Framework kognitive Produktionsarbeit 4.0/Framework for Cognitive Production Work 4.0 – Conceptualization and Model Approach for the Production System of the Future within the Future Work Lab

2020 ◽  
Vol 110 (03) ◽  
pp. 108-112
Author(s):  
Simon Schumacher ◽  
Bastian Pokorni
Keyword(s):  
Artificial Intelligence ◽  
Production System ◽  
Research Agenda ◽  
Reference Model ◽  
Work Organisation ◽  
Research Topics ◽  
Interdisciplinary Project ◽  
The Future ◽  
Future Work ◽  
Model Approach

Das Future Work Lab ist ein Innovationslabor für Arbeit, Mensch und Technik am Standort Stuttgart mit Fokus auf Künstlicher Intelligenz (KI) und vernetzter Arbeitsorganisation. Ein zentraler Bestandteil ist das Framework kognitive Produktionsarbeit 4.0, das als Referenzmodell für das Themenfeld Produktionsarbeit 4.0 dienen soll. Ein entsprechendes Konzept wurde in einem interdisziplinären Projektteam entwickelt. In diesem Beitrag wird das Grobmodell vorgestellt und die weitere Forschungsagenda präsentiert.   The Future Work Lab is an innovation lab for work, people and technology in Stuttgart, Germany with a focus on artificial intelligence and interconnected work organisation. A key component consists of the framework for cognitive production work 4.0, which will serve as a reference model for the research topics. A corresponding concept was developed in an interdisciplinary project team. In this article the raw model is introduced and the further research agenda is presented.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

scholarly journals How artificial intelligence will change the future of marketing

2019 ◽  
Vol 48 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Thomas Davenport ◽  
Abhijit Guha ◽  
Dhruv Grewal ◽  
Timna Bressgott
Keyword(s):  
Artificial Intelligence ◽  
Research Agenda ◽  
Marketing Strategies ◽  
Important Policy ◽  
The Future ◽  
Task Types ◽  
The Impact ◽  
Future Of Marketing

Abstract In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.

Study on the Transformation from CP-Nets to Game Theory

2011 ◽  
Vol 268-270 ◽  
pp. 1750-1754
Author(s):  
Zhao Wei Liu ◽  
Jing Lei Liu ◽  
Xue Jiao Sun
Keyword(s):  
Artificial Intelligence ◽  
Game Theory ◽  
Nash Equilibrium ◽  
Optimal Outcome ◽  
Concept Model ◽  
Preference Representation ◽  
Essential Question ◽  
The Future ◽  
Future Work

Researching preference is significative in artificial intelligence. The paper shows the concept and example of CP-nets on preference representation firstly. And then game theory with strategy preference is proposed. For the nature relation between CP-nets and game theory on preference, this paper attempts to transform the question of CP-nets to game theory on three points which is concept, model and essential question(optimal outcome and Nash equilibrium) and the proves are given on the heel. Finally, the future work on relation and equivalence of CP-nets and game theory is presented.

scholarly journals Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations

2020 ◽  
Vol 9 (3) ◽  
pp. 394-396
Author(s):  
Chioma A Ikedionwu ◽  
Deepa Dongarwar ◽  
Korede K Yusuf ◽  
Sitratullah O. Maiyegun ◽  
Sahra Ibrahimi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Child Health ◽  
Maternal And Child Health ◽  
Research Agenda ◽  
Vaccine Development ◽  
Nominal Group Technique ◽  
Nominal Group ◽  
Research Topics ◽  
And Child Health ◽  
Group Technique

As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. This was achieved using the Nominal Group Technique through which researchers identified and ranked 12 research topics across various disciplines relating to MCH in the setting of COVID-19. Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US. Key words: • COVID-19 • Coronavirus • Pandemics • Maternal and child health • MCH • Big data • Artificial intelligence   Copyright © 2020 Ikedionwu et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.

scholarly journals Artificial intelligence for software development — the present and the challenges for the future

2019 ◽  
Vol 68 (1) ◽  
pp. 15-32 ◽  
Author(s):  
Łukasz Korzeniowski ◽  
Krzysztof Goczyła
Keyword(s):  
Artificial Intelligence ◽  
Software Development ◽  
Code Generation ◽  
System Structure ◽  
Research Fields ◽  
Levels Of Detail ◽  
First Case ◽  
The Future ◽  
Software Modules ◽  
Future Work

Since the time when first CASE (Computer-Aided Software Engineering) methods and tools were developed, little has been done in the area of automated creation of code. CASE tools support a software engineer in creation the system structure, in defining interfaces and relationships between software modules and, after the code has been written, in performing testing tasks on different levels of detail. Writing code is still the task of a skilled human, which makes the whole software development a costly and error-prone process. It seems that recent advances in AI area, particularly in deep learning methods, may considerably improve the matters. The paper presents an extensive survey of recent work and achievements in this area reported in the literature, both from the theoretical branch of research and from engineer-oriented approaches. Then, some challenges for the future work are proposed, classified into Full AI, Assisted AI and Supplementary AI research fields. Keywords: software development, artificial intelligence, machine learning, automated code generation

Recycling. The Future Work for the Recycling of Steel.

1997 ◽  
Vol 113 (12) ◽  
pp. 986-988
Author(s):  
Shozo MIZOGUCHI
Keyword(s):  
The Future ◽  
Future Work

On Sinofuturism

Screen Bodies ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 46-62
Author(s):  
Yunying Huang
Keyword(s):  
Artificial Intelligence ◽  
Pop Culture ◽  
Chinese People ◽  
Modern Chinese ◽  
The Future ◽  
Dominant Design ◽  
Urban Rural ◽  
Cultural Expressions ◽  
Contemporary Chinese

Dominant design narratives about “the future” contain many contemporary manifestations of “orientalism” and Anti-Chineseness. In US discourse, Chinese people are often characterized as a single communist mass and the primary market for which this future is designed. By investigating the construction of modern Chinese pop culture in Chinese internet and artificial intelligence, and discussing different cultural expressions across urban, rural, and queer Chinese settings, I challenge external Eurocentric and orientalist perceptions of techno-culture in China, positing instead a view of Sinofuturism centered within contemporary Chinese contexts.

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