Successful Use Case Applications of Artificial Intelligence in the Steel Industry

2019 ◽  
Author(s):  
L. Ometto ◽  
S. Challapalli ◽  
M. Polo ◽  
G. Cestari ◽  
A. Villagrossi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Steel Industry ◽  
Use Case

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

scholarly journals Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 235
Author(s):  
Paulo Garcia ◽  
Francine Darroch ◽  
Leah West ◽  
Lauren BrooksCleator
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Issues ◽  
Social Computing ◽  
Social Systems ◽  
Human Interaction ◽  
Data Driven ◽  
Vulnerable Groups ◽  
Use Case ◽  
Ongoing Research

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

scholarly journals Artificial Intelligence in Healthcare: Validating an AI Algorithm in Health Institutions in the COVID-19 Pandemic (a Use Case)

2021 ◽  
Author(s):  
Иван Андреевич Блохин ◽  
Сергей Павлович Морозов ◽  
Валерия Юрьевна Чернина ◽  
Анна Евгеньевна Андрейченко ◽  
Ислам Висханович Шахабов ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Predictive Value ◽  
Healthcare Access ◽  
Use Case ◽  
Public Health Action ◽  
Health Action ◽  
Automated Data Analysis ◽  
New Challenges ◽  
Health Institutions ◽  
Improve Access

The paper considers new challenges related to public health. Action is needed to improve access to healthcare while maintaining its quality. The introduction of AI-based automated data analysis systems can be a solution to that. The present study seeks to assess the use of AI in outpatient care to detect pathological changes in the lungs typical of a coronavirus amidst the pandemic. The sample size was 600 patients. The results were statistically and analytically processed. The sensitivity attained 94%; the specificity, accuracy and the area under the ROC curve were 77%, 83%, and 87%, respectively. The negative predictive value was 97%; the positive predictive value was 66%. The data obtained show that the algorithm separates the CT scan results having no abnormalities in the lungs. The authors conclude that the usage of AI technologies helped to improve diagnostic accuracy during the COVID-19 pandemic. Artificial intelligence algorithms can also work with patients in non-pandemic times, thus improving healthcare access.

scholarly journals The Use of Artificial Intelligence in Industry 4.0

2020 ◽  
Vol 73 ◽  
Author(s):  
Maksim Sharabov ◽  
Georgi Tsochev
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Industry 4.0 ◽  
New Technologies ◽  
Web Of Science ◽  
Predictive Analytics ◽  
Use Case ◽  
Industrial Robotics ◽  
The Past ◽  
New Generation

This article presents a brief overview of the effect of new technologies, how they are changing the manufacturing process, and how the machines are starting to get a lot smarter thanks to the artificial intelligence. The focus is over the examination of Industry 4.0 and how it revolutionized the whole manufacturing segment and what promise of a better, more efficient future it brings. This analysis focuses primarily on how artificial intelligence is integrated, what benefits it brings, and how big of an improvement it is over basic programming. Part of the research is based on 771 publications tracked over the past three to five years. Publications are within some of the well-known databases Scopus, Web of Science, and IEEE. We will examine the basic use case scenarios where AI is crucially needed and how a new generation of the factory can look and feel like a living human being. Keywords: Industry 4.0, artificial intelligence, predictive analytics, predictive maintenance, industrial robotics, computer vision.

scholarly journals Cognitive capabilities for the CAAI in cyber-physical production systems

2021 ◽  
Author(s):  
Jan Strohschein ◽  
Andreas Fischbach ◽  
Andreas Bunte ◽  
Heide Faeskorn-Woyke ◽  
Natalia Moriz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Cognitive Architecture ◽  
Use Case ◽  
Test Functions ◽  
Model Quality ◽  
Implementation Effort ◽  
Data Platform ◽  
Cyber Physical Production Systems ◽  
The Individual

AbstractThis paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.

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