Artificial intelligence (AI) is a rapidly growing technological phenomenon that all industries wish to exploit to benefit from efficiency gains and cost reductions. At the macrolevel, AI appears to be capable of replacing humans by undertaking intelligent tasks that were once limited to the human mind. However, another school of thought suggests that instead of being a replacement for the human mind, AI can be used for intelligence augmentation (IA). Accordingly, our research seeks to address these different views, their implications, and potential risks in an age of increased artificial awareness. We show that the ultimate goal of humankind is to achieve IA through the exploitation of AI. Moreover, we articulate the urgent need for ethical frameworks that define how AI should be used to trigger the next level of IA.

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

Digitization is the emerging process in the current transformation of industry. Understanding the role and socio-economic consequences of digitalization is crucial for the way technology is being deployed in each sector. One of the affected sectors is dentistry. This study highlights the current advances and challenges in integrating and merging artificial intelligence (AI), intelligence augmentation (IA), and machine learning (ML) in dentistry. We conduct a comparative analysis to give an overview of which technology is being currently deployed and what role IA and AI will play in dentistry, as AI plays an assistive role in advancing human capabilities. We find that challenges range from AI finding its way into routine medical practice to qualitative challenges of retrieving adequate data input. Other challenges lie in the yet unanswered questions of liability in how to reduce deployment costs of new technology. Given these challenges, we provide an outlook of how future technology can be deployed in daily-life dentistry and how robots and humans will interact, given the current technology developments. The aim of this paper is to discuss the future of dentistry and whether it is AI or IA conquering the modern dentistry era.

This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.

The focus of this paper is to bring to light the vital issue of energy poverty alleviation and how big data could improve the data collection quality and mechanism. It also explains the vicious circle of low productivity, health risk, environmental pollution and energy poverty and presents currently used energy poverty measures and alleviation policies and stresses the associated problems in application due to the underlying dynamics.

We conducted a singular and sectoral vulnerability assessment of ESG factors of Dow-30-listed companies by applying the entropy weight method and analyzing each ESG factor’s information contribution to the overall ESG disclosure score. By reducing information entropy information, weaknesses in the structure of a socio-technological system can be identified and improved. The relative information gain of each indicator improves proportionally to the reduction in entropy. The social pillar contains the most crucial information, followed by the environmental and governance pillars, relative to each other. The difference between the social and economic pillars was found to be statistically not significant, while the differences between the social pillar, respective to the economic and governance pillars were statistically significant. This suggests noisy information content of the governance pillar, indicating improvement potential in governance messaging. Moreover, we found that companies with lean and flexible governance structures are more likely to convey information content better. We also discuss the impact of ESG measures on society and security.