Artificial Intelligence (AI) and Machine Learning (ML) dominate numerous industries, right from design & technology to the academic world. A few years ago, these were the futuristic terms used in every domain to comprehend that computer applications will outperform humans and replace them in performing complex tasks with their intellect. Nevertheless, these two futuristic terms are now used in everyday life, playing a pivotal role in every sector, including agriculture, transportation, satellites, food processing, digital payments, media, computer chips, e-commerce, and more.
Similarly, the world of academic publishing is not distant from its impact. AI and ML will surely be the next big thing that completely transforms the scientific arena. The publishing industry is gradually adopting AI and ML by helping researchers, scholars, and publishers to mitigate problems associated with peer-review, plagiarism, identifying data fabrication, and more. In addition, the use of AI is enhancing the automation of research methods, right from constructing theories to performing experiments, analyses, and assessments. Due to the high influx of scholarly journal articles and further development of interdisciplinary research, a plethora of literature, data, and content has to be consumed by researchers and scholars in a short time. Thus, AI comes to the rescue via the deep learning platforms that can perform analysis and understand the paper's nature (whether it is supporting or contradictory through article citations). Further, it helps accumulate essential data sets related to the research papers, which is a tedious task to find out manually.
AI and ML are all about consuming vast sets of data, and the more the amount of data it receives, the better will be the result. Moreover, their development depends solely on the quantity and quality of data. In this case, the publishing industry can effortlessly tap into the enormous potentials of AI and ML because of the widespread research findings that help to stride ahead in the future. Nevertheless, the academic arena needs a massive investment of capital and time in developing & training the systems to bring them into action.
As the scope for AI and ML is quite extensive, the following pointers explain why it is the next big thing for academic publishing.
Let us get started!
Introduction to AI and ML
First, let us understand the true meaning of these two terms and their role in the publishing world. Though AI and ML are different in their essential nature, people often use them interchangeably.
Artificial Intelligence is a concept that stimulates and replaces human intelligence with machines (computer or digital systems) to perform complicated tasks, learn and manage massive quantities of data. Further, AI requires a strong base of specialized and advanced software for writing, compiling, and feeding algorithms or programs. The entire AI system starts working when we feed it with vast amounts of labeled training data. Then, it starts analyzing the ingested data for correlations, guidelines, patterns and employs them to predict and perform the tasks. Another subset or application of AI is Natural Language Processing (NLP), which helps build systems to understand and process a language.
Whereas Machine Learning is a branch and application of AI which enables a system to learn and constantly improve accuracy by accessing large sets of data and algorithms. Doing so facilitates the development and up-gradation of programs and applications. Some even refer to machine learning as a necessary element and prerequisite in realizing the genuine concept of AI. The primary aim of ML is to minimize human assistance and intervention through deep learning constantly.
Present Scenario of Publishing Industry
The publishing industry is at a critical juncture from where it can advance to the next level with the help of new technologies such as AI. Due to the growth of academic publishing and increased scholarly output in the last few years, sufficient data is available to feed and train AI systems. Thus, the top publishers have started investing in the research and development of AI-related software systems. They are upgrading themselves from traditional publishing to a data-driven technology system. The focus is on acquiring sophisticated AI software systems to analyze text, search content, semantic search, peer-reviews, statistical results, and plagiarism in research papers.
For example, Elsevier, which publishes scientific and medical information journals, has changed its course towards data-related operations. It has a rich publishing history of more than 140 years and massive data for machine learning.
Potentiality of AI and ML in Transforming Academic Publishing Industry
It is evident that the publishing industry has been experiencing various changes and challenges in the last few years. Now, it is the right time to transform by utilizing powerful technology like artificial intelligence swiftly. For an AI system to work efficiently and prognosticate, publishers must train it on extensive research data and algorithms. Since data is the core of every AI operation, data quality determines the accuracy of AI's predictions and results. The publishing sector has massive potential for growth in the forthcoming years, and many publishers have already started fostering AI systems. So, they have begun using Natural Language Processing (NLP) to provide semantic enrichment and content recommendations to the readers. Also, the publishers are exploring new avenues of business growth, new markets for their journals, and improving their existing research works with the aid of XML. They are bringing simplified publishing processes & mechanisms to target more submissions and authors. Further, they are also planning to reduce the overall editorial cost that involves staff's salary, distribution and printing fees, editing, proofreading, plagiarism checks, and sending the papers for peer review.
