Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition

The SP theory of computing and cognition, described in previous publications, is an attractive model for intelligent databases because it provides a simple but versatile format for different kinds of knowledge, it has capabilities in artificial intelligence, and it can also function like established database models when that is required. This paper describes how the SP model can emulate other models used in database applications and compares the SP model with those other models. The artificial intelligence capabilities of the SP model are reviewed and its relationship with other artificial intelligence systems is described. Also considered are ways in which current prototypes may be translated into an 'industrial strength' working system

An Application-centric Perspective on Industrial Artificial Intelligence

Advances in Artificial Intelligence have made its application increasingly relevant to all types of Information Systems. One area where researchers and practitioners see massive potential is the interface between Artificial Intelligence-empowered Information Systems and industrial processes. This explicit area of Industrial Artificial Intelligence and Industry 4.0 has been a popular topic of recent work and has opened up new research streams and applications. However, given the increasing number of publications, it is difficult to discern where the research field is heading. In our work, we conduct a systematic literature review of 296 scientific articles to provide a comprehensive overview of the current state of Industrial Artificial Intelligence in terms of research streams and application areas. We present both a metadata analysis as well as an application-specific analysis. Our results reveal insights into 14 major application areas as well as several findings on applied algorithms and approaches of Industrial Artificial Intelligence

Knowledge representation on design of storm drainage system

Innovations in applied artificial intelligence : 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004, Ottawa, Canada, May 17-20, 2004Author name used in this publication: Kwokwing Chau2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Issues and Path Selection of Artificial Intelligence Design Talents Training in applied Undergraduate Universities in Smart City

The widespread popularity and application of artificial intelligence technology requires technological innovation, of which talent training is an important content. The lack of professional talents has greatly restricted the development of the artificial intelligence industry to some extent. How to train industrial design talents with comprehensive qualities of artificial intelligence design talents in universities has now become the most important topic. Carry out university teacher training based on the industrial design profession, carry out the collaborative education innovation model based on university-enterprise-government , innovative ability and awareness training curriculum system, and use artificial intelligence talent training goals and curriculum system for the construction and practice of goals. Analyze the current plight of artificial intelligence design talent training in applied general universities, clarify the current types and status quo of artificial intelligence design talents, and propose specific ways to solve the current artificial intelligence design talent training. There are few relevant talents for artificial intelligence design professionals who can combine their ideas and technology in actual production. The lack of design talents has greatly limited the development of their industries to some extent. The current application-oriented undergraduates Colleges and universities should explore specific paths for the training of artificial intelligence design talents, and the construction and practice of related curriculum systems should also be gradually revised during practical exploration, so as to realize innovative education through educational innovation

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Cooperation in Industrial Systems

ARCHON is an ongoing ESPRIT II project (P-2256) which is approximately half way through its five year duration. It is concerned with defining and applying techniques from the area of Distributed Artificial Intelligence to the development of real-size industrial applications. Such techniques enable multiple problem solvers (e.g. expert systems, databases and conventional numerical software systems) to communicate and cooperate with each other to improve both their individual problem solving behavior and the behavior of the community as a whole. This paper outlines the niche of ARCHON in the Distributed AI world and provides an overview of the philosophy and architecture of our approach the essence of which is to be both general (applicable to the domain of industrial process control) and powerful enough to handle real-world problems

Taming Data Quality in AI-Enabled Industrial Internet of Things

We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We present the design and deployment of our approach from an AI engineering perspective using two industrial case studies.acceptedVersio

the admissibility of AI- generated evidence

Durante as duas últimas décadas, a Inteligência Artificial tornou-se uma presença constante nas nossas vidas. Ao impactar setores relevantes da sociedade, tem relevando o seu caráter disruptivo, sendo um dos motores impulsionadores da Quarta Revolução Industrial. A Inteligência Artificial além dos seus presentes benefícios para a humanidade, promete soluções inovadoras para os problemas que afligem a sociedade contemporânea, porém a mesma comporta uma duplicidade de efeitos. Os sistemas de Inteligência Artificial pela sua capacidade de monitorizar o seu ambiente circundante, e autonomamente recolher, processar dados, aprender e agir, podem concretizar riscos para os direitos fundamentais, principalmente no contexto da justiça criminal. Esta análise irá focar-se nas especificidades dos sistemas dotados de Inteligência Artificial, aprofundando a temática da admissibilidade da prova gerada por Inteligência Artificial no quadro probatório do Direito Processual Penal Português à luz dos direitos de defesa do arguido e dos seus princípios que norteadores.During the last two decades Artificial Intelligence became ubiquitous in our lives. Revealing itself as a disruptive technology, it is already impacting important sectors of society, being a driver of the Fourth Industrial Revolution. Artificial Intelligence is benefiting humanity, and promises innovative solutions to modern-life problems, nevertheless it has a twofold effect. Artificial Intelligence as systems that are capable to monitor their surrounding environment, autonomously collect and process data, learn and act, may constitute harm to fundamental rights, mainly when deployed to criminal justice. This analysis will focus on the specificities of Artificial Intelligence systems, delving into the admissibility of AI-generated evidence in the Portuguese criminal evidentiary framework in light of the defence rights and structuring principles of Portuguese criminal procedure

Green supply chain based on artificial intelligence of things (AIoT)

Purpose: The most important driving force for the IoT is artificial intelligence. The dramatic growth of the Internet of Things in various fields necessitates the use of artificial intelligence capabilities in the optimal use of data. By combining these technologies, it reduces cost, automation and productivity more dynamically. This hybrid technology is called artificial intelligence of things (AIoT). Methodology: Intelligent solutions in the supply chain, i.e. the use of the Internet of Things with the capability of artificial intelligence, has been able to make various industries great. Findings: Due to the colorful role of IoT technology in the sustainability of industrial systems, this paper provides a framework for the implementation of an AIoT-based green supply chain. This framework shows a clear path to understanding the impact of this hybrid supply chain technology. Originality/Value: In his paper, a framework for the implementation of an AIoT-based green supply chain is provided

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities