Assignment #4 The use of AI in publications

Al definition and types in general

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks — such as discovering proofs for mathematical theorems or playing chess — with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, voice or handwriting recognition, and chatbots.

Learning in AI can fall under the types “narrow intelligence,” “artificial general intelligence,” and “super.” These categories demonstrate AI’s capabilities as it evolves — performing narrowly defined sets of tasks, simulating thought processes in the human mind, and performing beyond human capability. Then, there are four main types of AI as defined by Arend Hintze, researcher and professor of integrative biology at Michigan State University. They are as follows:

1. Reactive machines

Reactive machines are AI systems that have no memory and are task specific, meaning that an input always delivers the same output. Machine learning models tend to be reactive machines because they take customer data, such as purchase or search history, and use it to deliver recommendations to the same customers.

2. Limited memory machines

The next type of AI in its evolution is limited memory. This algorithm imitates the way our brains’ neurons work together, meaning that it gets smarter as it receives more data to train on. Deep learning algorithms improve natural language processing (NLP), image recognition, and other types of reinforcement learning.

3. Theory of mind

The first two types of AI, reactive machines and limited memory, are types that currently exist. Theory of mind and self-aware AI are theoretical types that could be built in the future. As such, there aren’t any real world examples yet. If it is developed, theory of mind AI could have the potential to understand the world and how other entities have thoughts and emotions. In turn, this affects how they behave in relation to those around them.

4. Self-awareness

The grand finale for the evolution of AI would be to design systems that have a sense of self, a conscious understanding of their existence. This type of AI does not exist yet. Artificial intelligence and machine learning algorithms are a long way from self-awareness because there is still so much to uncover about the human brain’s intelligence and how memory, learning, and decision-making work.

The use of AI in some fields

Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery, and toys. However, many AI applications are not perceived as AI: “A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it’s not labeled AI anymore,” Nick Bostrom reports. “Many thousands of AI applications are deeply embedded in the infrastructure of every industry.” the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes (Anushree & Sonal, n.d.). For example:

· Finance

· Hospitals and Medicines Heavy Industries

· Online and Telephone Customer Service Transportation

· Telecommunication Toys and Games

· Music

· Aviation

· News, Publishing & Writing

Artificial Intelligence research can make a valuable contribution to the education of human beings. An intellectual problem is solved, at least in many cases, by dividing it into pieces and developing a technique for each sub problem. The sub problems are the same whether it is a computer or a person trying to solve the problem. If a certain technique proves valuable for the computer, it may be helpful for a human problem solver to be aware of the computer‟s methods. Some researchers in cognitive science and education have proposed the idea of intelligent CAI (computer assisted instruction), in which a computer would be programmed as a “tutor” that would observe the efforts of a student in solving a problem. The tutor would know about some of the mistaken ideas people can have about a particular class of problem and would notice a student falling into one of those traps. It could then offer advice tailored to the needs of that individual student. A second educational advantage is indirect but ultimately more important. By deliberately learning to imitate mechanical thinking, the learner becomes able to articulate what mechanical thinking is and what it is not. The exercise can lead to greater confidence about the ability to choose a cognitive style that suits the problem.

The use of AI in scientific publicationsBottom of Form

The use of AI has also been widely used in scientific publications in recent years. An example of one of its uses in conducting data analysis. AI can be used to analyze data in scientific publications, both to identify patterns or trends that might be difficult for humans to recognize, or to extract important information from large data sets. The application of AI in scientific publications continues to grow along with technological advances and the need to manage and understand increasingly large and complex information.

This scientific study is to see whether AI can be used in a scientifically appropriate way. ways to improve the scientific writing process. It’s true, AI reduces writing time but has significant inaccuracies. This last point requires that AI cannot currently be used alone but can be used with careful supervision by humans to help in writing scientific review articles (Kacena et al., 2024).

Ethics and rule in using AI in publications

The use of artificial intelligence (AI) in research offers many important benefits for science and society but also creates some novel and complex ethical issues. While the issues raised by AI use will not necessitate a radical change in the established ethical norms of science, they will require the scientific community to develop new guidance for the appropriate use of AI. In this article, we provide a brief introduction to AI and how it can be used in research, examine some of the ethical issues raised by using AI in research, and offer recommendations for appropriate use of this technology. We recommend that: 1) researchers and software developers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors; 2) researchers should disclose and explain their use of AI in language that can be understood by non-experts; 3) if appropriate, researchers should engage with impacted communities, populations, and other stakeholders concerning the use of AI in research to obtain their advice and assistance and address their interests and concerns; 4) researchers may be liable for misconduct if they intentionally, knowingly, or recklessly use AI to fabricate or falsify data or commit plagiarism; 5) AI systems should not be named authors, inventors, or copyright holders but their contributions to research should be disclosed and described; 6) AI systems should not be used in situations that may involve unauthorized disclosure of confidential information related to human research subjects, unpublished research, potential intellectual property claims, or proprietary or classified research; and 7) education and mentoring in responsible conduct of research should include discussion of ethical use of AI (Resnik, 2023) .

