Powerful Language Research Articles

Interprofessional collaboration in telemedicine for long-term care: An exploratory qualitative study.

Widespread and sustained adoption of telemedicine in long-term residential care is emerging. Nursing home (NH) nurses play a key role in collaborating with remote physicians to manage residents' medical conditions through videoconferencing. Therefore, understanding of interprofessional collaboration and effective communication between nurses and physicians is critical to ensure quality of care and safety during teleconsultations. To explore NH nurses' and physicians' experiences of interprofessional collaboration and communication during teleconsultations. A qualitative descriptive design was adopted. Purposive sampling was conducted to recruit 22 physicians and nurses involved in NH teleconsultations. Semi-structured online interviews were conducted, and data were thematically analyzed. Three themes were identified: (1) Manner of communication in telemedicine, (2) sociocultural influences in collaborative practice, and (3) role expectations in telemedicine. Both nurses and physicians recognized the importance of building and maintaining trust as physicians heavily depended on nurses for provision of objective information for clinical decision-making. However, practice differences were observed between nurses and physicians during teleconsultations. Sociocultural influences such as power relations and language barriers also affected the nurse-physician relationship and interpersonal communication. Additionally, different performance expectations were identified between nurses and physicians. Interprofessional collaboration in teleconsultations is challenging because of lack of in-person assessment and dependence on nurses for clinical information. In addition, expectations and communication styles differ among healthcare professionals. This study called for interprofessional telemedicine training with incorporation of shared mental models to improve role clarity and communication. Given the international-dominated healthcare workforce in long-term care, the development of cultural competency could also be considered in telemedicine training to enhance nurse-physician collaborative practice. Telemedicine is increasingly adopted in long-term care settings, where multidisciplinary healthcare professionals from different health institutions are involved in resident care. Interprofessional collaboration should be incorporated into telehealth education for enhanced clinical practice in this care delivery model.

Application of ChatGPT in natural disaster prevention and reduction

Improving disaster prevention, reduction, and emergency response capabilities is crucial in a country prone to frequent natural disasters. Since the release of ChatGPT, it has garnered widespread attention and sparked extensive discussions in various fields due to its powerful language processing and reasoning abilities. This paper explores the application of ChatGPT in natural disaster prevention and reduction, building upon its language capabilities. The paper examines ChatGPT's ability to gather information and its potential for disaster prevention science popularization and education. It describes the rapid response and availability of ChatGPT in natural disaster prevention and highlights its potential to assist emergency response efforts. The paper also outlines ChatGPT's assistance in the pre-disaster, during-disaster, and post-disaster phases. Additionally, it points out the current limitations and challenges in applying ChatGPT and provides prospects for future research directions in natural disaster prevention and reduction.

“I'm Workin’ on My Buildin”: Freedom and Foundation-Building in Florence Price's Two Violin Fantasies

AbstractFlorence Price (1887–1953) was instrumental in establishing a “black musical idiom” in the twentieth century (Samantha Ege, 2020) by embedding vernacular songs into her works, including Violin Fantasy No. 2 in F-sharp minor, built on “I'm workin’ on my Buildin.’” In 1940 she arranged the melody as the second of the Two Traditional Negro Spirituals, finished on March 26, 1940. On March 29 and 30, 1940, she quickly dispatched Fantasy No. 2. Price often performed the piano part of her works herself. The performative act of playing Fantasy No. 2 with its embedded spiritual “I'm workin’ on my Buildin’ […] All for my Lord” would have solidified her faith, which rested in part in her own interpretation of its lyrics: Her “work” on her “buildin” and foundations, in composition and in life. Furthermore, each performance of Fantasy No. 2 would have created an embodied performed commemoration, from her perspective, of historical events of injustice and oppression in the Jim Crow South, which she abandoned in 1927 for Chicago. By engaging with Price's fantasies through the lens of performance studies and genre theory, and by drawing on Ege (2020), Rae Linda Brown (2020), Cooper (2019, 2020), and Douglas Shadle (2021), this article examines Price's vernacular foundation and sonic foundation-building symbolically. Meanings of freedom emerge on several levels, which we relate to creative freedom and to “freedoms in the most oppressive of social environments,” such as Price's environment, to which she responded with “a powerful musical language” (Ege, 2020).

