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Medical imaging is currently using artificial intelligence-based technologies to aid evaluate diagnostic information images, particularly in enforcing kidney stones. Artificial intelligence technology continues to develop, many studies show that deep learning is more widely used compared to traditional machine learning, so an Artificial intelligence system is needed to assist the accuracy of health diagnoses, thus helping in the field of radiology health. The aim of the research is to use artificial intelligence with deep learning models to help detect abnormalities in the kidneys. This research method is a literature review of Scopus data related to deep learning in medical imaging in detecting kidney stones. The results of using Artificial Intelligence in medical imaging can be used in diagnosing diseases including detecting Covid-19, musculoskeletal, calcium scores on Cardiac CT, liver tumors, urinary tract lesions, examination of the abdomen and kidney stones. Utilization of Artificial Intelligence in detecting kidney stones can be done with various classification models including XResNet-50, ExDark19, CystoNet, CNN, ANN. Using the right model and having a high accuracy value can help radiologists to accurately detect kidney stones.

Over the past fifty years, neonatal screening has become essential in the public health programs of a large number of countries. During all these years, the number of detectable diseases has continued to grow, following the possibilities offered by technical advances in clinical biology. The Guthrie test has enabled the miniaturization of blood sampling, opening up the possibilities of biological screening in the newborn population. Fluorimetry, immunoassay and more recently tandem mass spectrometry have subsequently allowed to detect many treatable disorders. The new developments of next generation sequencing and artificial intelligence may open a new era despite many ethical questions that will arise. This review provides an overview of the biological techniques currently used for neonatal screening and opens up perspectives on the place of new technological developments., (© 2021 médecine/sciences – Inserm.)

For more than a decade, we have witnessed an acceleration in the development and the adoption of artificial intelligence (AI) technologies. In medicine, it impacts clinical and fundamental research, hospital practices, medical examinations, hospital care or logistics. These in turn contribute to improvements in diagnostics and prognostics, and to improvements in personalised and targeted medicine, advanced observation and analysis technologies, or surgery and other assistance robots. Many challenges in AI and medicine, such as data digitalisation, medical data privacy, algorithm explicability, inclusive AI system development or their reproducibility, have to be tackled in order to build the confidence of medical practitioners in these technologies. This will be possible by mastering the key concepts via a brief history of artificial intelligence., (© 2020 médecine/sciences – Inserm.)

While we hear more and more about the miracle of so-called 'neural' machine translation (NMT), all too little attention is paid to discourse on NMT. In this paper I analyse this discourse focusing on the hype around artificial intelligence in general and machine translation in particular, including the now often mentioned generative AI (based on generative pre-trained models, GPT). how it is actually used and how users perceive it. As a counterpoint to these analyses, I then focus on actual uses on NMT. Who are the users of machine translation systems today, and what are their practices? What do they know about machine translation and how do they perceive it? Based on data available in the literature and from recent surveys, I seek to define the current uses of NMT, in order to show the advantages that certain well-supervised uses can present, as well as the risks incurred by unsophisticated users. These risks are linked with well described effects of influence, persuasion and sometimes disinformation, in order to show that the risks incurred are still considerable but that what users need most is long-term projections, ethical discussions and AI commons. [ABSTRACT FROM AUTHOR]

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(1), 105-116 | European Forum Insight of 20 May 2024 | (Table of Contents) I. Introducing the impact of Artificial Intelligence. – II. The AI Act: the risk architecture. – III. Further AI policies: development incentives and state regulatory frameworks. – IV. Proposals for legislation and other measures in Italy. – V. The human-centric approach. | (Abstract) The development of new emerging technologies, such as artificial intelligence, has sparked a scientific debate on their risks and benefits. This debate necessitates legal and regulatory considerations, particularly regarding the balance between technological growth and the protection of human rights. This Insight analyses the legal framework established by the European Union in its initial regulatory measures. The Insight highlights the importance of considering the human-centric perspective and adopting a risk-based methodology in the Artificial Intelligence Act. It also mentions the AI regulatory measures proposed by Member States, with a particular focus on Italy.

