Artificial Intelligence and Innovation Management

10.1142/q0334 ◽  
2022 ◽  
Author(s):  
Stoyan Tanev ◽  
Helena Blackbright
Keyword(s):  
Artificial Intelligence ◽  
Innovation Management

INTERDISCIPLINARY LINKS, INTEGRATIVE APPROACH,HEURISTIC METHODS AND ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF INNOVATION - PART 1

2018 ◽  
Vol 28 (5) ◽  
pp. 1527-1532
Author(s):  
Hristo Patev
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Innovation Management ◽  
Integrative Approach ◽  
Heuristic Methods ◽  
Organizational Renewal ◽  
Innovation Activities ◽  
Management Of Innovation ◽  
Inventive Activity ◽  
High Degree

In this first work, out of the total of twenty-four, are considered: Integrative approach, interdisciplinary relations and transnational language in the technical and economic fundament of engineering and management, for the purpose of competitive innovation and successful business. Approaches to develop the innovation with a high degree of complexity. Interactive heuristic methods and algorithms for inventive activity, for inspiring and developing new industrial products and services for households and production systems. Implementing an effective business vocabulary for organizational renewal. Introduction of gaming and "art" methods in innovation management. Intensifying innovation activities through an attempt to introduce artificial intelligence into teamwork, with simultaneous implementation of an engineering and non-engineering approach.

scholarly journals Automation

Management ◽  
2019 ◽  
Author(s):  
Manav Raj ◽  
Robert C. Seamans
Keyword(s):  
Artificial Intelligence ◽  
Labor Market ◽  
21St Century ◽  
Innovation Management ◽  
Major Work ◽  
Dominant Firms ◽  
Occupational Task ◽  
And Robotics ◽  
Different Levels ◽  
Task Level

Since the first decades of the 20th century, there has been concern that automation, including mechanization, computing, and more recently robotics and artificial intelligence (AI), will take away jobs and damage the labor market. There has also been concern that large, dominant firms will capture whatever value is created by automating technologies. In an effort to understand these issues, a wide variety of scholars have studied automation. Automation has been studied at a number of levels, including country, industry, firm, occupation, and even the occupational-task level, and by a range of disciplines, including economics, innovation, management, organizational theory, sociology, and strategy. This annotated bibliography attempts to include a range of literature that speaks to these different levels and different disciplines. It includes articles that are older, foundational pieces so readers can familiarize themselves with the major work in the area, as well as more recent articles so readers can get a sense of current research interests and opportunities. Notably, much of the recent research is focused on the effects of AI and robotics on workers, firms, and the economy. It is likely that there will be a large increase in research in this space in the coming years, especially as more data on the adoption of these technologies becomes available, and that this research will tell us much more about how these technologies are affecting our economy in the 21st century as well as inform our understanding of automation more generally.

scholarly journals Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tianxiang Wang ◽  
Qingqing Ma ◽  
Jinxi Li
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Technology Policy ◽  
Innovation Management ◽  
High Technology ◽  
Policy Innovation ◽  
Prediction Algorithm ◽  
Power Prediction ◽  
Management Mechanism

Since industrialization, manufacturing has been an important pillar of a country’s economic development. Under the dual pressure of the new trend of global manufacturing development and the loss of competitive advantage of manufacturing industry, it is especially important to accelerate the enhancement of national high technology innovation capacity and the optimization of high technology policy innovation management mechanism driven by advanced evolutionary Internet of Things (IoT) arithmetic. The main of this paper thus introduces the effective method of optimization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic. To study the optimization of high technology policy innovation management mechanism, a conceptual analysis of currently popular information technologies, such as big data technologies, artificial intelligence technologies, and Internet of Things technologies, and an overview of the application of these technologies in microgrids are given. In the paper, all factors are studied using the STP innovation management mechanism-based approach, and finally, all factors are classified into two categories of cause and effect factors by this approach, and the importance of all factors is ranked. Secondly, a wind power prediction algorithm based on data mining technology and an improved algorithm and a PV power prediction algorithm based on a deep neural network were established with the technical support of high-tech information technology such as big data and artificial intelligence. Finally, the majorization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic is proposed.

scholarly journals Artificial intelligence and new technologies in times of pandemics

