scholarly journals Opportunities and threats of using artificial intelligence in the business foresight of Russian companies

2021 ◽  
pp. 131-148
Author(s):  
Tatiana A. Panteleeva ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Research ◽  
Business Environment ◽  
Remote Access ◽  
It Services ◽  
Multiple Sources ◽  
Learning Mechanisms ◽  
Active Growth ◽  
Genetic Method

Subject/Topic. The article is devoted to the study of the possibilities and threats of using scientific intelligence in the business foresight and its impact on the business potential of the business in the short and long term. Methodology. In the process of writing the article, general scientific and philosophical methods of knowledge were used, as well as special economic methods based on them. Especially, the articles of the object of research – artificial intelligence – as the current process necessitated the use of problem-chronological and historical-genetic methods, which made it possible to distinguish the main stages of the formation of ideas, concepts, theories and methods for the use of artificial intelligence in business foresight, and the historical-genetic method showed the inseparability and intersectability from one stage to another of the development of the conceptual and methodological apparatus of the object of scientific research. Results. Currently, in business practice, artificial intelligence is used as a foresight tool very individually, since the complexity of its development and significant investments in the landscape infrastructure of its functioning form objective barriers to its rapid spread in the business environment. Currently, the following models of artificial intelligence are used in the business force: anthropocentric, hybrid, instrumental, machine-centric. According to the above calculations, starting from 2020, active growth is expected in the segment of business and IT services using artificial intelligence, it is also expected to increase spending on R&D projects in the field of development of products with artificial intelligence, and the most forward-specific from the point of view of investing capital and development as part of their own business model of AI directions on the horizon 2018-2025 are technologies for remote access (VDI, BKC, online communications, control), AI/ML (artificial intelligence, machine learning), VR/AR (virtual and augmented reality). Conclusions/Significance. In general, in 2020 compared to 2019, the optimism and motivation of the business to introduce artificial intelligence clearly showed a decline, and it should also be noted that the goals set by managers have become more «grounded»: in 2020, 45% spoke in favor of using artificial intelligence as a means of forming their own Big Data libraries, another 45% – for the integration of the artificial intelligence mechanism and existing systems for analysis and collection of information, however, a modern business strategy is not possible without processing huge amounts of customer information, and given their weak structuring and localization in multiple sources, the speed and quality of their processing and interpretation without the use of machine learning mechanisms became economically impractical. Application. The results of the scientific research will be useful both for educational purposes for students and readers interested in the use of artificial in-tech in business management, and for practitioners who plan to use artificial intelligence in foresight business processes.

scholarly journals Empowering IT Operations through Artificial Intelligence – A New Business Perspective

2020 ◽  
Author(s):  
Logica Banica ◽  
Persefoni Polychronidou ◽  
Cristian Stefan ◽  
Alina Hagiu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Performance ◽  
Business Environment ◽  
Infrastructure Monitoring ◽  
Parallel Data ◽  
New Business ◽  
Main Components ◽  
It Operations

This paper aims to describe the concept of applying Artificial Intelligence to IT Operations (AIOps) and its main components, Big Data, Machine Learning and Trend Analysis. The concept was implemented by developing a multi-layered fusion of the technologies that powers the components in AIOps platforms present on the IT market. The core of an AIOps platform is represented by the Big Data organization structure and by a massive parallel data processing platform like Apache Hadoop. The ML component of the platform is able to infer the future behaviour and the regular operations that are performed from the large volume of collected data, in order to develop the ability to automate the activities. AIOps platforms find their place especially in very complex IT infrastructures, ones that require constant monitoring and quick decisions in case of failures. The case study is based on the Moogsoft AIOps platform, and its features are presented in detail, using the Cloud trial version, clearly showing the potential of such an advanced tool for infrastructure monitoring and reporting. The experiment was focused on the way Moogsoft is monitoring computing resources,    is handling events and records alerts for the defined timespan, alerts grouped by category (like web services, social media, networking). The platform is also able to display at any given moment the unresolved situations and their type of origin, and includes automated remediation tools. The study presents the features of this software category, consisting in benefits for the business environment and their integration into the Internet-of-Things model. Keywords: Big Data, Machine Learning, AIOps, business performance.

scholarly journals USAGE OF MACHINE LEARNING IN THE PROCESS OF RISK-MANAGEMENT

2021 ◽  
Vol 9 (3) ◽  
pp. 126-130
Author(s):  
Sof'ya Svistunova ◽  
Sergey Muzalev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Business Processes ◽  
Corporate Structure ◽  
Learning Mechanisms ◽  
Economic Entity ◽  
Innovative Methods ◽  
Risk Management Process ◽  
Operational Activities

Background. Currently, artificial intelligence (AI) and machine learning are frequently implemented into the corporate structure and are aimed to transform the risk management system. Not only AI is useful for detection the interconnections between business processes, but also allows to accurately predict financial indicators and the reasons for possible deviations from standard values. Thus, the implementations of artificial intelligence and machine learning mechanisms makes it possible to increase the efficiency of operational activities and detect hidden risks. Method. The article discusses the main types of risks, identidication and minimization of which can be carried out using machine learning and also reveals key difficulties that arise while introducing innovative mechanisms into the structure of risk-management. The scientific novelty of the work lies in the relevance of using artificial intelligence mechanisms while minimizing the risks of an economic entity, as well as in identifying the main incentives for the efficient usage of machine learning in risk management. Result. As a result, the potential of introducing innovative methods into the structure of risk management to improve the efficiency of operating activities was revealed. Conclusion. In the process of the methodological study, the features of the application of machine learning methods in the risk management process were identified, moreover the article main limitations and possibilities of using artificial intelligence in order to minimize risks were revealed.

Use of chatbot on mobile devices

2020 ◽  
pp. 36978-36980
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Electronic Commerce ◽  
Mobile Devices ◽  
Call Center ◽  
Business Environment ◽  
Mobile Platforms ◽  
Efficient Tool ◽  
Center Service ◽  
Over Time

This article shows the beginning of the creation of chabots for mobile devices and their evolution on this platform over time, it was discussed what a chatbot is and its purpose, types of chatbot and how they act according to the concepts of artificial intelligence and machine learning. A questionnaire was prepared for the general public, where it was observed that the focus of use of this tool occurs on mobile devices, with the main areas of chatbot integration, electronic commerce and call center service. It was found that in most situations the chatbot proved to be an efficient tool for the user in solving a problem, searching for information, etc. The trend is that the use of this feature will gradually grow on mobile platforms and that it will be further incorporated into the business environment.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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