scholarly journals The next step for Artificial Intelligence in a globally connected economy

2021 ◽  
Vol 129 ◽  
pp. 04001
Author(s):  
Dumitru Alexandru Bodislav ◽  
Florina Bran ◽  
Carol Cristina Gombos ◽  
Amza Mair

Research background: This research paper represents an overview of what artificial intelligence is, what are its roots, and what is the next big thing regarding the domain. In this paper we try to highlight how the domain is growing and what is the difference between the ideology, the business factor and the human factor. We try to create a big picture on the entire phenomenon by creating a parallel between machine learning, artificial intelligence and the influence of technological breakthrough from a hardware perspective. Purpose of the article: The paper is built as a tool in understanding technology, globalization and the pathway to success and scientific glory for what can be seen as the industry of artificial intelligence. The tools presented in the research have the purpose to create an easier path to how we can develop this domain by accelerating theoretical processing and business analytics that come together to form the next level of machine learning/artificial intelligence; research and development, everything being filtered from an economic point of view. Methods: The used research method is based on fundamental analysis of the artificial intelligence domain and its purpose in the complexity of globalization and economic development. Findings & Value added: The paper tries to offer a tool for building a better understanding of the next decade in the domain of artificial intelligence.

2020 ◽  
Vol 07 (03) ◽  
pp. 209-229
Author(s):  
Bálint Fazekas ◽  
Attila Kiss

In classical artificial intelligence and machine learning fields, the aim is to teach a certain program to find the most convenient and efficient way of solving a particular problem. However, these approaches are not suitable for simulating the evolution of human intelligence, since intelligence is a dynamically changing, volatile behavior, which greatly depends on the environment an agent is exposed to. In this paper, we present several models of what should be considered, when trying to simulate the evolution of intelligence of agents within a given environment. We explain several types of entropies, and introduce a dominant function model. By unifying these models, we explain how and why our ideas can be formally detailed and implemented using object-oriented technologies. The difference between our approach and that described in other papers also — approaching evolution from the point of view of entropies — is that our approach focuses on a general system, modern implementation solutions, and extended models for each component in the system.


2018 ◽  
pp. 153-168
Author(s):  
Magdalena Dziedzic

In contemporary contract and consumer law, obligations to inform are an example of instruments (protective ones) which imposes on business entities a duty to make a statement of knowledge (a representation), the content of which is determined by regulations and the purpose of which is to aid the consumer in taking a well-informed, rational decision. Appropriate regulations referring to liability for failing to carry out this obligation to inform aim to maintain optimal trust between the contracting parties and, as a result, lead to a balance in the parties’ position, at the same time upholding the principle of the freedom of contract. In accordance with the fundamental assumption in European consumer law, one’s liability towards a consumer should meet the criteria of both efficiency and proportionality, which means that one should not strictly consider such liability purely formally, i.e., as maintaining an economic balance between the parties. The sanction the company shall incur is to serve the actual satisfaction of the interests of the consumer, and not only to make a profit. Additionally, the sanctions for neglecting the obligation to inform are expected to encourage companies to comply with them. Neglecting this obligation to inform in the pre-contractual phase may take the form of not providing information which is required and explicitly defined by law or providing incomplete information. A large amount of detail in determining a business’s responsibility is presumedto guarantee the consumer knowledge of his/her rights and to enable him/her to evaluate the risks resulting from entering into a particular transaction. One must not, however, ignore the fact that providing excessive, thus illegible, information must be treated equally to non-disclosure of such information, which may result in infringement of the aforementioned regulations. Neglecting the obligation to inform may also arise in such a case where the consumer is not provided with a particular piece of information, despite the lack of a definite legal basis in this regard – such as a detailed regulation contained in an act – but such a duty would result from a general loyalty duty between the contracting parties. In the beginning, it should be noted that the liability for an infringement of the pre-contractual obligation to inform is characterised by system heterogeneity. In particular, it refers to the distinct consumer protection regime. It is very often the case that depending on the contractor’s status (professional or nonprofessional) the legal consequences of failing to inform or improperly informing are framed in different ways. One must bear in mind the difference between solely the failure to inform or to improperly carry out the pre-contractual obligation to inform (pursued within pre-contractual liability, fundamentally according to an ex delicto regime) and the consequences arising from the content of the delivered information, i.e., the guarantee of definite elements in the legal relationship of an obligatory nature (assigned to the classic liability in an ex contractu regime). The subject of civil liability for the infringement of duties to inform can be analysed from two perspectives: firstly, from an economic point of view, i.e., whether for the aggrieved party and for the market at large it would be more favourable for the infringement of the duty to inform to be pursued within an ex contractu or ex delicto regime, and secondly, from the perspective of the theory of law, whether for the system of contract law it would be better for this liability to be pursued within an ex contractu or ex delicto regime. In response to the second question, the position of academics is that the liability for the violation of trust due to failing to properly inform the consumer should be pursued in an ex delicto system in order to maintain the internal cohesion of contract law.