Communication of Traditional Elites’ Power Relations and Language Power Over Mute Groups

Research on the communication of traditional elite power relations and language power over silent groups in the deliberative culture of "Lonto Leok" of the Manggarai ethnic group aims to explore the culture of lonto leok and to find out the power relations of traditional elites with the community (silent groups). To achieve this goal, the researchers used a qualitative approach in order to get a variety of problems related to the object and subject of research. This research found that the lonto leok culture is a form of deliberation in the context of the Manggarai indigenous community, where the traditional elite has a very strong role in the social interaction of the Manggarai community. The position of the traditional elite and the power of language in lonto leok culture implicitly silence the lower layers of non-elite society, clearly illustrated in the patrilineal-genealogical structure of the local elite and the ritual language where all lonto leok participants are directed to always be one heart and soul and avoid differences of opinion. Language is not only a tool of communication, but also a tool of power that is not only for understanding, but because language is related to certain shared ideologies and values, it must be believed, fulfilled, obeyed, respected and recognized.

Fictionalizing the Language of Power: A Comparative Study of Pakistani and Latin American Novels

Latin American dictator fiction has a long tradition and is widely acclaimed in critical studies, while Pakistani dictator fiction began to emerge in recent years. The study is analytical and comparative research based on the contents of two culturally, historically, and geographically diverse novels—Mario Vargas Llosa’s The Feast of the Goat and Mohammad Hanif’s A Case of Exploding Mangoes so as to locate their common and contrasting aspects. It is a qualitative study and through content analysis, it finds convergences and divergences between a Latin American and a Pakistani dictator novel. A special emphasis of the study is on power, dictator portrayal, torture, resistance, retelling/rewriting history, and representation. The study concludes that where Llosa presents the detailed description of events related to the atrocious regime of Trujillo, Hanif presents a partial and not the whole view of Zia’s dictatorship. Llosa misses no opportunity to create the sentiments of detestation and wrath against the despotic rule and his language throbs with bitterness and becomes satiric while unveiling the dark aspects of the Trujillo regime. However, Hanif, instead of giving a detailed and direct portrayal of certain dark aspects and brutalities, gives just oblique references to them and even the events which he brings to the forefront are presented in a very light and humorous vein which fail to arouse the emotions of abhorrence and denouncement against the despotic rule.

ChatGPT encounters multiple opportunities and challenges in neurosurgery.

ChatGPT, powered by the GPT model and Transformer architecture, has demonstrated remarkable performance in the domains of medicine and healthcare, providing customized and informative responses. In our study, we investigated the potential of ChatGPT in the field of neurosurgery, focusing on its applications at the patient, neurosurgery student/resident, and neurosurgeon levels. The authors conducted inquiries with ChatGPT from the viewpoints of patients, neurosurgery students/residents, and neurosurgeons, covering a range of topics, such as disease diagnosis, treatment options, prognosis, rehabilitation, and patient care. The authors also explored concepts related to neurosurgery, including fundamental principles and clinical aspects, as well as tools and techniques to enhance the skills of neurosurgery students/residents. Additionally, the authors examined disease-specific medical interventions and the decision-making processes involved in clinical practice. The authors received individual responses from ChatGPT, but they tended to be shallow and repetitive, lacking depth and personalization. Furthermore, ChatGPT may struggle to discern a patient's emotional state, hindering the establishment of rapport and the delivery of appropriate care. The language used in the medical field is influenced by technical and cultural factors, and biases in the training data can result in skewed or inaccurate responses. Additionally, ChatGPT's limitations include the inability to conduct physical examinations or interpret diagnostic images, potentially overlooking complex details and individual nuances in each patient's case. Moreover, its absence in the surgical setting limits its practical utility. Although ChatGPT is a powerful language model, it cannot substitute for the expertise and experience of trained medical professionals. It lacks the capability to perform physical examinations, make diagnoses, administer treatments, establish trust, provide emotional support, and assist in the recovery process. Moreover, the implementation of Artificial Intelligence in healthcare necessitates careful consideration of legal and ethical concerns. While recognizing the potential of ChatGPT, additional training with comprehensive data is necessary to fully maximize its capabilities.

Unraveling the Enigma: how can ChatGPT perform so well with language understanding, reasoning, and knowledge processing without having real knowledge or logic?

