Translation Techniques of Legalese Archaisms: Evaluating Human Expertise, Machine Power, and Artificial Intelligence

With the advances in the field of computer and information technologies (CIT), Artificial Intelligence (AI), which revolutionized the machine translation process, has become part of our lives over the last years. There has been a shift from tasks based on human intelligence to such based on AI. Living in the era of the global reach of science and technology, it was impossible for the field of translation to be left unaltered. The review focuses on the quality of Machine Translation (MT) output in terms of the complexity and diversity of Bulgarian as the source language and English as the target language. MT quality is analyzed in terms of fidelity, adequacy, lexicon and cultural uniqueness. Furthermore, the purpose of this article is to outline the drawbacks of MT in the cultural and linguistic context of Bulgarian. Some concluding remarks will be made about the dimensions and boundaries of MT. This article, however, contributes to the description of translation as the canvas on which subtle nuances and tender strokes turn into a masterpiece. Literature review was adopted as the main research method. Based on the review of 47 articles, three books and a thesis, I conclude that translation is still a domain that is a privilege to humans. Machines can only accelerate the process of human translation but they cannot serve as a universal replacement. While machine translation that operates through a computer code cannot be combined with social and cultural background, high quality translation can be stimulated through the synergy between artificial and human intelligence.

ObjectivesTo assess quality, clinical acceptance, time-efficiency, and consistency of a novel artificial intelligence (AI)-driven tool for automated presurgical implant planning for single tooth replacement, compared to a human intelligence (HI)-based approach. Materials and methodsTo validate a novel AI-driven implant placement tool, a dataset of 10 time-matching cone beam computed tomography (CBCT) scans and intra-oral scans (IOS) previously acquired for single mandibular molar/premolar implant placement was included. An AI pre-trained model for implant planning was compared to human expert-based planning, followed by the export, evaluation and comparison of two generic implants—AI-generated and human-generated—for each case. The quality of both approaches was assessed by 12 calibrated dentists through blinded observations using a visual analogue scale (VAS), while clinical acceptance was evaluated through an AI versus HI battle (Turing test). Subsequently, time efficiency and consistency were evaluated and compared between both planning methods. ResultsOverall, 360 observations were gathered, with 240 dedicated to VAS, of which 95 % (AI) and 96 % (HI) required no major, clinically relevant corrections. In the AI versus HI Turing test (120 observations), 4 cases had matching judgments for AI and HI, with AI favoured in 3 and HI in 3. Additionally, AI completed planning more than twice as fast as HI, taking only 198 ± 33 s compared to 435 ± 92 s (p < 0.05). Furthermore, AI demonstrated higher consistency with zero-degree median surface deviation (MSD) compared to HI (MSD=0.3 ± 0.17 mm). ConclusionAI demonstrated expert-quality and clinically acceptable single-implant planning, proving to be more time-efficient and consistent than the HI-based approach. Clinical significancePresurgical implant planning often requires multidisciplinary collaboration between highly experienced specialists, which can be complex, cumbersome and time-consuming. However, AI-driven implant planning has the potential to allow clinically acceptable planning, significantly more time-efficient and consistent than the human expert.

This paper explores the evolving role of teachers and literary translators in the era of artificial intelligence and machine translation and addresses the challenges encountered in literary translation. It focuses on achieving a balance between human expertise and machine power to ensure faithful translations of literary works while preserving their inherent artistic value. The study analyses four translations of an excerpt from the short story “Lake Como” by Serbian writer Srđan Valjarević. These translations, from Serbian to English, were conducted in groups by third-year Anglistics students at the Faculty of Philology, Sinergija University in Bijeljina, whose native language is Serbian. The quality and accuracy of the translations were assessed, accompanied by detailed justifications of our preference judgments. A comparison was made between our assessments and those generated by Chat GPT-3, aiming to provide insights into the advancements made by AI in the realm of literary translation. This comprehensive evaluation of translations, encompassing both human translators and AI-powered language models (LLMs), offers a deeper understanding of the specific strengths and weaknesses exhibited by LLMs in the context of literary translation. Through this human evaluation process, we strive to shed light on the specific areas where LLM translators excel and identify the challenges they are still facing. The findings contribute to a deeper understanding of AI's impact on literary translation and pave the way for future research and advancements in this area.

