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 MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

2019 ◽  
Author(s):  
Сергей Шумский ◽  
Sergey Shumskiy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Revolution ◽  
Machine Intelligence ◽  
General Intelligence ◽  
New Science ◽  
Artificial General Intelligence ◽  
Science Of Mind ◽  
Artificial Brains

This book is about the nature of mind, both human and artificial, from the standpoint of the theory of machine learning. It addresses the problem of creating artificial general intelligence. The author shows how one can use the basic mechanisms of our brain to create artificial brains of future robots. How will this ever-stronger artificial intelligence fit into our lives? What awaits us in the next 10-15 years? How can someone who wants to take part in a new scientific revolution, participate in developing a new science of mind?

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.

Attention, Consciousness, and Linguistic Cooperation with AI

2021 ◽  
pp. 1-17
Author(s):  
Carlos Montemayor
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Problem Solving ◽  
Joint Attention ◽  
Phenomenal Consciousness ◽  
General Intelligence ◽  
Collective Rationality ◽  
Satisfactory Solution ◽  
Epistemic Agency ◽  
Artificial General Intelligence

Contemporary debates on Artificial General Intelligence (AGI) center on what philosophers classify as descriptive issues. These issues concern the architecture and style of information processing required for multiple kinds of optimal problem-solving. This paper focuses on two topics that are central to developing AGI regarding normative, rather than descriptive, requirements for AGIs epistemic agency and responsibility. The first is that a collective kind of epistemic agency may be the best way to model AGI. This collective approach is possible only if solipsistic considerations concerning phenomenal consciousness are ignored, thereby focusing on the cognitive foundation that attention and access consciousness provide for collective rationality and intelligence. The second is that joint attention and motivation are essential for AGI in the context of linguistic artificial intelligence. Focusing on GPT-3, this paper argues that without a satisfactory solution to this second normative issue regarding joint attention and motivation, there cannot be genuine AGI, particularly in conversational settings.

The Type of Temperament, Mood, and Strategies of Categorization

2012 ◽  
Vol 33 (4) ◽  
pp. 227-236 ◽  
Author(s):  
Agata Wytykowska
Keyword(s):  
Information Processing ◽  
Negative Mood ◽  
Cognitive Activity ◽  
Processing Capacity ◽  
Affective Value ◽  
The Relationship ◽  
The Way

In Strelau’s theory of temperament (RTT), there are four types of temperament, differentiated according to low vs. high stimulation processing capacity and to the level of their internal harmonization. The type of temperament is considered harmonized when the constellation of all temperamental traits is internally matched to the need for stimulation, which is related to effectiveness of stimulation processing. In nonharmonized temperamental structure, an internal mismatch is observed which is linked to ineffectiveness of stimulation processing. The three studies presented here investigated the relationship between temperamental structures and the strategies of categorization. Results revealed that subjects with harmonized structures efficiently control the level of stimulation stemming from the cognitive activity, independent of the affective value of situation. The pattern of results attained for subjects with nonharmonized structures was more ambiguous: They were as good as subjects with harmonized structures at adjusting the way of information processing to their stimulation processing capacities, but they also proved to be more responsive to the affective character of stimulation (positive or negative mood).

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

Anthropological communities of interpretation for Burma: An overview

2008 ◽  
Vol 39 (2) ◽  
pp. 239-254 ◽  
Author(s):  
U Chit Hlaing
Keyword(s):  
Southeast Asia ◽  
Ethnic Groups ◽  
Field Of Study ◽  
Anthropological Theory ◽  
History Of ◽  
Relationship Of ◽  
The Relationship ◽  
The Way

AbstractThis paper surveys the history of anthropological work on Burma, dealing both with Burman and other ethnic groups. It focuses upon the relations between anthropology and other disciplines, and upon the relationship of such work to the development of anthropological theory. It tries to show how anthropology has contributed to an overall understanding of Burma as a field of study and, conversely, how work on Burma has influenced the development of anthropology as a subject. It also tries to relate the way in which anthropology helps place Burma in the broader context of Southeast Asia.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals The brain as a fundamental axis in learning process

2019 ◽  
Vol 5 (4) ◽  
pp. 38-45
Author(s):  
Luis Raul Meza Mendoza ◽  
María Elena Moya Martinez ◽  
Angelica Maria Sabando Suarez
Keyword(s):  
Learning Process ◽  
The Body ◽  
Traditional Scheme ◽  
Teaching Learning ◽  
Relationship Of ◽  
The Relationship ◽  
The Brain ◽  
Learning Schemes ◽  
New Strategies ◽  
The Way

Since the beginning of humanity, an attempt has been made to explain the way in which man acquires knowledge, the way in which he assimilates, processes and executes it in order to develop the teaching-learning process that people need throughout of his life, which forces to change the learning schemes using new study methodologies, such as neuroscience, which is a discipline that studies the functioning of the brain, the relationship of neurons to the formation of synapses creating immediate responses which transmits to the body voluntarily and involuntarily, in addition to controlling the central and peripheral nervous system with their respective functions. It is necessary to change the traditional scheme and implement new strategies that allow the teacher to venture into neuroscience, in order to individually understand the different learning processes that students do. As some authors of neuroscience say, the brain performs processes of acquisition, storage and evocation of information, which form new knowledge schemes that generate changes in the attitude of the human being, for this reason teachers are responsible for taking advantage of what It is known about the multiple functions of the brain and be clear about the various ways of acquiring knowledge.

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