Artificial intelligence (AI) is motivating the automation of processes and services, being recently used as a way to interact directly with customers in frontline services (Belanche et al., 2020a). AI constitutes a major source of innovation (Huang and Rust, 2018), with a potential for disruption particularly high in services (Bock et al., 2020). As a result, there is an increasing interest in implementing automated forms of interaction in services (Paluch et al., 2020; Flavián et al., 2021), and this trend is not different in the tourism, leisure and hospitality industry. The use of AI and autonomous robots to perform different tasks in this context is continuously increasing (Ivanov and Webster, 2019; Tussyadiah, 2020; Belanche et al., 2020b), which is reshaping the service and affecting experiences and relationships with customers. In addition, service automation may have a great impact on customer choices (Van Doorn et al., 2017) and behaviors (Grewal et al., 2017).
From commerce, finance and agriculture to self-driving cars, personalised healthcare and social media – advancements in artificial intelligence (AI) unlock countless opportunities. New applications promise to improve the quality of people's lives throughout the world, but at the same time, raise a number of societal questions. A joint panel discussion of the German National Academy of Sciences Leopoldina and the Korean Academy of Science and Technology (KAST) explores AI technologies, their benefits and their challenges for society. Virtual panel discussion of the German National Academy of Sciences Leopoldina and the Korean Academy of Science and Technology „Realizing the Promises of Artificial Intelligence" Thursday, 25 November 2021, 8am to 9am (CET) Online Following opening remarks from the President of the Leopoldina, Prof (ETHZ) Dr Gerald Haug and Prof Min-Koo Han, PhD, President of the KAST, legal scholar Prof Ryan Song, PhD, Kyung Hee University, Seoul/South Korea, will provide an introduction into the topic. Subsequently, computer scientist Prof Alice Oh PhD, KAIST School of Computing, Daejeon/ South Korea, and Member of the Leopoldina Prof Dr Alexander Waibel, Karlsruhe Institute of Technology/Germany and Carnegie Mellon University, Pittsburgh/USA, will provide input statements for further discussion.
Intelligent vehicle (IV) is a comprehensive system that integrates functions such as environment perception, planning, and decision making, and multi-level assisted driving. It concentrates on the technologies of computers, modern sensing, information fusion, communication, artificial intelligence, and automatic control, etc. The improvement of the intelligence level of IV can enhance traffic safety and efficiency effectively. In recent years, with the development of hardware and software, the technology of Intelligent Connected Vehicle (ICV) has achieved rapid progress. However, there are many critical and difficult issues that remain to be addressed.
This Special Issue is devoted to the new trends in optics applied to Information and Communication Technologies (ICT). This issue aims to host original, unpublished, and breakthrough concepts in optics that make use of new tools and mechanisms, such as artificial intelligence, to solve complex problems for applications in ICT. Optical systems use communication and information processing. To name a few large fields, we enumerate telecommunications (fiber optics, etc.), information processing (optical and quantum computing, etc.), sources of light (VCSEL, etc.). Manuscripts should be submitted online at www.mdpi.com
With the emerging opportunities of artificial intelligence (AI), learning and teaching may be supported in situ and in real-time for more efficient and valid solutions. Hence, AI have the potential to further revolutionise the integration of human and artificial intelligence and impact human and machine collaboration during learning and teaching (Seeber et al., 2020; Wesche & Sonderegger, 2019). The discourse around utilisation of AI in education shifted from being narrowly focused on automation-based tasks to augmentation of human capabilities linked to learning and teaching (Chatti et al., 2020). As such, AI systems are capable of analysing large datasets, including unstructured data, in real-time, and detect patterns or structures that can be used for intelligent human decision-making in learning and teaching situations (Baker, 2016). This special issue will address the reciprocal issues when augmenting human intelligence with machine intelligence in K-12 and higher education.
Pioneer Investors – Next Sunday, the 41st edition of GITEX Technology Week starts and continues for 5days (17 – 20 October 2021) in the World Trade Center in Dubai. GITEX Technology Week is the most prominent technical event that has been held for 41 years and brings different groups of industry leaders, emerging companies, and major players in the technological development world under one roof. GITEX is the only event that highlights the most prominent technical visions on a large scale around the world, including the latest technologies in artificial intelligence, 5G communications, cloud computing, big data, digital security, blockchain technologies, quantum computing, immersive marketing technologies, and financial technology. GITEX is also considered as one of the most important technology exhibitions with the participation of the largest technology companies, governmental agencies, and startups to present the latest innovations from around the world. A wide range of established and emerging technology companies working in 26 different sectors will participate in GITEX 2021 activities include training sessions in the field of deep learning, workshops, and live presentations that contribute to shaping a glorious future for the digital market.
Joshua Zamora is a Premium Seller with JVZoo and has a well-established affiliate marketing career. Born and raised in Miami, Florida he is the creator of several products and an affiliate for many more. In today's interview, you'll learn how the simple act of flipping channels on the TV planted the seed that led to Joshua Zamora's online success.
In this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields. It is a great joy to see that the SCSS (Symbolic Computation in Software Science) conference series, this year, experiences its 9th edition. A big Thank You to the organizers, referees, and contributors who kept the series going over the years! The series emerged from a couple of meetings of research groups in Austria, Japan, and Tunisia, including my Theorema Group at RISC, see the home pages of the SCSS series since 2006. In 2012, we decided to define "Symbolic Computation in Software Science" as the scope for our meetings and to establish them as an open conference series with this title. As always, when one puts two terms like "symbolic computation" and "software science" together, one is tempted to read the preposition in between - in our case "in" - as just a set-theoretic union. Pragmatically, this is reasonable if one does not want to embark on scrutinizing discussions. However, since I was one of the initiators of the SCSS series, let me take the opportunity to explain the intention behind SC in SS in this note. Also, this note, for me, is a kind of revision and summary of thoughts I had over the years on the subject of SCSS and related subjects.
The Industry 4.0 paradigm has been characterized by greater connectivity between networks of digitalized manufacturing systems. The application of enabling technologies, including automation and cyber-physical systems, has supported smart manufacturing and decentralized decision making. The implications of Industry 4.0 technologies are significant, leading to reduced production time and cost, while improving product quality. The challenges include how to analyze, exchange, and securely manage the vast amounts of data generated between manufacturing systems. These challenges have spurred growth in research areas including additive manufacturing, Artificial Intelligence, collaborative robotics, digital manufacturing, Internet of Things, machine learning, Big Data analytics, virtual and augmented reality, as well as many others.