Artificial intelligence (AI) is increasingly applied in translation, raising questions about how AI tools, particularly large language models (LLMs) such as ChatGPT, perform in generating translated content. These systems are designed to perform tasks that would typically require human intelligence, including learning, reasoning, and interpreting context, capabilities that are critical for producing accurate and culturally appropriate translations. This paper examines translation patterns in a source text and in versions produced by both commercial neural machine translation (NMT) systems (Google Translate and DeepL) and AI-powered LLMs (ChatGPT), aiming to identify the influence of linguistic and cultural factors on translation outcomes. The analysis focuses on a specialized culinary domain, using a corpus of forty menus from southern Spanish restaurants translated into English. First, the paper outlines the characteristics of machine translation and its interaction with AI technologies. Then, it compares the outputs of traditional NMT systems with those generated by ChatGPT. This comparison highlights differences in translation techniques and their impact on intelligibility and cultural adequacy, clarifying the potential role of these systems as tools to support human translators. By explicitly distinguishing between MT and AI-generated content, the study provides further insight into the capabilities and limitations of contemporary translation technologies.

Introduction: The third molar is frequently affected in the oral cavity, with rare cases of inverted impaction in the maxillary region. This rarity poses unique challenges in diagnosis, treatment planning, and surgical procedures, with potential complications like sinusitis or infection. Current literature highlights a divide between conservative and surgical management, lacking comprehensive guidelines and exploring the role of AI-assisted tools. This study addresses this gap by evaluating the diagnostic accuracy of AI tools, particularly ChatGPT, against human specialists in Oral and Maxillofacial Surgery. Considering the growing role of AI in medicine, this research aims to provide insights into the potential of AI in enhancing diagnosis and treatment planning for rare cases, emphasizing collaboration between AI systems and medical professionals. Objectives: • Evaluate the diagnostic accuracy of AI tools (ChatGPT) compared to human-generated (specialist OMFS) diagnoses in dental cases. • Assess the efficiency and reliability of AI-assisted treatment plans in contrast to those generated by dental professionals. • Compare the performance and features of paid and non-paid versions of the AI programs utilized. Materials and Methods: This study centered on the unique case of a 59-year-old woman at Thumbay Dental Hospital, presenting issues related to a faulty dental bridge and a history of managed hypertension. An orthopantomogram showed the inverted impacted maxillary third molar Figure 1. The patient exhibited no direct symptoms from this impaction. A cone computed tomography was performed for a detailed analysis of the patient for complete prosthetic rehabilitation and academic purposes Figure 2. All the available data, including the history, clinical examination, and radiographic findings, were provided to a specialist and AI tools (ChatGPT version 3 and ChatGPT version 4) to get a diagnosis and treatment plan for this unusual case of an impacted third molar. Data collection comprised clinical examinations, imaging, and AI outputs, focusing on the accuracy of diagnostic and treatment plans. The study also assessed AI’s adaptability, cultural sensitivity, and practicality in clinical settings, aiming to gauge AI tools’ potential in enhancing dental diagnostics and treatment planning alongside human expertise. AI tools, including ChatGPT and its advanced versions, were employed to generate and compare diagnostic assessments and treatment plans against those created by dental professionals. Results: In a rare dental case involving a 59-year-old woman with a faulty dental bridge and managed hypertension, specialists at Thumbay Dental Hospital identified functional issues and an inverted impacted maxillary third molar using orthopantomogram and Cone Beam Computed Tomography. Collaborating with oral and maxillofacial surgeons, a comprehensive treatment plan for complete oral rehabilitation was formulated, considering age, anatomical complexity, and medical history, offering two options for the impacted third molar. The AI-generated diagnosis and treatment plans from ChatGPT versions 3 and 4 were explored. ChatGPT-3 provided a detailed plan for bridge replacement, including a specialized segment for managing the impacted third molar. ChatGPT-4 crafted a comprehensive plan starting with an initial consultation, encompassing diagnostic procedures, discussions on bridge replacement options, preparation, fabrication, fitting, and post-procedure care. The plan addressed missing teeth and the impacted tooth, highlighting adaptability to individual needs. However, ChatGPT-4 emphasized its inability to provide medical diagnoses, stressing the importance of professional evaluation. In summary, the study compares human-generated and AI-generated diagnosis and treatment plans. The human-generated plan prioritizes collaboration and comprehensive care, while AI-generated plans from ChatGPT versions 3 and 4 demonstrate detailed and adaptable approaches. ChatGPT-4 underscores the need for professional evaluation. The research sheds light on the potential roles of human and AI expertise in dental diagnostics and treatment planning, emphasizing the importance of collaboration for optimal patient care. Conclusion: This study highlights the collaborative potential of AI and human intelligence in handling intricate dental cases, such as Inverted Maxillary Third Molar Impaction. While AI tools like ChatGPT showcase the ability to create detailed treatment plans, their incapacity to replicate nuanced clinical judgment underscores the vital role of human oversight, particularly in specialized fields like Oral and Maxillofacial Surgery. The results are consistent with existing research, emphasizing AI as a supplement to, rather than a substitute for, human expertise in healthcare. The ongoing integration of AI with human medical practice shows promise in improving diagnostic accuracy and treatment effectiveness in dental healthcare.

