The Future of Artificial Intelligence and Natural Language Processing

Annotated Bibliography: Natural Language Processing and Artificial Intelligence in the Telecom Industry

Castillo, A. (2021). Natural Language Processing. CPA Journal, 91(6/7), 16–19.

The author is an advisory supervisor and data team member at Marks Paneth LLP who provides a valuable description of natural language processing (NLP) and its current and potential future applications. The author notes that NLP is a subfield of artificial intelligence (AI) which enables computer-based applications to automatically scan, comprehend and extract relevant meanings from human languages. Although its specific uses depend on the industrial setting, NLP technologies allow data analysts to use machine learning to automatically identify textual patterns by analyzing textual data. These types of NLP applications have been shown to be especially useful for forensic analyses of textual data in order to identify potential fraud in financial documentation. The author uses a case study to illustrate how the process works, and emphasizes that the capabilities of NLP continue to expand to a wide array of other types of textual documentation.

Gabriel, A (2019). Artificial intelligence in scholarly communications: An Elsevier case study. Information Services & Use, 39(4), 319–333.

The author is the senior vice president for global strategic networks at Elsevier who describes the proliferation of AI and NLP applications in recent years. In particular, the author cites the use of the AI technologies being developed by Elsevier that are designed to reduce that costs that are associated with human oversight in various industries as well as the various ways that NLP is providing additional “sematic enrichment and content recommendations” for consumers. Despite the significant progress that has been made in developing sophisticated AI programming, however, researchers have still not reached the point where AI can replicate all of the abilities that remain uniquely human in nature but emphasize that such an eventuality may represent yet another existential threat for humankind in the future.

Ma, J., Wu, X., & Huang, L. (2022). The Use of Artificial Intelligence in Literature Search and Selection of the PubMed Database. Scientific Programming, 1–9.

The authors are researchers with the National Natural Science Foundation of China who describe the increasing functionalities and applications for NLP in a wide range of settings, including most especially in the PubMed database which contains more than 22 million publications where keyword searches currently fail to identify all of the most relevant publications that are available on a given topic. The findings that emerged from this study indicate that NLP and other AI techniques can dramatically improve both search comprehensiveness as well as the accuracy of PubMed database searches and the authors conclude that NLP has significant potential to improve these outcomes even further in the foreseeable future.

Patrick, B., & Williams, K. L. (2020). What is artificial intelligence? Journal of Accountancy, 229(2), 1–4.

The authors are an assistant professor of accounting and a senior applications consultant, respectively, who provide a working definition of A) as well as some of the common themes that have emerged in recent years with respect to its applications in the telecom industry. Although one of the overarching definitional criteria for AI is that it must be capable of emulating intelligent human behaviors or performing tasks that required human intelligence in the past, AI must also be able to perform these tasks without constant human oversight and direction. In addition, the authors also note that AI includes other technologies such as NLP, machine learning, image recognition, and robotics and they provide useful definitions and examples of these innovations as well.