If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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).
Precision medicine requires big data. In order to improve the treatment of individuals with cancer, or to understand rare diseases, scientists and clinicians, as well as AI technologies require access to larger sets of health research data that covers diverse populations and wide ranges of conditions. For AI, more data means a better understanding of diseases, which will lead to more accurate diagnosis and treatment. At the same time, each hospital will only see a relatively small number of individuals with a disease, and even across the province, we have access to only a small portion of the total data available worldwide. To build the large-scale datasets needed to drive forward precision medicine, sharing of data across the country and around the world is critical.
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.
The group, led by Guillaume Cabanac at the University of Toulouse in France, could not understand why researchers would use the terms'counterfeit consciousness', 'profound neural organization' and'colossal information' in place of the more widely recognized terms'artificial intelligence', 'deep neural network' and'big data'. Further investigation revealed that these strange terms -- which they dub "tortured phrases" -- are probably the result of automated translation or software that attempts to disguise plagiarism. And they seem to be rife in computer-science papers. Research-integrity sleuths say that Cabanac and his colleagues have uncovered a new type of fabricated research paper, and that their work, posted in a preprint on arXiv on 12 July1, might expose only the tip of the iceberg when it comes to the literature affected. To get a sense of how many papers are affected, the researchers ran a search for 30 tortured phrases in journal articles indexed in the citation database Dimensions.
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.
Researchers found that 80% of marketers surveyed believe more than 25% of all marketing tasks will be automated in some way during the next five years. Meanwhile, 41% of marketers reported they enjoyed a spike in revenue after adopting AI-powered selling tools. Plus, 56% of marketers surveyed believe that AI will create more marketing jobs than it eliminates during the next decade. The study, dubbed "2021 State of Marketing AI Report Released," is available for free download on the Web. Entrepreneur Dave Kerpen is all-in on auto-text writer Copy.ai.