PurposeThe paper aims to present key insights for achieving a sustainable competitive advantage from implementing data analytics solutions and artificial intelligence (AI).Design/methodology/approachThis conceptual paper builds on prior empirical and conceptual research, and it offers anecdotal evidence from a best-practice case study of Liverpool Football Club.FindingsBeyond the well-known companies in profiting from AI, such as Amazon, this paper presents a surprising best-practice example for achieving a competitive advantage from combining data analytics and AI with human expertise. Liverpool Football Club is a leading soccer club from England, and it has experienced a major revival in terms of sporting success and financial performance in recent years. The club’s emphasis on data analytics and AI only contributed to this impressive evolution when it was complemented with the emotional intelligence and people skills of its present manager Juergen Klopp. Along with new conceptual arguments, this example of integrated intelligence offers important insights for managers and executives in companies from many industries – far beyond sports management.Originality/valueThe conceptual arguments and case example illustrate that the competitive benefits of implementing standardized AI solutions in an isolated way will often be relatively limited. Many AI solutions will be standardized in the near future, and they may easily be applied by many firms. Thus, even those companies that are considered as AI pioneers may not be able to sustain their competitive advantage unless they develop an integrated intelligence architecture, which combines human and AI. This integration of data analytics and AI with the human intelligence and expertise of a firm’s employees offers the basis for a sustainable advantage because it is difficult to match for competitors. Thus, the paper offers new theoretical insights and direct managerial implications with regard to profiting from AI and data analytics.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

The landscape of the translation profession has been fundamentally transformed by the rapid integration of Artificial Intelligence (AI), particularly Neural Machine Translation (NMT). The current discourse is frequently polarised, fluctuating between optimistic visions of seamless communication and insecure fears of human obsolescence. This article transcends this dichotomy to examine the intricate, symbiotic relationship that has been developed between AI systems and human translators. Employing a systematic review of recent academic literature, this article examines the current state of human–AI collaboration in translation. The analysis indicates that the predominant interaction model is not a replacement, but rather a collaborative framework known as Machine Translation Post-Editing (MTPE) within a “Human-in-the-Loop” (HITL) paradigm. Although AI is capable of processing large volumes of texts at an astonishing pace, human expertise is still essential for tasks that require cultural nuance, contextual comprehension, creativity, and ethical judgement. As a result, translators are now required to possess a new set of linguistic, technological, and domain-specific abilities as their roles evolved from text generators to quality validators, editors, and cultural consultants. In summary, the future of translation is neither fully automated nor fully human, but rather a synergistic collaboration that combines the cognitive depth of human intelligence with the computational power of AI.

