scholarly journals Towards Artificial Intelligence. Sociological Reflections on the Relationship Man - Organization - Device

2019 ◽  
Vol 1 (1) ◽  
pp. 912-920
Author(s):  
Małgorzata Suchacka ◽  
Nicole Horáková
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Management ◽  
Learning Process ◽  
Social Dimensions ◽  
Human Organization ◽  
Interdisciplinary Nature ◽  
The Relationship

AbstractThe main goal of the study will be to pay attention to technologization of the learning process and its social dimensions in the context of artificial intelligence. The reflection will mainly cover selected theories of learning and knowledge management in the organization and its broadly understood environment. Considering the sociological dimensions of these phenomena is supposed to lead to the emphasis on the importance of the security of the human-organization-device relationship. Due to the interdisciplinary nature of the issue, the article will include references to the concept of artificial intelligence and machine learning. Difficult questions will arise around the ideas and will become the conclusion of the considerations.

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

scholarly journals Entropy of Artificial Intelligence

Universe ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 53
Author(s):  
T. S. Biró ◽  
Antal Jakovác
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Process ◽  
Probability Space ◽  
Natural Processes ◽  
Artificial Intelligence Systems

We describe a model of artificial intelligence systems based on the dimension of the probability space of the input set available for recognition. In this scenario, we can understand a subset, which means that we can decide whether an object is an element of a given subset or not in an efficient way. In the machine learning (ML) process we define appropriate features, in this way shrinking the defining bit-length of classified sets during the learning process. This can also be described in the language of entropy: while natural processes tend to increase the disorder, that is, increase the entropy, learning creates order, and we expect that it decreases a properly defined entropy.

scholarly journals Inverse Problems and Artificial Intelligence

2021 ◽  
pp. 33-43
Author(s):  
С.И. Кабанихин
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
A Priori ◽  
Stability And Convergence ◽  
Computational Algorithms ◽  
Conditional Stability ◽  
A Priori Information ◽  
Sensitivity Data ◽  
Priori Information ◽  
The Relationship

В данной работе приведен анализ взаимосвязей теории обратных и некорректных задач и математических аспектов искусственного интеллекта. Показано, что при анализе вычислительных алгоритмов, которые условно можно отнести к вычислительному искусственному интеллекту (машинное обучение, природоподобные алгоритмы, методы анализа и обработки данных), возможно, а подчас и необходимо, использовать результаты и подходы, развитые в теории и численных методах решения обратных и некорректных задач, такие как регуляризация, условная устойчивость и сходимость, использование априорной информации, идентифицируемость, чувствительность, усвоение данных. This paper analyzes the relationship between the theory of inverse and incorrect problems and the mathematical aspects of artificial intelligence. It is shown that computational algorithms that can be categorized as computational artificial intelligence (machine learning, nature-like algorithms, data analysis and processing) can or should be analyzed with the approaches developed for the theory and numerical methods for solving inverse and incorrect problems. They are regularization, conditional stability and convergence, the use of a priori information, identifiability, sensitivity, data assimilation.

Media Technologies in Communication and Critical Cultural Studies

2018 ◽  
Author(s):  
Ned O'Gorman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Studies ◽  
Digital Media ◽  
Media Studies ◽  
21St Century ◽  
Social Life ◽  
Media Technologies ◽  
Cultural Processes ◽  
The Relationship

Media technologies are at the heart of media studies in communication and critical cultural studies. They have been studied in too many ways to count and from a wide variety of perspectives. Yet fundamental questions about media technologies—their nature, their scope, their power, and their place within larger social, historical, and cultural processes—are often approached by communication and critical cultural scholars only indirectly. A survey of 20th- and 21st-century approaches to media technologies shows communication and critical cultural scholars working from, for, or against “deterministic” accounts of the relationship between media technologies and social life through “social constructivist” understandings to “networked” accounts where media technologies are seen embedding and embedded within socio-material structures, practices, and processes. Recent work on algorithms, machine learning, artificial intelligence, and platforms, together with their manifestations in the products and services of monopolistic corporations like Facebook and Google, has led to new concerns about the totalizing power of digital media over culture and society.

scholarly journals Artificial Intelligence and the Limitations of Information

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 332 ◽  
Author(s):  
Paul Walton
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Processing ◽  
General Intelligence ◽  
Selection Pressures ◽  
Artificial General Intelligence ◽  
Relationship Of ◽  
The Relationship ◽  
The Way