Copyright of Vie et Sciences de l'Entreprise is the property of ANDESE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The role of artificial intelligence in shaping Islamic finance services reflects a fusion of modern technology and Islamic principles, offering innovative opportunities to enhance financial services in ways that are compatible with Islamic law. This research paper aims to study the role of artificial intelligence in shaping Islamic finance services by addressing key theoretical literature related to artificial intelligence and Islamic finance and examining their relationship. Using a descriptive-analytical approach, the paper reviews current and future applications of artificial intelligence in financial services, as well as the major challenges in applying artificial intelligence in Islamic finance. The study concludes that artificial intelligence can enhance innovation in the Islamic finance sector by providing advanced and innovative solutions that better meet customer needs.

The rise of AI technology across different sectors has drastically influenced jobs and employment overall. This paper's goal is to identify and redefine the fundamental skills that are needed in the job market in relation to the massive application of artificial intelligence. The methodology used includes a case study and document analysis. The case study is centred on the Finnish universities that have implemented a project to connect academic training and professional requirements in the era of artificial intelligence. The results of this paper suggest a radical shift from the usual and well-known labour-oriented competencies to a far more complex cognitive and interdisciplinary array of skills like complex problem-solving, emotional intelligence, and flexibility. Most importantly, the case study that this paper brings forward underscores that the educational process and innovation must be sustained in the job market because technology can never be stagnant and is continuously redrawing the boundaries of the job's responsibilities. In a nutshell, the competencies required in the labour market will have to be recast with the integration and development of artificial intelligence systems. Such developments need proactive actions from stakeholders, especially educational institutions, through which a proper channel is established for the smooth coexistence of the workforce in the future headed by artificial intelligence.

(Series Information) European Papers - A Journal on Law and Integration, 2020 5(2), 1087-1097 | European Forum Insight of 20 September 2020 | (Table of Contents) I. Introduction. - I.1. AI and the need for a system of governance. - I.2. Introduction to AI and the risks it poses to fundamental rights. - II. An assessment of the Commission's concerns surrounding the EU's primary legal framework's suitability to address the risks posed by AI to fundamental rights. - III. An analysis of how well the GDPR addresses the challenges posed by AI to the fundamental rights of privacy, personal data protection, and non-discrimination. - IV. Conclusion. | (Abstract) In early 2020, the European Commission published a White Paper on artificial intelligence (AI) regulation, in which it highlighted the need to review the EU's legislative framework with a view to making it fit for the cur-rent technological developments. The aim of this Insight is to carry out such review from the perspective of fundamental rights. The Insight briefly assesses the Commission's concerns surrounding the suitability of the Union's primary legal framework to address the risks posed by AI. More specifically, the analysis is focused on the question concerning how well the GDPR addresses the challenges posed by AI to the fundamental rights of privacy, personal data protection, and non-discrimination - which are the three main intersections between AI and fundamental rights. The perspective adopted in the present study is particularly relevant because the need to regulate AI through the lens of fundamental rights law is still largely underdeveloped. Fundamental rights concerns are mainly triggered when the development and use of AI concern the processing of personal data and thus fall within the scope of application of the GDPR. In this Insight, it is argued that the GDPR is well equipped to disruptively challenge actual or potential undesirable uses and applications of AI but some deficiencies are also clearly visible. In particular, in relation to the concept of specific consent, the scope of the data subject's right to information, and on how best to conduct data protection impact assessments when it comes to guaranteeing a trustworthy fundamental rights compliance of the technology.

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

AI-based chat agents within educational settings present a promising pathway for bolstering collaborative learning (CL), especially in the sphere of English as a Foreign Language (EFL). This paper investigates the evolving role of chat agents based on Artificial Intelligence (AI) and Large Language Models (LLM), notably ChatGPT, in the landscape of CL specific to EFL. The primary aim is twofold: firstly, to discern the productive and unproductive applications of ChatGPT in a CL environment, and secondly, to investigate approaches for harnessing ChatGPT to amplify productive CL experiences in EFL contexts. We present a literature review addressing the main objective of this paper, i.e., the exploration of the productive and unproductive uses of ChatGPT for CL in the EFL context. The findings aim to provide insights into the capabilities and limitations of ChatGPT in fostering language skills, enhancing communication competence, and encouraging cooperative learning strategies among EFL learners. This present paper delineates approaches and possible pitfalls when employing AI chat agents in language learning, attempting to bridge the divide between AI technology and instructional design, aiming to facilitate a more informed and efficacious utilisation of AI within collaborative EFL learning environments.