Artificial Intelligence (AI) has made significant progress in various domains, but the quest for machines to truly understand natural language has been challenging. Traditional mainstream approaches to AI, while valuable, often struggled to achieve human-level language comprehension. However, the emergence of neural networks and the subsequent adoption of the downstream approach have revolutionized the field, as demonstrated by the powerful and successful language model, ChatGPT. The deep learning algorithms utilized in large language models (LLMs) differ significantly from those employed in traditional neural networks. This article endeavors to provide a valuable and insightful exploration of the functionality and performance of generative AI. It aims to accomplish this by offering a comprehensive, yet simplified, analysis of the underlying mathematical models used by systems such as ChatGPT. The primary objective is to explore the diverse performance capabilities of these systems across some important domains such as clinical practice. The article also sheds light on the existing gaps and limitations that impact the quality and reliability of generated answers. Furthermore, it delves into potential strategies aimed at improving the reliability and cognitive aspects of generative AI systems.

An energy-aware combinatorial auction-based virtual machine scheduling model and heuristics for green cloud computing

Considering the increasing demand for cloud computing, and the financial and environmental impact of the increasing energy consumption trend of data centers, improving energy efficiency is vital for cloud service providers. In this study, an energy-aware virtual machine scheduling model is proposed which is based on the multi-unit nondiscriminatory combinatorial auction. The model includes a powerful bidding language that allows users to declare their complicated virtual machine requests using logical AND and OR relations along with the time constraints. The study also presents the formal definition of the model and the associated optimization problem for determining the optimum schedule and energy-efficient placement of VMs on physical servers. The optimization problem is formulated using integer linear programming and several heuristic solution methods including the Genetic Algorithm are proposed for this problem. The performances of the model and the proposed heuristics are assessed on a comprehensive test suite. The proposed model is estimated to provide approximately a 37% improvement in revenues, and the solution methods are estimated to provide high-quality solutions within only 5% of the optimum which enable the model to be deployed in large-scale clouds.

ChatGPT: A Threat or an Opportunity for Scientists?

AbstractThe emergence of artificial intelligence (AI) has brought about numerous opportunities and challenges for scientists in various fields. One notable advancement in AI is OpenAI’s ChatGPT (Generative Pretrained Transformer), a powerful language model based on the GPT‐3.5 architecture that can generate text resembling human‐like responses. Like any technological advancement, the risk‐benefit opinions of ChatGPT diverge among scientists. This paper explores the implications of AI and, in particular, ChatGPT, delving into its potential as both a threat and an opportunity for scientists. It examines the risks associated with misinformation, ethical considerations, and the impact on traditional research processes. Ultimately, the argument put forth is that while ChatGPT and chatbots like it do present challenges, they also have the potential to significantly transform scientific inquiry and foster innovation.

Artificial intelligence in cardiology: Exciting but handle with caution

While artificial intelligence is not new, recent advances have caught the attention of people in many fields of study. In fact, Alan Turing, who conceived of the idea of what is now called a computer in 1937, was also an early leader in the field of artificial intelligence. The Turing test was developed to measure a machine's ability to be indistinguishable from human output [ [1] Topol E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, 2015 Google Scholar ]. Only recently, with sufficient computing power, the internet, big data, and large language models, such as ChatGPT4 and has the vision of ‘AI’ begun to be met [ [2] Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019; 25: 44-56 Crossref PubMed Scopus (1969) Google Scholar ]. In addition to ChatGPT, which as developed by OpenAI, in collaboration with Microsoft, other large language model, possessing large data and computing power have been developed by Google and Facebook, and it is likely that more will follow. ChatGPT4 recently gained a lot attention when, despite no formalized training, was able to pass the United States Medical Licensing examination (USMLE), providing explanations of the medical reasoning [ [3] Nori H. King N. McKinney S. Carignan D. Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv. 20 Mar 2023; (2303.13375v1 [cs.CL]) Google Scholar ]. Artificial Intelligence and machine learning systems (AI/ML) are only as good as the dataset that was used to train the system. Bias in the training dataset will be passed onto the system.

TSR: A User-Friendly R Shiny Application for Assessment of Optimal Blood Product Selection in ABO-Incompatible Hematopoietic Stem Cell Transplantation.

