From the Digital Data Revolution toward a Digital Society: Pervasiveness of Artificial Intelligence

Frank Emmert-Streib
<span title="2021-03-04">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="" style="color: black;">Machine Learning and Knowledge Extraction</a> </i> &nbsp;
Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and responsible usage of such data. In this paper, we discuss these developments and the fields that have been particularly
more &raquo; ... fected by the digital revolution. Our discussion is AI-centered showing domain-specific prospects but also intricacies for the method development in artificial intelligence. For instance, we discuss recent breakthroughs in deep learning algorithms and artificial intelligence as well as advances in text mining and natural language processing, e.g., word-embedding methods that enable the processing of large amounts of text data from diverse sources such as governmental reports, blog entries in social media or clinical health records of patients. Furthermore, we discuss the necessity of further improving general artificial intelligence approaches and for utilizing advanced learning paradigms. This leads to arguments for the establishment of statistical artificial intelligence. Finally, we provide an outlook on important aspects of future challenges that are of crucial importance for the development of all fields, including ethical AI and the influence of bias on AI systems. As potential end-point of this development, we define digital society as the asymptotic limiting state of digital economy that emerges from fully connected information and communication technologies enabling the pervasiveness of AI. Overall, our discussion provides a perspective on the elaborate relatedness of digital data and AI systems.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3390/make3010014</a> <a target="_blank" rel="external noopener" href="">fatcat:4n6em3jedzg37ltdc5eesdxnza</a> </span>
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