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.

In this paper, we take a city’s budget, which represents the resources that need to be allocated, and test how many blockchain users need to join a voting process of how the city’s resources should be allocated in order to best represent their preferences. This voting process can be tracked very well through the utilization of IoT and smart technology in a smart city. Therefore, we showed that the budget resource allocation of a smart city can be significantly optimized through the utilization of blockchain technology. We found that just a tiny fraction of 0.12% of the population of blockchain participants is needed to significantly represent the spending behavior of the total population. This has significant implications as it shows the strength and importance of a required blockchain in a smart city and its minimal energy consumption requirements.