Significance of AI and ML in Academic Publishing
Content Curation and Interpretation are the two main areas that commonly use AI. The prominent role of AI and ML is identifying new peer reviewers from different web sources, tracking down plagiarism & intentional data fabrication, scanning text for incorrect reporting and inaccurate statistics & data.
As far as content curation is concerned, AI and machine learning help publishers and institutions organize the data, content, and research findings; and swiftly deliver them to the readers. Content curation helps create different groupings and sets of content associated with a particular niche. Further, these groupings facilitate recommendations based on previous readings.
AI and ML bridge research work with other authors in the same field regarding content interpretation. Likewise, the adaptation of AI is growing and paving the way for the development of many start-ups.
Implementation of AI and ML by startups to address the woes of Scholarly Publishing industry
AI-enabled start-ups in academia use a deep learning platform to assist researchers and other stakeholders in myriad ways. To be precise, it helps researchers analyze article citations and check if they support or contradict their research work. It also helps examine digital research piles and identify essential findings to strengthen the manuscript.
Scientific communication is transforming continuously through the evolution of new technologies in the scholarly publishing landscape. This includes academic search engine optimization that deals majorly by ranking articles higher on search engines based on keywords. In addition, the usage of machine learning to generate statistical and methodological reviews for scientific manuscripts.
Also, academia witnessed the evolution of multiple start-ups that are focussed only on the fragmented processes of the publishing lifecycle and not deliberately focused on providing full-length publishing solutions. For example, they focus solely on peer-review or editing or academic search engine optimization, and so on.
But, the invention of SciSpace (Formerly Typeset) in 2015 - intended to offer a complete journal management platform to help the researchers both write and submit their manuscripts on a single web authoring platform. Also, it automatically converts files to XML, PDF, ePUB, and HTML within no time and saves both time and production costs. So far, SciSpace can be considered as an AI-empowered research writing tool or software.
Limitations of AI applications lately
There are several limitations in the AI application subjecting to the publishing industry. Most importantly, it simply rejects the latest or new insights because the data used for training is from the current sciences. Suppose we ingest data and algorithms focusing only on the findings and results from one field. In that case, the AI system will be incapable of performing a robust content interpretation (a critical component of AI). Thus, it fails to understand papers from two different fields as they train on data for identifying content from only one area.
The other most concerning factor is human bias, which can affect adversely. Human biases tend to crawl into the data-modeling processes while feeding data into the AI application, and the biased data can harm the whole publishing process. On top of that, the entire data-modeling process, data mining, storage, and analysis consumes much energy and requires massive investment to support it in the long run. Though AI can be more intelligent than humans, there are still multiple things that only humans can do and perform. It includes different levels of data interpretations and a deep understanding of the subject matter, which only a researcher gains after years of reading, hard work, and failures.
What future lies ahead for AI-empowered Publishing industry
The publishing industry's future lies in the foreseeable automated publishing system where the AI-enabled system will perform complete subject-oriented reviews of manuscripts, have automated methodological studies, and decide to publish the paper on its own without human interference. From submitting the manuscript to publishing in the journal, the whole process would be over at the drop of a hat, enabling superfast sharing of research findings and reducing the time consumed. Thus Al-enabled publishing will embrace and enhance the overall scientific communication.
The overall impact could be measured based on the capacity to publish high-quality academic works and greater scholarly output. There are myriad opportunities for scientific journals to use AI and help researchers correct unintentional errors and mistakes. It includes focusing on wrong research questions/topics, sampling and sample frame errors, and concerns regarding the reproduction of the research work.
At the same time, AI will enable researchers and publishers to identify institutions and journals that do not adhere to established norms and appropriate standards. Further, human bias will also be minimalized as the human intervention will slowly fade away from the publishing process.
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Conclusion
There is no denying that AI and Machine Learning is the big thing that is slowly taking over the academic publishing industry. The scope and possibilities for growth are immense as it is a fast-changing technology. It might take a considerable amount of time to emerge as an AI-enabled, fully automated publishing system. However, there are many areas where artificial intelligence can not surpass human intelligence. As AI systems are being created and trained by humans only, so the possibility of bias, prejudice, and unfairness is supreme. Therefore, chances of shortcomings would be higher in the AI system and impact the overall predictions and results. At this moment, AI and machine learning are apt tools that can fuel the growth of the publishing industry, but we need to move forward cautiously.