Hybrid intelligence in procurement: Disillusionment with AI’s superiority?

The pros and cons of which one is better in producing good result of translation between Machine Translation (MT) and Human Translation (HT) has been going on for many years. In the attempt to observe which is better between MT and HT, this article focuses on exploring the techniques used by U-Dictionary as a MT and Maggie Tiojakin as a HT in translating The Gift of the Magi into Indonesian. Data in this research are the words, phrases, clauses and sentences related to the translation techniques in the original version of The Gift of the Magi and the two translation versions. The collected data are analyzed qualitatively by using Molina and Albir’s (2002) theory. The results describe that Maggie Tiojakin used 12 techniques; they are adaptation, amplification, compensation, description, discursive creation, established equivalent, generalization, literal translation, modulation, particularization, reduction, and transposition. Meanwhile, U-Dictionary used 8 techniques; they are amplification, borrowing, calque, established equivalent, literal translation, modulation, reduction, and transposition. The dominant translation technique used by Maggie Tiojakin is discursive creation (24.54%), whereas in U-Dictionary, it is literal translation (47.27%). From the different translation techniques used, it can be proven that HT uses more various techniques and has better translation result than MT, in which the translation of the literary works especially a short story done by HT is more accurate, readable, and acceptable.

PurposeThis paper draws on the dynamic capabilities (DC) view to develop a conceptual framework that explicates the mechanisms through which human intelligence (HI) and artificial intelligence (AI) substitute and complement each other for organizational knowledge management (KM) while considering the role of ethics.Design/methodology/approachThis is a conceptual paper that draws on DC theory and integrates insights from the burgeoning literature on organizational AI adoption and application to develop a conceptual framework that explains the mechanisms through which HI and AI may substitute and complement each other for organizational KM to develop DC.FindingsThe conceptual framework demonstrates that substituting HI with AI is suitable for external environmental scanning to identify opportunities, while AI substitution for HI is ideal for internal scanning through data analytics. Additionally, HI complementing AI is effective for seizing opportunities by aligning internal competencies with external opportunities, whereas AI complementing HI is beneficial for reconfiguring assets by transforming tacit knowledge into explicit knowledge. This substitution and complementarity between HI and AI shape KM processes—acquisition, conversion, application, and retention—that influence organizational performance, depending on how internal and external ethical standards govern organizational AI use.Research limitations/implicationsThe paper presents key insights into how AI may substitute for HI for internal data analytics in KM but may be ineffective for external environmental scanning to sense opportunities. It further reveals that using AI to capture and convert tacit knowledge (HI) to explicit knowledge requires ethical considerations at the organizational level, but ethical considerations are necessary at the employee/manager level when HI relies on AI-generated insights for strategic decisions.Practical implicationsThe study implies that in environments with defined regulations for AI and KM (e.g. privacy protection), responsibility for the consequences of AI-HI substitution and complementarity in developing DC can be assigned to specific steps in the KM process. However, in environments with undefined regulations, responsibility must be assigned to people, units or departments who manage the entire KM process to ensure accountability for ethical breaches.Originality/valueThis study proposes AI-HI substitution and complementarity in organizations to extend the understanding of the relationship between AI and HI to DC development.

