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).
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 This issue aims to host original, unpublished, and breakthrough concepts in optics that make use of new tools and mechanisms, such as artificial intelligence, to solve complex problems for applications in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.
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.
Landslides pose a serious risk to population, property, and environment in mountainous regions and even in flat areas worldwide. Landslides have caused massive casualties and significant losses and damage to property. In recent years, machine learning (ML) techniques, including deep learning methods, have increasingly been used to model complex landslides. Analyses so far have demonstrated promising predictive ability compared to traditional, deterministic solutions, and physical model testing. This Special Issue of Applied Sciences seeks to incorporate the latest developments in machine learning with respect to modeling and prediction of landslide susceptibility, including quantitative and qualitative assessments of the classification, volume (or area) and spatial distribution of landslides, as well as the velocity, intensity, and runout (and consequences) of existing or potential landsliding.
A special issue of Geophysical Prospecting is being planned on machine learning applications in geophysical exploration and monitoring. Artificial intelligence, and in particular its subdomain machine learning, has revolutionized many science and engineering disciplines during the past decade. In many domains such as image recognition, machine translation, and speech analysis, machine learning outperforms conventional techniques and has emerged as the method of choice. It is no surprise that recently geophysicists have also found great value in machine learning to automate workflows, extract valuable information from big data, and create new pathways in solving challenging computational problems. Despite this surge in interest, we are still in the early days of developing machine learning applications for subsurface resource exploration, and the geophysical community at large will benefit from a better understanding of the promise of machine learning in transforming industrial practices.
This trend also brings about a unique opportunity and good assurance for solving different critical problems in medical and healthcare systems as well as engineering applications of Artificial Intelligence (AI) and Operations Research (OR). However, such an assurance strongly depends on the extent to which researchers can discover useful patterns, find informative mechanisms underlying the fragmented and diverse data sets, as well as convert this knowledge into intelligent decisions. AI techniques have been recently studied and applied as promising tools for the development and application of intelligent systems in the healthcare context. AI-based systems can generally learn from data and evolve according to real-time changes and fluctuations by considering the indisputable uncertainty of health data and processes. Many attempts have been made so far that employ different techniques including, inter alia, Machine Learning (ML), neural networks, optimization, computational intelligence and human–machine interface.
This entry is a part of the NYU Center for Data Science blog's recurring guest editorial series. Irina Espejo Morales is a CDS Ph.D. student in data science and also a DeepMind fellow. Kyle Cranmer is a CDS professor of data science and professor of physics at the NYU College of Arts & Science. Lukas Heinrich is a staff scientist at CERN working with the ATLAS experiment at the LHC and former NYU graduate student. Gilles Louppe is an associate professor in artificial intelligence and deep learning at the University of Liège (Belgium) and former Moore Sloan fellow.