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Perspectives and Challenges of AI Techniques in the Field of Social Sciences and Communication

Raul Ramos Pollán

Abstract


In the past decade, the methods and technologies of artificial intelligence (AI) have made great progress. In many cases, they have become part of the usual landscape of solving new or old problems in different fields of human knowledge. In this progress, there are several aspects, especially three aspects: the availability and universality of data in many fields of human activities; a deeper understanding of the mathematics of the basic control algorithm; and the availability and capability of hardware and computing which allows a wide range and a large number of data experiments. Considering these aspects, the key challenge for each problem and application area is to understand how to use these technologies, to what extent they may reach, and what constraints need to be overcome in order to obtain beneficial results (in terms of production cost, value, etc.). This challenge includes identifying data sources and their integration and recovery requirements, the necessity and cost of acquiring or constructing tag data sets, volume data required for measurement, verifying its feasibility, technical method of data analysis task and its consistency with the final application goal, and social and communication sciences are no exception. The knowledge in these fields is related to artificial intelligence, but they do have particularities that define the most appropriate type of artificial intelligence technology and method (i.e. natural language processing). The successful use of AI technology in these disciplines involves not only technical knowledge, but also the establishment of a viable application environment, including the availability of data, the appropriate complexity of tasks to be performed, and verification procedures with experts in the field. This paper introduces the methodology of generating artificial intelligence model, summarizes the artificial intelligence methods and services most likely to be used in social and communication sciences, and finally gives some application examples to illustrate the practical and technical considerations in this regard.


Keywords


Artificial Intelligence; Machine Learning; Social Science; Data Science

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DOI: http://dx.doi.org/10.32629/jai.v5i1.504

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