Artificial intelligence (AI) and machine learning promise to make major changes to the relationship of people and organizations with technology and information. However, as with any form of information processing, they are subject to the limitations of information linked to the way in which information evolves in information ecosystems. These limitations are caused by the combinatorial challenges associated with information processing, and by the tradeoffs driven by selection pressures. Analysis of the limitations explains some current difficulties with AI and machine learning and identifies the principles required to resolve the limitations when implementing AI and machine learning in organizations. Applying the same type of analysis to artificial general intelligence (AGI) highlights some key theoretical difficulties and gives some indications about the challenges of resolving them.

scholarly journals Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020 ◽  
Vol 10 (3) ◽  
pp. 1042 ◽  
Author(s):  
Juan L. Rastrollo-Guerrero ◽  
Juan A. Gómez-Pulido ◽  
Arturo Durán-Domínguez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technology Enhanced Learning ◽  
Learning Collaborative ◽  
Academic Tasks ◽  
Learning Contexts ◽  
The Relationship ◽  
Enhanced Learning

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

Artificial Intelligence and the Temporality of Machine Images

2021 ◽  
Author(s):  
Andrew R. Johnston
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Video Games ◽  
Learning Strategies ◽  
Learning Algorithms ◽  
Computational Time ◽  
Artificial Intelligence Technology ◽  
Learning Test ◽  
The Relationship

DeepMind, a recent artificial intelligence technology created at Google, references in its name the relationship in AI between models of cognition used in this technology‘s development and its new deep learning algorithms. This chapter shows how AI researchers have been attempting to reproduce applied learning strategies in humans but have difficulty accessing and visualizing the computational actions of their algorithms. Google created an interface for engaging with computational temporalities through the production of visual animations based on DeepMind machine-learning test runs of Atari 2600 video games. These machine play animations bear the traces of not only DeepMind‘s operations, but also of contemporary shifts in how computational time is accessed and understood.

Artificial Intelligence

2021 ◽  
pp. 210-221
Author(s):  
Ankit Dhamija ◽  
Niti Chatterji
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Knowledge Management ◽  
Stage Of Development ◽  
Indian Context ◽  
The Relationship ◽  
Current Stage

Machines have emerged as intelligent players and are set to replace skilled practitioners in various fields. So, what would be a leader's contribution be if machines do the decision making? The chapter addresses this question by proposing that artificial intelligence will act as a catalyst enabling managers and leaders in the process of knowledge management. Further, the chapter aims to bring together the three constructs of leadership, artificial intelligence, and knowledge management and try to theoretically establish a relationship among them. The work is immensely relevant to the Indian context given the fact that at its current stage of development, artificial intelligence has the potential to add $957 billion to the country's economy by 2035. Thus, the chapter will emphasize the relationship between leadership and artificial intelligence and how it supports knowledge management in organizations and influences its everyday decision making.

scholarly journals Artificial intelligence as a subject of property law

2020 ◽  
pp. 328-332
Author(s):  
Yelizaveta Tymoshenko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intellectual Property ◽  
Intellectual Property Rights ◽  
Legal Status ◽  
Property Law ◽  
Process Automation ◽  
Long Time ◽  
Relationship Of ◽  
The Relationship

The article considers artificial intelligence (AI) as a new and existing subject of legal relations. It is described in detail about hisability to be a full participant in the relationship of intellectual property rights. Artificial intelligence allows computers to learn fromtheir own experience, adapt to given parameters and perform tasks that previously could only be done by humans. In most cases, theuse of artificial intelligence, whether for playing chess or as an unmanned vehicle, is extremely important to be able to learn and processnatural language. That is, the development and awareness of AI is needed. Thanks to information technology, computers can be “taught”to perform certain tasks by processing large amounts of data and identifying patterns in them.Artificial intelligence is not in itself identical to the concept of “process automation”, but the development of AI will lead to thefact that more and more tasks will be under the power of a computer program. Therefore, it is important to start adapting the legislationto future realities now. It is necessary to define the range of rights and responsibilities of AI, to indicate its legal status. Accordingly, itis necessary to determine who will be the legal bearer of all rights and responsibilities that will arise as a result of the creation of a particularcreative object. In the field of intellectual property law, AI is seen as a new source of creativity, a source capable of producingnew results.The article offers consideration of these issues in the field of intellectual property, as for several years there are examples of worksinvented by artificial intelligence. For a long time, programs were just a tool to help the author create a work. However, with the deve -lopment of machine learning and neural networks, artificial intelligence has learned to create a variety of objects: images, videos, music,design. Since the result obtained by artificial intelligence can be potentially protective, the article discusses the question of who shouldrecognize the rights to objects created by AI.

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