Artificial Intelligence constitutes one of the most fundamental pillars for the implantation of the EU Digital Agenda. Its impact both in private and public life is omnipresent. AI has become an inherent part of the political life since politicians use it for several reasons, such as to promote their strategy as well as to achieve better and closer communication with people. All this is based on the existing set of legal rules. However, there are significant issues, both ethical and legal, which pose a wide range of concerns: from the protection of fundamental rights and freedoms to the safeguard of the principle of rule of law. The core question is the following: does AI strengthen democracy or lead to its deterioration? This paper aims at demonstrating the implementation of AI in politics. Firstly, there will be pursued, via a juridical methodology, a description of the regulatory framework governing AI, in connection with justice and democracy. Following a critical approach, there will be an analysis of principal ethical and legal concerns regarding the necessity and/or efficiency of use of AI in political life. Finally, the ultimate goal of the paper is to stimulate critical thinking and suggest fruitful proposals for the safeguard of the democracy and the establishment of a trustful and powerful digital environment.

This paper delves into the integration of artificial intelligence within Estonia’s education system, showcasing the country’s global reputation for digital innovation. Utilising the method of the case study and the method of document analysis, we explore not only the practical outcomes, challenges, and strategies related to the adoption of artificial intelligence in the Estonian education system but also its economic implications. The research highlights artificial intelligence’s potential to lead to cost efficiencies and economic benefits for the education sector. Furthermore, we address the critical issue of fostering artificial intelligence literacy among students, schools, and universities, pinpointing data privacy and ethical considerations as key areas of concern. The strategies implemented by educational institutions to mitigate these challenges and promote a comprehensive understanding of artificial intelligence are also discussed. In conclusion, the paper underscores artificial intelligence’s transformative potential in reshaping education, offering guidance for other nations and academic entities aiming to successfully integrate artificial intelligence-based technologies. Emphasis is placed on the importance of a supportive policy framework and a solid ethical foundation to navigate the complexities of artificial intelligence adoption and ensure responsible use, while also considering the economic impact on the educational ecosystem and the labour market at large.

Copyright of Canadian Journal of Bioethics / Revue canadienne de bioéthique is the property of Ecole de Sante Publique de l'Universite de Montreal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Activités is the property of ARPACT and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Context and background The incorporation of artificial intelligence (AI) into businesses has become increasingly widespread in recent years, particularly in developed countries, fundamentally reshaping the landscape of entrepreneurship and innovation. AI presents numerous opportunities for small businesses, including those involved in cross-border trade. It has the potential to transform various aspects of business operations, such as customer interactions, marketing research, understanding consumer behavior, optimizing routes, and forecasting sales and marketing trends. Additionally, AI can contribute to the formalization of business processes, and assist these businesses in adapting more effectively to the ever-changing dynamics of the market. However, the successful implementation of AI necessitates a meticulous assessment of AI applications to ensure alignment with the objectives and resources of each individual small business. Goal and Objectives: The primary focus of this paper is to explore the potential of Artificial Intelligence in enhancing Africa's cross-border trade. The paper will address the following research questions: What are the effective applications of artificial intelligence in cross-border informal trade in Africa, and what are the limitations? What does the future hold for African countries in terms of utilizing artificial intelligence for cross-border trade? How can African informal cross-border traders and their stakeholders benefit from incorporating artificial intelligence in their activities? Methodology: The research is purely empirical research based on observation and measurement of phenomena, as directly experienced by the researcher. Results: AI can be effectively applied by Informal Cross-Border Traders (ICBT) to enhance their businesses and enhance competitiveness. There are several AI applications accessible to ICBT within their operational context. Although the adoption and utilization of AI in Africa are still in their infancy, there is considerable promise for the future. Africans must address the challenges hindering the adoption and utilization of AI, as technology is advancing rapidly, and opportunities await those who embrace it.

Copyright of Synergies France is the property of GERFLINT (Groupe d'Etudes et de Recherches pour le Francais Langue Internationale) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates how Artificial Intelligence (AI) can improve Hotel Property Management Systems (PMS) in the hospitality industry. It traces the evolution of PMS from manual to modern AI-infused counterparts, demonstrating how AI improves effectiveness and guest satisfaction. AI-driven PMS significantly improve efficiency in operations, making hotels more competitive in a dynamic landscape, by focusing on dynamic pricing strategies, real-time brand monitoring, and streamlined customer experiences. The paper highlights the practical significance of AI in the hospitality industry, promoting to a better understanding of technology's role in providing customized services and operational excellence, ultimately improving the quality of travel experiences.