AI-based systems and platforms for publications

Several systems and platforms based on Artificial Intelligence (AI) that can be used in scientific publications such as Semantic Scholar, IBM Watson Discovery, arXiv Sanity Preserver, Meta AI, SciNote, PubPeer, ReadCube, Magpie. These AI-based systems and platforms can help researchers find relevant information, analyze literature, and manage and collaborate in scientific research more efficiently and effectively.

Artificial Intelligence (AI) has vast potential in marketing. It aids in proliferating information and data sources, improving software’s data management capabilities, and designing intricate and advanced algorithms. AI is changing the way brands and users interact with one another. The application of this technology is highly dependent on the nature of the website and the type of business. Marketers can now focus more on the customer and meet their needs in real time. By using AI, they can quickly determine what content to target customers and which channel to employ at what moment, thanks to the data collected and generated by its algorithms. Users feel at ease and are more inclined to buy what is offered when AI is used to personalise their experiences. AI tools can also be used to analyse the performance of a competitor’s campaigns and reveal their customers’ expectations. Machine Learning (ML) is a subset of AI that allows computers to analyse and interpret data without being explicitly programmed. Furthermore, ML assists humans in solving problems efficiently. The algorithm learns and improves performance and accuracy as more data is fed into the algorithm. For this research, relevant articles on AI in marketing are identified from Scopus, Google scholar, researchGate and other platforms (Haleem et al., 2022).

Latest hot issues about AI-assisted publications that is circulating in social media currently

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts (Dwivedi et al., 2023).

Illustration how to use AI in creating introduction part of a scientific paper

Using Artificial Intelligence (AI) in creating the introductory part of a scientific work by going through the stages of selecting an appropriate theme and title, conducting a literature search, writing a paper systematically, analyzing research trends, summarizing literature, identifying research gaps, creating a preliminary draft, stabilizing, and adjusting, and consult papers with colleagues or supervisors. By combining AI technology with scientific research and writing, you can create a strong and informative introduction to a scientific paper. However, make sure to still check and validate the information provided by AI with trusted sources and with guidance from peers or mentors.

Many journals have already instituted policies regarding the use of AI in scientific paper writing and publishing. It is important to realize that the issues concerned overlap the same issues regarding the use of human assistance in paper writing (Ciaccio, 2023).

1. Level of assistance

There are different levels of human and AI assistance in scientific paper writing. At the basic level would be the automated or human version of spell checking. Manuscripts submitted for publication should be spell-checked. If someone other than the authors, or some program, spell checks your article, there is no need to acknowledge this in the manuscript. Likewise, there is no need to acknowledge grammar checking. However, at the editing level, acknowledgement may be needed. Originally, when only human editing was possible, acknowledgement of such assistance in the manuscript was often required by the publisher. Now with the possibility of AI doing the editing, this requirement has, for many journals, been updated to include AI.

2. Unethical practices

Plagiarism deprives the original authors of credit for their work. Likewise, if one copies from the AI program verbatim without attribution, it is a form of plagiarism. Paraphrasing without attribution is also unethical. The paraphrasing can be done by the authors themselves, by another person, or now, by an AI system.

3. English enhancement

The main language of science is English, and those who are not wellversed in English can be at a disadvantage. AI systems for polishing text are now recommended by some journals during the initial stage of the manuscript submission process, prior to the actual submission.

4. Reviewing

Use of AI for review of submissions is tantalizing in the sense that some journal submissions have no immediate takers to review them, whether it is because the topic is beyond the expertise, it may not seem interesting, perhaps the reviewer candidates think it has already been published in one form or another, they may be too busy, or maybe the English grammar is not highly polished. Even when many candidate reviewers accept to review, there is no guarantee that they will actually complete the review.

5. Non-published works

AI is potentially helpful for developing documents never to be formally published. For example, grant writing requires knowledge in many areas which the authors may not be as familiar with, such as in model design and statistical analysis. AI could speed up the process and obviate the need to call in outside experts and drain their time. Likewise, AI might be useful to research a topic prior to preparing a presentation, or prior to attending a meeting.

REFERENCES

Anushree, P., & Sonal, N. (n.d.). Applicability of Artificial Intelligence in Different Fields of Life. In International Journal of Computer Science and Information Technology Research (Vol. 3). www.researchpublish.com

B.J. Copeland. 2024. Artificial Intelligence. Accessed March 20, 2024 https://www.britannica.com/technology/artificial-intelligence

Ciaccio, E. J. (2023). Use of artificial intelligence in scientific paper writing. Informatics in Medicine Unlocked, 41, 101253. https://doi.org/10.1016/j.imu.2023.101253

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. In International Journal of Intelligent Networks (Vol. 3, pp. 119–132). KeAi Communications Co. https://doi.org/10.1016/j.ijin.2022.08.005

Kacena, M. A., Plotkin, L. I., & Fehrenbacher, J. C. (2024). The Use of Artificial Intelligence in Writing Scientific Review Articles. In Current Osteoporosis Reports. Springer. https://doi.org/10.1007/s11914-023-00852-0

Resnik, D. , & H. M. (2023). The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool.

The Conversation. “Understanding the four types of AI, from reactive robots to self-aware beings, https://theconversation.com/understanding-the-four-types-of-ai-from-reactive-robots-to-self-aware-beings-67616.” Accessed March 20, 2024. https://www.coursera.org/articles/types-of-ai