Machine reengineering: robots and people working smarter together

2017 ◽  
Vol 45 (6) ◽  
pp. 50-54 ◽  
Author(s):  
Prashant Shukla ◽  
H. James Wilson ◽  
Allan Alter ◽  
David Lavieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Design Methodology ◽  
It Professionals ◽  
Content Type ◽  
Public Agency ◽  
Intelligent Machines ◽  
Intelligent Machine ◽  
Working Together ◽  
Practical Implications

Purpose The authors explore the potential of machine learning, computers employ that an algorithm to sort data, make decisions and then continuously assess and improve their functionality. They suggest that it be used to power a radical redesign of company processes that they call machine reengineering. Design/methodology/approach The authors interpret a survey of more than a thousand corporate public agency IT professionals on their use of artificial intelligence and machine learning. Findings Companies that embrace machine learning find that it adds value to the work product of their employees and provides companies with new capabilities. Practical implications Working together with an intelligent machine, workers become custodians of powerfully smart tools, tools that personalize work to maximize their most productive ways of working. Originality/value A guide to establishing a culture that empowers employees to thrive alongside intelligent machines.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

How to optimize marketing strategy development

2020 ◽  
Vol 36 (8) ◽  
pp. 17-19
Keyword(s):  
Artificial Intelligence ◽  
Marketing Strategy ◽  
Design Methodology ◽  
Reading Time ◽  
Marketing Strategies ◽  
Strategy Development ◽  
Content Type ◽  
Human Creativity ◽  
Pertinent Information ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Artificial Intelligence (AI) is enabling companies to perform many functional tasks more efficiently. Some organizations are starting to further utilize its capabilities by combining the rationality of AI with human creativity in order to optimize development of marketing strategies. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

scholarly journals Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Babak Abedin
Keyword(s):  
Artificial Intelligence ◽  
System Performance ◽  
Fuzzy Systems ◽  
Data Analytics ◽  
Design Methodology ◽  
Contingency Theory ◽  
Content Type ◽  
The Social ◽  
Opposing Effects ◽  
Practical Implications

PurposeResearch into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.Design/methodology/approachThe author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.FindingsThe author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).Research limitations/implicationsAs in other systematic literature review studies, the results are limited by the content of the selected papers.Practical implicationsThe findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.Originality/valueThis study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

scholarly journals Robotopias: mapping utopian perspectives on new industrial technology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rhiannon Firth ◽  
Andrew Robinson
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
New Technologies ◽  
Social Role ◽  
Content Type ◽  
Human Essence ◽  
The Relationship ◽  
Labour Power ◽  
Practical Implications

PurposeThis paper maps utopian theories of technological change. The focus is on debates surrounding emerging industrial technologies which contribute to making the relationship between humans and machines more symbiotic and entangled, such as robotics, automation and artificial intelligence. The aim is to provide a map to navigate complex debates on the potential for technology to be used for emancipatory purposes and to plot the grounds for tactical engagements.Design/methodology/approachThe paper proposes a two-way axis to map theories into to a six-category typology. Axis one contains the parameters humanist–assemblage. Humanists draw on the idea of a human essence of creative labour-power, and treat machines as alienated and exploitative form of this essence. Assemblage theorists draw on posthumanism and poststructuralism, maintaining that humans always exist within assemblages which also contain non-human forces. Axis two contains the parameters utopian/optimist; tactical/processual; and dystopian/pessimist, depending on the construed potential for using new technologies for empowering ends.FindingsThe growing social role of robots portends unknown, and maybe radical, changes, but there is no single human perspective from which this shift is conceived. Approaches cluster in six distinct sets, each with different paradigmatic assumptions.Practical implicationsMapping the categories is useful pedagogically, and makes other political interventions possible, for example interventions between groups and social movements whose practice-based ontologies differ vastly.Originality/valueBringing different approaches into contact and mapping differences in ways which make them more comparable, can help to identify the points of disagreement and the empirical or axiomatic grounds for these. It might facilitate the future identification of criteria to choose among the approaches.

The impact of artificial intelligence and machine learning on the global economy and its implication for the hospitality sector in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhanshu Bhushan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Global Economy ◽  
Regulatory System ◽  
Content Type ◽  
Original Contribution ◽  
Hospitality Sector ◽  
Global Regulatory System ◽  
The Impact ◽  
Practical Implications

Purpose The purpose of this paper is to explore and evaluate the existing and future impact of artificial intelligence (AI) and machine learning on the global economy. It includes viewing the inclusion of AI in different sectors, its impact on industries, the trends of the forerunning companies that are capitalizing on AI and the idea of crystalizing exponential growth while maintaining a balance between the understanding of humans and the subsequent possibilities of AI. Design/methodology/approach This paper is based on secondary research, reviewing literature based on different industries and perspectives. Findings The global potential of AI is exponential; the development of AI should be effective. Globally, we see contrasting views, defining the consequences of AI. Hence, the balance between humans and AI, protocols and a global regulatory system needs to be established to prevent catastrophic results soon. Practical implications The benefits of AI are enormous. The rising incorporation of AI must take into consideration the basic safety fundamentals for a better future. Social implications This paper will enable readers to understand the importance of AI in the global economy, its current involvement in major industries and the subsequent need for balance in technology. Originality/value This conceptual review is by its nature and original contribution and, specifically, an interpretation for India.