The present paper functions as an introduction to this special issue of mediAzioni, which brings together selected papers presented at the Translating Europe Workshop entitled "L'intelligenza artificiale per la traduzione verso una nuova progettazione didattica?" (3 December 2021), focusing on the pedagogical implications of Artificial Intelligence (AI), with a particular emphasis on Neural Machine Translation (NMT). The article revolves around three main themes. First, it considers the impact of emerging neural technologies on language services, redefining the traditional role of the translator and creating new responsibilities and tasks. Despite the considerable progress that NMT has made compared to previous models, it still has shortcomings that are almost "invisible" because they often lack clear grammatical or morphosyntactic marking. Such errors require a certain cognitive effort to identify, since they involve problems that are less obvious to the untrained eye, such as textuality or inadequacies at the level of discourse. Secondly, the article looks more closely at professional practices, especially those related to pre-editing, post-editing and proofreading. Particular emphasis is placed on the indispensable role of the human in translation workflows that integrate NMT not only at the level of post-editing and text revision, but also at the level of translation project management. Finally, the paper moves on to the implications of NMT for the training of translators and beyond, discussing key insights from the workshop participants regarding future teaching practices at university level. The article concludes by outlining the structure of this special issue and providing a brief synopsis of the collected papers.

Waste management has become an increasingly pressing issue due to urbanization, population growth, and economic development. According to World Bank projections, waste production will reach 3.4 billion tonnes by 2050. The paper is focused on detailed analysis of waste management techniques that has to be improved and resources to be maximized, to be able to deal with various types of waste, including agricultural waste, industrial waste, municipal solid waste (MSW), and electronic waste (e-waste). The advancement in the artificial intelligence in various fields has drawn the attention towards utilizing its benefits in achieving optimized management of different types of wastes also. The paper is focused on description of on-recyclable waste materials which can be transformed into energy by using waste-to-energy (WTE) technologies. The different types of wastes generated in different sectors are being studied with details on their quantity and challenges in handling the wastes. The literature highlights the performance analysis of various methodologies of waste handling in terms of their efficiency, economic impacts and ecological implications. The prediction models and their performance was discussed with respect to the R2 value and mean absolute error (MAE) root mean square error (RMSE) to find the most suitable algorithm. The conclusion suggested that these AI based optimization methods can bring about enhancement in the various waste to energy conversion process making the management of waste materials more sustainable and reliable.

This review paper explores the transformative role of big data analytics in geotechnical engineering, transferring past conventional methods to a data-driven paradigm that complements decision-making and precision in subsurface investigations. By integrating large statistics analytics with geotechnical engineering, this study demonstrates big improvements in website characterization, danger assessment, and production methodologies. The research underscores the capability of big data to revolutionize geotechnical investigations through improved prediction models, threat management, and sustainable engineering practices, highlighting the critical role of big data in addressing international warming and ozone depletion. Through the examination of numerous case studies and AI-driven methodologies, this paper sheds light at the efficiency gains and environmental benefits attainable in geotechnical engineering.

This study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) algorithms, which have considerably enhanced the precision and effectiveness of water quality tracking systems. The study analyzes the integration of these innovations into water treatment methods, focusing their ability to more efficiently identify and reduce contaminants compared to traditional techniques. This paper examines a collection of case studies in which artificial intelligence (AI)-powered devices have been used, showcasing significant developments in the evaluation of water quality and improved levels of treatment efficiency. The present study additionally analyzes the various problems and potential future developments of Artificial Intelligence (AI) and Machine Learning (ML) within this particular domain. These challenges cover issues of scalability, data security, as well as the importance for interdisciplinary collaboration. This paper gives a comprehensive analysis of the impact of AI and ML technologies on water quality management, demonstrating their potential to transform current practices towards greater sustainability and efficiency.

This research paper provides a comprehensive review of the current state-of-the-art and prospects of smart materials for sensing and actuation applications. Smart materials, with their unique ability to respond to external stimuli, have been the subject of extensive research and development in recent years. The paper begins by discussing the various types of smart materials, including piezoelectric materials, shape memory alloys, and electroactive polymers, and their properties and applications in sensing and actuation. the paper covers the advancements in the design and fabrication of smart materials and devices, including the use of nanotechnology and 3D printing. The potential for integration with emerging technologies, such as artificial intelligence and the internet of things, is also explore. The paper provides a comprehensive and in-depth analysis of the state-of-the-art and prospects of smart materials for sensing and actuation applications. The research presented has significant implications for the development of nextgeneration smart materials and devices, with the potential to revolutionize various industries and improve our quality of life.