2021 ◽  
Vol 291 ◽  
pp. 04010
Author(s):  
Anton Nazarov ◽  
Denis Kovtun ◽  
Stefan Talu

Artificial intelligence as a simulator of human behavior and thinking emerged as a result of machine learning. Through AI, they recognize and interpret data, on the basis of which programs of various types of activities are subsequently built. The rapid introduction of artificial intelligence-based technologies into the economic and social spheres of the international community has not been left out of the United Nations’ view from the point of view of using the capabilities of digital computers to solve problems at the level of intelligent beings in order to achieve the goals of sustainable development. The article discusses the specific aspects of I, the application of which will make the process of achieving the SDGs more effective and of high-quality.


2003 ◽  
Vol 358 (1435) ◽  
pp. 1293-1309 ◽  
Author(s):  
Jean-Daniel Zucker

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.


2020 ◽  
Vol 23 (3) ◽  
pp. 79-98
Author(s):  
Fernando Silva Pereira

Machine learning is a field of artificial intelligence that gives computers the ability to learn without being explicitly programmed, posing the problem of using the outputs of deep learning software as evidence in a judicial process. Focusing on Civil Procedure Law, this article aims to reflect on this problem, from the point of view of the admissibility and weight of such an evidence, giving close attention to the north-American experience, where the problem of the use of scientific and technic evidence has been largely discussed.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hishan S. Sanil ◽  
Deepmala Singh ◽  
K. Bhavana Raj ◽  
Somya Choubey ◽  
Narinder Kumar Kumar Bhasin ◽  
...  

Purpose “Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic. Findings According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way. Originality/value The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).


2020 ◽  
Vol 144 ◽  
pp. 58-67
Author(s):  
Oleg L. Figovsky ◽  
◽  
Oleg G. Pensky ◽  

Current mathematical models of economics practically do not take into account the human factor when making management decisions and applying them to practice. Therefore, the creation of a mathematical theory of general human psychology, the dialectical development of human society and macroeconomics are becoming particularly relevant at present. This paper describes the main results of the mathematical modeling of psychological behavior, so-called digital twins, which are psychological analogs of people. Theorems explaining the dangers of artificial intelligence for people from the mentality point of view are formulated. We propose general models of dialectical development of the virtual world for digital twins, human society and macroeconomics.


Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2105 ◽  
Author(s):  
Shrinathan Esakimuthu Pandarakone ◽  
Yukio Mizuno ◽  
Hisahide Nakamura

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.


Author(s):  
Joachim Wagner

AbstractThis paper uses a new tailor-made data set to investigate the differences in extensive and intensive margins of exports in manufacturing firms from East Germany and West Germany. It documents that these margins do still differ in 2010, 20 years after the re-unification of Germany. West German firms outperform East German firms at all four margins of exports – they have a larger propensity to export, export a larger share of total sales, export more goods and export to a larger number of countries. All these differences are large from an economic point of view. A decomposition analysis shows that in 2010 between 59 percent and 78 percent of the difference in margins can be explained by differences in firm characteristics.


Akademos ◽  
2021 ◽  
pp. 133-140
Author(s):  
Miroslava Luchiancicova (Metleaeva) ◽  

The cognitive process and one of its most difficult aspects – translation into different languages - is an obstacle to creating a perfect Artificial Intelligence, because translation is not limited to formulas. The accuracy of the terminology and the lack of ambiguity of the word are the purposes of artificial intelligence while limiting the development of linguistic thinking. The transhumanism involves the improvement of the man, as an unfinished link of evolution, based on his connection with technological elements. From the author`s point of view, the “man-machine” interaction offers a dubious advantage for the human civilization and the planetary ecological system. This is, first of all, anachronistic and its main goal is singularity, i.e., the management of intellectual energy within the strict framework of technology. The article schematically provides the explicit sequence of the translation process. Based on relevant examples, the author demonstrates the difference between mental operations of Artificial Intelligence and those of the man in the translation process. According to the author, translation is a universal feature of human thought, while the process of translating the thought into the speech form is due to biological-genetic, emotional, socio-historical memory and experience, as well as the possibility of the individual translation of the thought into the mother tongue and into any other language. Moreover, translation is one of the strongest survival qualities of homo sapiens.


Sign in / Sign up

Export Citation Format

Share Document