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

Raúl Ramos Pollán
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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.
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人工智能技术在社会科学与传播领域的前景与挑战
在过去的十年中,人工智能(AI)的方法和技术取得了很大的进步。在许多情况下,它们已经成为解决人类知识不同领域的新问题或老问题的通常景观的一部分。在这一进展中,有几个方面,特别是三个方面:在人类活动的许多领域中数据的可用性和普遍性;对基本控制算法的数学原理有了更深入的了解;并且硬件和计算的可用性和能力允许进行大范围和大量的数据实验。考虑到这些方面,每个问题和应用领域的关键挑战是了解如何使用这些技术,它们可以达到什么程度,以及为了获得有益的结果(在生产成本,价值等方面)需要克服哪些限制。这一挑战包括识别数据源及其集成和恢复需求,获取或构建标签数据集的必要性和成本,测量所需的数据量,验证其可行性,数据分析任务的技术方法及其与最终应用目标的一致性,社会科学和传播科学也不例外。这些领域的知识与人工智能相关,但它们确实具有定义最合适类型的人工智能技术和方法(即自然语言处理)的特殊性。在这些学科中成功使用人工智能技术不仅涉及技术知识,还涉及建立可行的应用环境,包括数据的可用性,要执行的任务的适当复杂性,以及与该领域专家的验证程序。本文介绍了人工智能模型的生成方法,总结了社会科学和传播科学中最有可能使用的人工智能方法和服务,最后给出了一些应用实例来说明这方面的实际和技术考虑。
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25
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