2021 ◽  
Vol 25 (110) ◽  
pp. 164-171
Author(s):  
Yanina Alexandra Viteri Alcivar ◽  
Cristhian Gustavo Minaya Vera ◽  
Diana Esperanza Saltos Pinargote ◽  
Maria Teresa Cano Montesdeoca
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Design ◽  
New Technologies ◽  
Innovation Management ◽  
Social Needs ◽  
Redes Sociales ◽  
Scientific Publications ◽  
Human Needs ◽  
Computer Aided ◽  
Before And After

Artificial intelligence has marked a before and after in the history of computing, in technological evolution. However, this type of development is unreliable for people, since it puts at risk the use of confidential data and information to be used for other particular interests of companies. This paper evaluates the most salient aspects of artificial intelligence associated with new technologies and its impact on society during the pandemic period. Social needs in the period of social distancing, confinement and how a life of confinement is made acceptable with the use of technological tools are evaluated. A set of scientific publications, academic material, technical reviews are taken into account and it can be observed that new technologies are useful and necessary in the pandemic, however, it is not enough to cover human needs. Keywords: New technologies, technological society, pandemic, artificial intelligence References [1]B. Pan y Q. Qin, «Construction of parallel corpus for english translation teaching based on computer aided translation software,» Computer-Aided Design and Applications, vol. 19, nº 1, pp. 70-80, 2022. [2]I. Salazar, «Los robots y la Inteligencia Artificial. Nuevos retos del periodismo.,» Doxa Comunicación, vol. 27, pp. 295-315, 2018. [3]V. Berlanga, «Inteligencia artificial y robótica: responsabilidad civil y penal de los robots,» Universidad de Cantabria, España, 2020. [4]T. Ord, «Retos de la industria 4.0,» Futuro hoy, vol. 2, nº 1, pp. 6-7, 2021. [5]E. E. S. P. Vlasova, «Artificial intelligence - The space for the new possibilities to train teachers,» Espacios, vol. 40, nº 9, 2019. [6]S. Fuentes, E. Tongson y C. Gonzalez- Viejo, «Novel digital technologies implemented in sensory science and consumer perception,» Current Opinion in Food Science, vol. 41, pp. 99-106, 2021. [7]Hootsuite, «140 estadísiticas de las redes sociales que son importantes para los mercadólogos en 2021,» 20 abril 2020. [Online]. Available: https://blog.hootsuite.com/es/125-estadisticas-de-redes-sociales/#generales. [Last access: June 17, 2021]. [8]Branch, «Estadísticas de la situación digital de Ecuador 2020-2021,» 5 mayo 2021. [Online]. Available: https://branch.com.co/marketing-digital/estadisticas-de-la-situacion-digital-de-ecuador-en-el-2020-2021/. [Last access: June 17, 2021]. [9]N. Yams, V. Richardson, G. Shubina, S. Albrecht y D. Gillblad, «Integrated ai and innovationmanagement: The beginning of a beautiful friendship,» Technology Innovation Management Review, vol. 10, nº 11, pp. 5-18, 2021.

scholarly journals MANAGING INNOVATION WITH ARTIFICIAL INTELLIGENCE

2020 ◽  
Vol 8 (2) ◽  
pp. 47-53
Author(s):  
Aleksey Aksenov
Keyword(s):  
Artificial Intelligence ◽  
Systematic Approach ◽  
Organizational Innovation ◽  
Technological Development ◽  
Innovation Management ◽  
Theory Of The Firm ◽  
Innovation Process ◽  
Human Labor ◽  
Manage Innovation ◽  
Artificial Intelligence Systems

Artificial intelligence is changing companies and how they manage innovation. Rapid technological development and the replacement of human labor with artificial intelligence can force management to rethink the entire innovation process of the company. Using the ideas of the Carnegie School and the behavioral theory of the firm, the implications for innovation management of artificial intelligence technologies and artificial intelligence systems based on machine learning were considered. A diagram is presented showing the extent to which artificial intelligence can replace humans. It is shown what aspects should be taken into account when transforming into a digital organization of innovations. It is noted that artificial intelligence plays a constructive role where the time-tested advantages of innovation management resources are suppressed, impossible due to digitization. The clear potential of artificial intelligence lies in creating a more systematic approach by integrating artificial intelligence into organizations seeking to innovate. The results of the study point to areas in which artificial intelligence systems can already be fruitfully applied in organizational innovation, namely, in cases where the development of new innovations is primarily constrained by the limitations of information processing. Artificial intelligence systems that rely on anomaly detection, for example, can be useful when firms grapple with processing constraints in search of new opportunities.

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