This research is entitled "Analysis of Translation Methods and Techniques on the Song Lyrics of "Mungkin Nanti" by Ariel NOAH into Japanese by Hiroaki Kato". This study aims to find out the use of translation methods and translation techniques applied to the lyrics of Ariel NOAH's song " Mungkin Nanti" which was translated into Japanese by Hiroaki Kato. The theory used in this translation method is Newmark, and the translation technique used is the theory of Molina and Albir. This research methodology uses descriptive analysis with a qualitative approach, in other words, that is to describe or describe the data that has been obtained. This study uses data analysis techniques using the agih method and the direct element technique (BUL). Based on the results of the analysis can be obtained 18 data translation methods and techniques. According to the 8 translation methods proposed by Newmark, it was found that there were 4 translation methods used in the lyrics of the song " Mungkin Nanti",1 data literal translation, 2 data Semantic translations, 5 data free translations, and 10 data Communicative translations, and then the data were analyzed to determine the translation technique from Molina and Albir, there were 4 translation techniques used, 2 data common equivalents, 4 data modulations, 6 data reductions, and 6 data transpositions.

Machine translation based on artificial intelligence has many commercial applications, such as Google translation, Baidu translation, and Youdao translation. More artificial intelligence and its translation are still used in all aspects of life. Therefore, we should reexamine its impact on the relationship between human translation and machine translation. Therefore, based on this background, this paper discusses the impact of the development of artificial intelligence machine translation on the relationship between human translation and machine translation. Although the translation accuracy and overall situation of machine translation based on artificial intelligence are similar to that of human translation, the basic algorithm of machine translation is still a program that judges right and wrong through computer code. It cannot simulate the “faithfulness, expressiveness, and elegance” of human translation in combination with social background and human culture. However, for some mechanical operations, such as business translation, scenes with low requirements, such as common vocabulary in daily tourism, can still meet the needs. Therefore, under the influence of artificial intelligence machine translation, the relationship between the two is that machine translation can replace human translation in some aspects, but it cannot replace human translation.

The ubiquity of artificial intelligence (AI) has significantly influenced research and practice in translation studies. As an integral part of the globalized world, AI's impact continues to grow. But translation accuracy can be compromised if machine translations are not supported by post-editing. This study evaluates the accuracy of machine and human translation by comparing their outputs using a qualitative descriptive approach. Machine translation, while efficient, struggles with polysemous terms, historical references, and culturally embedded expressions, leading to semantic distortions and omissions. The indispensability of human expertise also has been highlighted in preserving the integrity of Arabic scholarly works in cross-cultural academic discourse. This study underscores the importance of hybrid approaches, combining machine translation's speed with human post-editing to enhance translation quality. Recommendations offered include improving machine translation systems through domain-specific training and contextual awareness algorithms.

With the search for a smarter, faster, and technological ways of getting things accomplished, Artificial Intelligence (AI) is developing at a faster pace. The technology has become a part of daily life, where the blend of human intelligence and machine learning has reached heights in various fields of science and technology. The machine simulates the human intelligence and improves their abilities with the help of self-adapting algorithms. Artificial intelligence has provided many benefits in various fields, particularly in medicine, where it plays a major role in the advancement of the medical field, ranging from virtual assistants to creating a better diagnosis and treatment using accumulated patient data. In orthodontics, the treatment focuses on altering the occlusion, controlling the development of dentoalveolar components and growth abnormalities. An effective assessment of these problems enables in determining the need for treatment and to prioritize it. Precise diagnosis, offering relevant and complete information is a key to a successful practice in orthodontics. Of late artificial intelligence is applied in orthodontics in decision making and planning effective treatment outcomes. Artificial intelligence is useful in simulation of various clinical scenarios in the three-essential sequence - diagnosis, treatment planning and treatment, which is efficient enough in reducing the workload, time and also increases the accuracy and monitoring. In no ways artificial intelligence can replace the dentist because clinical practice is not just about the diagnosis and treatment plan. So, humans should have a basic understanding on artificial intelligence models to assist in clinical judgement and not to replace the knowledge and expertise of humans. KEY WORDS Artificial intelligence, Machine Learning, Artificial Neural Network, Orthodontics, Review