Patients undergoing hematopoietic stem cell transplant (HSCT) need frequent transfusions, until their red blood cells (RBCs) and platelets start to recover. The safe transfusion for patients who receive ABO-incompatible HSCT is essential to the transplant process. To date, there is no user-friendly tool to choose the right blood product for transfusion treatment, despite the number of guidelines and expert advice on the subject. R/shiny is a powerful programming language for clinical data analysis and visualization. It can create interactive web applications that work in real-time. The web application named TSR was built using R programming, simplifying blood transfusion practice for ABO-incompatible HSCT with a one-click solution. The TSR is divided into four main tabs. The home tab provides an overview of the application, while RBC, plasma and platelet transfusion tabs offer tailored suggestions for blood product selection in each category. Unlike traditional methods that rely on treatment guidelines and specialist consensus, TSR leverages the power of the R/Shiny interface to extract critical content based on user-specified parameters, providing an innovative approach to improve transfusion support. The present study highlights that the TSR enables real-time analysis, and promotes transfusion practice by offering a unique and efficient one-key output for blood product selection to ABO-incompatible HSCT. TSR has the potential to become a widely-utilized tool for transfusion services, providing a reliable and user-friendly solution that enhances transfusion safety in clinical practice.

DreamCoder: growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning.

Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages-systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating domain-specific programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A 'wake-sleep' learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton's and Coulomb's laws. Concepts are built compositionally from those learned earlier, yielding multilayered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Research in a Closed Political Context, COVID, and Across Languages: Methodological Lessons, Messages, and Ideas

This study offers a number of contemporary qualitative and mixed methods research lessons regarding cross-language research and research in closed social and political contexts such as post-Soviet countries, especially how such research may be adapted to disruptions like those posed by the COVID pandemic or other disasters. The lessons draw from a study of policy learning among government officials in Kazakhstan which illustrated several methodological strengths and weaknesses and generated a number of methodological recommendations. In particular, while the literature on best practices for research in closed contexts emphasizes deep interactions to develop trust, the COVID pandemic and other disruptions may force researchers to transition to online modalities and constrain options. We suggest strategies to overcome these limitations, including supplementing findings from interviews with content analysis or other multimethod approaches. Additionally, we argue that researchers in post-Soviet and post-colonial contexts must be particularly attuned to the challenges of cross-language research and the combination of local languages with the language of the colonial power. These lessons hold relevance for researchers working in a variety of contexts as they conduct research during times of disasters and geopolitical instability.

AI Text Summarization System

Abstract: Our research paper presents an AI text summarization system that utilizes GPT, a powerful language model, to generate concise and meaningful summaries of lengthy text documents. The system consists of four modules: User, Android Application, GPT API, and GPT server. The User interacts with the system through the Android Application, which serves as the user interface. The GPT API acts as the intermediary between the Android Application and the GPT server, which hosts the GPT model and handles the text summarization process. The system employs state-of-the-art natural language processing techniques to generate summaries while preserving contextual coherence and salient information. The system's summarization capabilities are evaluated using metrics such as Rouge and F1 scores, demonstrating its effectiveness in capturing key information from different types of text documents. The system's integration with Android platforms provides convenient access for mobile users, making it suitable for applications such as news summarization, document summarization, and content curation. The modular architecture of the system allows for scalability and flexibility, enabling future enhancements and extensions. Our AI text summarization system utilizing GPT presents a promising approach for automatically summarizing large volumes of text, providing users with time-saving and meaningful summaries. The system has potential applications in various domains and can serve as a foundation for further research in the field of text summarization and natural language processing

Discussion on the Reform of Higher Legal Education in China Based on the Application and Limitation of Artificial Intelligence in Law Represented by ChatGPT

ChatGPT, as a representative of generative artificial intelligence, is a disruptive innovation in the field of AI. With its powerful natural language processing technology, ChatGPT has been widely used in the legal profession, providing strong support for the legal industry. As ChatGPT and other AI technologies continue to evolve and become more widely adopted, they will undoubtedly impact the legal industry and current higher education in Chinese law. Based on this, we will explore the challenges and opportunities facing Chinese legal education in the era of artificial intelligence, focusing on the applications and limitations of generative AI such as ChatGPT in the legal profession.

The language of power and the power of language. Analysis of propaganda’s narrative in Fahrenheit 451

Ray Bradbury’s book is a narrative, in a dystopian context, of how the language of power manages to be pervasive, to infiltrate reality, in people’s experiences, in the consciousness of an entire society, so as to powerfully transform the meanings of commonly used words, as well as the normative qualifications of acts and professions. A reflection will be proposed on some “semantic spies” present in Fahrenheit 451, to highlight the centrality of some uses of language, aimed at influencing not only the behaviour of the members but also at modifying some institutions, manipulating rules and procedures, for the benefit of those who social control operates by administering power. The proposed intervention will deal with the specific function of propaganda language. Finally, a reflection on the power of the word will be proposed, as a tool capable of fully understanding the present and allowing all individuals to lay the foundations for new social, legal and political realities for the future.

