are discussed. Finally, some conclusions are drawn for further research.

scholarly journals Five Case Studies of Governance in Tertiary Education

2016 ◽  
Vol 46 (3) ◽  
pp. 42-58
Author(s):  
Daniel Wallace Lang
Keyword(s):  
Tertiary Education ◽  
Colleges And Universities ◽  
Governing Boards ◽  
Tertiary Institutions ◽  
College And University ◽  
For Profit ◽  
Not For Profit ◽  
Different Types ◽  
Institutional Contexts ◽  
Definition Of

Most studies of governance in tertiary education take as their points of reference colleges and universities, with few examining governance in organizations that deliver various other forms of tertiary education. These organizations often have governing boards, but the boards are not necessarily downsized versions of their college and university counterparts. Although some studies classify governing boards into different types, few offer a clear definition of such boards or explain how they actually function in institutional contexts other than colleges and universities. This study examines governance in five small, public, not-for-profit tertiary institutions, each with a board, to determine what the boards look like, how they perform, what is expected of them, and how they are similar to or different from other types of boards in colleges and universities.  

scholarly journals Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

2020 ◽  
Author(s):  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
Jean-Louis Reymond
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Virtual Screening ◽  
Generative Models ◽  
Synthetic Route ◽  
Synthetic Accessibility ◽  
Wide Range ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

scholarly journals Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Iku Tsutsui-Kimura ◽  
Hideyuki Matsumoto ◽  
Korleki Akiti ◽  
Melissa M Yamada ◽  
Naoshige Uchida ◽  
...  
Keyword(s):  
Decision Making ◽  
Activity Level ◽  
Decision Task ◽  
Prediction Errors ◽  
Temporal Difference ◽  
Dorsolateral Striatum ◽  
Negative Prediction ◽  
Different Types

Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence, and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, that is the temporal difference error. A major difference between areas was the overall activity level of reward responses: reward responses in dorsolateral striatum were positively shifted, lacking inhibitory responses to negative prediction errors. The differences in dopamine signals put specific constraints on the properties of behaviors controlled by dopamine in these regions.

scholarly journals ANALYSIS OF APPLICATION AND DEVELOPMENT OF DIFFERENT TYPES OF ARTIFICIAL INTELLIGENCE FOR THE RUSSIAN ECONOMY

2021 ◽  
Vol 229 (3) ◽  
pp. 382-390
Author(s):  
DANIL A. ERMOLAEV ◽  
Keyword(s):  
Artificial Intelligence ◽  
Clear Understanding ◽  
Wide Audience ◽  
The Public ◽  
Russian Economy ◽  
Different Types ◽  
Systematic Understanding

Artificial intelligence (AI) technologies become widespread use. And due to the lack of a clear understanding of the types and principles of work, AI can be perceived negatively by the public. In this article, the analysis of various types of artificial intelligence for the development and application in the Russian economy is carried out. Studying this topic will help you have a clear understanding of AI technologies, which will expand its areas of application. A wide audience with a systematic understanding of AI, inthe future, will change approaches to consumption and production.

scholarly journals Machine Learning Based Behavioral Modification

2019 ◽  
Vol 8 (6S2) ◽  
pp. 1134-1138
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Analysis ◽  
Time Of Day ◽  
Computational Techniques ◽  
Behavioral Modification ◽  
Optimal Temperature ◽  
The Past ◽  
Different Types ◽  
Artificial Intelligence Technology

To build up a particular profile about a person, the study of examining the comportment is known as Behavior analysis. Initially the Behavior analysis is used in psychology and for suggesting and developing different types the application content for user then it developed in information technology. To make the applications for user's personal needs it becoming a new trends with the use of artificial intelligence (AI). in many applications like innovation to do everything from anticipating buy practices to altering a home's indoor regulator to the inhabitant's optimal temperature for a specific time of day use machine learning and artificial intelligence technology. The technique that is use to advance the rule proficiency that rely upon the past experience is known as machine learning. By utilizing the insights hypothesis it makes the numerical model, and its real work is to infer from the models gave. To take the information clearly from the data the methodology utilizes computational techniques.

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