Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

2021 ◽  
Author(s):  
Adrien Bécue ◽  
Isabel Praça ◽  
João Gama
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Cyber Threats ◽  
Challenges And Opportunities

scholarly journals Corporate governance for trillion dollar opportunities

2017 ◽  
Vol 13 (3) ◽  
pp. 19-27 ◽  
Author(s):  
Hugh Grove ◽  
Maclyn Clouse
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Big Data ◽  
Boards Of Directors ◽  
Industry 4.0 ◽  
The Internet ◽  
Technological Changes ◽  
Corporate Executives ◽  
Challenges And Opportunities ◽  
The Internet Of Things

Boards of Directors will have to play a key role in the technological survival and development of companies by asking corporate executives about their plans and strategies for these emerging technological changes and challenges. Key challenges and opportunities discussed in this paper, with corresponding corporate governance implications, included Big Data, Artificial Intelligence (AI) with Industry 4.0, AI with the Internet of Things (IoT), Deep Learning, and Neural Networks. Survival should not be the goal, but it may be the necessary first step for today’s companies. Potential winners seizing these trillion dollar opportunities will be company executives and Boards of Directors who can incorporate these technological changes into specific new business models, strategies, and practices. While the awareness on boards regarding risks originating from disruptive innovation, cyber threats and privacy risks has been increasing, Boards of Directors must equally be able to challenge executives and identify opportunities and threats for their companies. This shift for companies is not only about digital technology but also cultural. How can people be managed when digital, virtual ways of working are increasing? What do robotics and Big Data analysis mean for managing people? One way to accelerate the digital learning process has been advocated: the use of digital apprentices for boards. For example, Board Apprentice, a non-profit organization, has already placed digital apprentices on boards for a year-long period (which helps to educate both apprentices and boards) in five different countries. Additional plans and strategies are needed in this age of digitalization and lifelong learning. For example, cybersecurity risks are magnified by all these new technology trends, such as Big Data, AI, Industry 4.0, and IoT. Accordingly, the main findings of this paper are analysing the challenges and opportunities for corporate executives, Boards of Directors, and related corporate governance concerning the driving force of Big Data, Artificial Intelligence with Industry 4.0, Artificial Intelligence with the Internet of Things, Deep Learning, and Neural Networks.

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Jan Harm Christiaan Pretorius
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Sustainable Manufacturing ◽  
Big Data Analytics ◽  
Content Type ◽  
Research Framework ◽  
Challenges And Opportunities

Purpose The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths. Design/methodology/approach Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities. Findings This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions. Research limitations/implications It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities. Social implications Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities. Originality/value This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

The Power of Matrix Factorization: Methods for Deconvoluting Genetic Heterogeneous Data at Expression Level

2021 ◽  
Vol 15 (8) ◽  
pp. 841-853
Author(s):  
Yuan Liu ◽  
Zhining Wen ◽  
Menglong Li
Keyword(s):  
Artificial Intelligence ◽  
Matrix Factorization ◽  
In Silico ◽  
Genetic Data ◽  
Heterogeneous Data ◽  
Expression Level ◽  
Mathematical Methods ◽  
Challenges And Opportunities ◽  
Factorization Methods ◽  
Genetic Profiles

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

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