An examination of the lyrical construction of Pop music

The current study examines the use of five different dimensions of language in popular music based on the sex of the performer. Theoretically grounded in both social cognitive theory and cultivation theory, the study analyzes popular music as a vehicle for enculturating gender norms within society. Sampling and analyzing over 700 popular songs, the lyrical construction of songs performed by females and males is compared. Similarities are found in many key areas, while differences are identified in the specific areas of power language and social language. Additional differences are identified and discussed when artists perform in mixed-sex groups. Based on the results, a discussion of the prominence of male writers and lack of female representation in the music industry is included.

Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations.

Background ChatGPT is a powerful artificial intelligence large language model with great potential as a tool in medical practice and education, but its performance in radiology remains unclear. Purpose To assess the performance of ChatGPT on radiology board-style examination questions without images and to explore its strengths and limitations. Materials and Methods In this exploratory prospective study performed from February 25 to March 3, 2023, 150 multiple-choice questions designed to match the style, content, and difficulty of the Canadian Royal College and American Board of Radiology examinations were grouped by question type (lower-order [recall, understanding] and higher-order [apply, analyze, synthesize] thinking) and topic (physics, clinical). The higher-order thinking questions were further subclassified by type (description of imaging findings, clinical management, application of concepts, calculation and classification, disease associations). ChatGPT performance was evaluated overall, by question type, and by topic. Confidence of language in responses was assessed. Univariable analysis was performed. Results ChatGPT answered 69% of questions correctly (104 of 150). The model performed better on questions requiring lower-order thinking (84%, 51 of 61) than on those requiring higher-order thinking (60%, 53 of 89) (P = .002). When compared with lower-order questions, the model performed worse on questions involving description of imaging findings (61%, 28 of 46; P = .04), calculation and classification (25%, two of eight; P = .01), and application of concepts (30%, three of 10; P = .01). ChatGPT performed as well on higher-order clinical management questions (89%, 16 of 18) as on lower-order questions (P = .88). It performed worse on physics questions (40%, six of 15) than on clinical questions (73%, 98 of 135) (P = .02). ChatGPT used confident language consistently, even when incorrect (100%, 46 of 46). Conclusion Despite no radiology-specific pretraining, ChatGPT nearly passed a radiology board-style examination without images; it performed well on lower-order thinking questions and clinical management questions but struggled with higher-order thinking questions involving description of imaging findings, calculation and classification, and application of concepts. © RSNA, 2023 See also the editorial by Lourenco et al and the article by Bhayana et al in this issue.

From ChatGPT-3 to GPT-4: A Significant Advancement in AI-Driven NLP Tools

Recent improvements in Natural Language Processing (NLP) have led to the creation of powerful language models like Chat Generative Pre-training Transformer (ChatGPT), Google’s BARD, Ernie which has shown to be very good at many different language tasks. But as language tasks get more complicated, having even more advanced NLP tool is essential nowadays. In this study, researchers look at how the latest versions of the GPT language model(GPT-4 & 5) can help with these advancements. The research method for this paper is based on a narrative analysis of the literature, which makes use of secondary data gathered from previously published studies including articles, websites, blogs, and visual and numerical facts etc. Findings of this study revealed that GPT-4 improves the model's training data, the speed with which it can be computed, the flawless answers that it provides with, and its overall performance. This study also shows that GPT-4 does much better than GPT-3.5 at translating languages, answering questions, and figuring out how people feel about things. The study provides a solid basis for building even more advanced NLP tools and programmes like GPT-5. The study will help the AI & LLM researchers, NLP developers and academicians in exploring more into this particular field of study. As this is the first kind of research comparing two NLP tools, therefore researchers suggested going for a quantitative research in the near future to validate the findings of this research.

ChatGPT for Education and Research: Opportunities, Threats, and Strategies

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores the potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. The generated codes are validated with an online judge system to evaluate their accuracy. In addition, we conducted several surveys with students and teachers to find out how ChatGPT supports programming learning and teaching. Finally, we present the survey results and analysis.