The impacts of artificial intelligence (AI) on jobs: an industry perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Placide Poba-Nzaou ◽  
Malatsi Galani ◽  
Sylvestre Uwizeyemungu ◽  
Arnela Ceric
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Employability Skills ◽  
Rapid Review ◽  
Professional Literature ◽  
Content Type ◽  
Job Tasks ◽  
Hr Function ◽  
Near Future ◽  
Practical Implications

Purpose This paper aims to explore the impacts of artificial intelligence (AI) on jobs. Design/methodology/approach The authors followed rapid review guidelines. The authors collected industry and government reports published prior and up to August 2017 in Google and Google Scholar using combination of key words: “job automation” or “work automation” with technology keywords: “artificial intelligence,” “machine learning,” etc. In total, 11 were included in this research. Findings The use of AI technologies will impact jobs in the near future as some job tasks are automated. AI is likely to substitute both, routine and nonroutine tasks. It is expected that humans and robots would work together in ways never imaginable. Changes in employability skills are expected. Because of the magnitude of these impacts on jobs, consulted reports call for concerted solutions that go beyond organizations’ and industry’s boundaries to include other relevant stakeholders. Moreover, organizations will have to rethink their human resource (HR) function to realign its expertise to the reality of AI. Practical implications In this context, the HR function will have to understand the dynamics that generate the impacts of these technologies in a workplace, to anticipate changes and actively contribute to creating an organizational environment that will facilitate the collaboration between human workers and complex digital agents, while ensuring compliance with labor and employment laws and supporting strategic organizational objectives. Originality/value This paper contributes to the debate on ongoing concerns by providing a synthesis of relevant professional literature.

Artificial intelligence-based training learning from application

2019 ◽  
Vol 33 (2) ◽  
pp. 20-23 ◽  
Author(s):  
Ashwani Kumar Upadhyay ◽  
Komal Khandelwal
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Theoretical Foundation ◽  
Relevant Information ◽  
Relevant Research ◽  
Research Papers ◽  
Content Type ◽  
Training Systems ◽  
Effectiveness Of Training ◽  
Practical Implications

Purpose This paper aims to discuss the rationale, theoretical foundation, application, and future of artificial intelligence (AI)-based training. Design/methodology/approach A review of relevant research papers, articles and case studies is done to highlight developments in research and practice. Findings AI-based training systems are smart, intelligent and expert in handling queries. These systems can curate content, grade, evaluate, and provide feedback to trainee, thus making learning adaptive and contextual. Practical implications Application of AI is vital in the field of training, as it helps personalization and customization of training programs to increase the effectiveness of training. Originality/value Executives and researchers can save time by reading relevant information on the linkage, and its contribution to AI is discussed and summarized in an easy to read format.

Artificial intelligence and policy: quo vadis?

2019 ◽  
Vol 21 (3) ◽  
pp. 238-263 ◽  
Author(s):  
Anastassia Lauterbach
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Fast Rate ◽  
Content Type ◽  
Social Governance ◽  
Board Directors ◽  
Quo Vadis ◽  
The Many ◽  
Practical Implications

Purpose This paper aims to inform policymakers about key artificial intelligence (AI) technologies, risks and trends in national AI strategies. It suggests a framework of social governance to ensure emergence of safe and beneficial AI. Design/methodology/approach The paper is based on approximately 100 interviews with researchers, executives of traditional companies and startups and policymakers in seven countries. The interviews were carried out in January-August 2017. Findings Policymakers still need to develop an informed, scientifically grounded and forward-looking view on what societies and businesses might expect from AI. There is lack of transparency on what key AI risks are and what might be regulatory approaches to handle them. There is no collaborative framework in place involving all important actors to decide on AI technology design principles and governance. Today's technology decisions will have long-term consequences on lives of billions of people and competitiveness of millions of businesses. Research limitations/implications The research did not include a lot of insights from the emerging markets. Practical implications Policymakers will understand the scope of most important AI concepts, risks and national strategies. Social implications AI is progressing at a very fast rate, changing industries, businesses and approaches how companies learn, generate business insights, design products and communicate with their employees and customers. It has a big societal impact, as – if not designed with care – it can scale human bias, increase cybersecurity risk and lead to negative shifts in employment. Like no other invention, it can tighten control by the few over the many, spread false information and propaganda and therewith shape the perception of people, communities and enterprises. Originality/value This paper is a compendium on the most important concepts of AI, bringing clarity into discussions around AI risks and the ways to mitigate them. The breadth of topics is valuable to policymakers, students, practitioners, general executives and board directors alike.

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