This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.

This research paper investigates the future of advanced materials processing, with a focus on the innovations and challenges that lie ahead. The study begins by exploring the current state of advanced materials processing and the latest trends in the field, including the use of advanced manufacturing technologies, such as additive manufacturing, to create complex geometries and novel materials. The paper then examines the challenges facing the field, including the need to develop new processing techniques that can handle a wider range of materials and produce materials with specific properties. The study also analyses the potential impact of emerging technologies, such as artificial intelligence and machine learning, on the future of materials processing. Finally, the paper concludes with a discussion of the key innovations and trends that are likely to shape the future of materials processing, including the use of sustainable materials, the development of new nanomaterials, and the integration of advanced sensors and data analytics into the manufacturing process. Overall, this research paper provides a comprehensive analysis of the future of advanced materials processing and highlights the critical role that innovation will play in shaping the field in the coming years.

Copyright of Revue Klesis is the property of Revue Klesis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Résumé: Le congrès européen 2018 de l'ISPOR Europe (International society for pharmacoeconomics and outcomes research) s'est tenu à Barcelone du 10 au 14 novembre 2018. Lors de ce congrès, ont été débattus les sujets d'actualités qui agitent le monde de la santé et bien au-delà : la santé numérique. Un groupe de pharmaciens, médecins et économistes en santé présents à Barcelone, a choisi de vous en restituer les messages forts. Dans cet article, les auteurs évoquent les questions issues des différentes sessions et/ou posters : après la libre circulation des personnes, des biens et services, l'Europe et le reste du monde sont confrontés à un nouveau mouvement : la « libre circulation des données », ce changement est-il une opportunité ou une menace pour la vie privée des patients ? La santé numérique peut-elle être source d'inégalités sociales ? Les métiers de la santé sont-ils menacés par l'intelligence artificielle (IA) notamment les économistes en santé ? Quelle sécurité dans l'utilisation des applications mobiles (Apps) et objets connectés (OC) ? Autant de questions discutées dans cet article pour l'enthousiasme ou le scepticisme qu'elles suscitent. The 2018 european congress of the ISPOR Europe (International society for pharmacoeconomics and outcomes research) was held in Barcelona from 10 to 14 November 2018. During the congress, the news topics that are stirring the world of health and far beyond were discussed: digital health. A group of pharmacists, physicians and health economists attended the conference and reported some of the highlights. In this paper, the authors discussed the following questions from the different sessions and/or posters. After the free movement of people, goods and services, Europe and the rest of the world should deal with a new trends: the "free flow of data". Is this change an opportunity or a threat to the privacy of patients? Can digital health be a source of social inequality? Are health professions threatened by artificial intelligence (AI) especially health economists? What security in the use of mobile applications (Apps) and connected objects (OC)? These are issues of both enthusiasm and concern that will be discussed according to the following plan. [ABSTRACT FROM AUTHOR]

Objective: This paper is dedicated to Artificial Intelligence and it consists in identifying the role and functions performed by Artificial Intelligence by noting the innovations brought by the introduction of this technology. Artificial Intelligence seems to be the most successful achievement of scientists today. As a result of this development and widespread use have arisen discussions on the legal regulation of Artificial Intelligence. Methodology: The main methodological approach applied is the normative approach. The paper will be based on the qualitative method, which consists of collecting and processing data in order to compare and interpret the provisions or policies that provide for artificial intelligence. The following research methods will be applied in this paper: research method, descriptive method, comparative and analytical method, interpretive analysis method and illustrative method. Results:There is still no proper legislative framework on Artificial Intelligence. Despite not having a consolidated legal framework, states have made the first efforts in drafting legal acts and norms governing the field of Artificial Intelligence. From the concrete analysis of existing legislative acts in many countries, we conclude that the intention of the legislator in trying to regulate the field of Artificial Intelligence arises as a result of the disadvantages of using these technologies, their widespread use and ability to influence the way of the development of many processes. Contributions: The article contributes to the theoretical treatment of Artificial Intelligence by arguing on the problems of its legal regulation.

Il saggio affronta in modo innovativo la delicata questione del sistema carcerario. Elemento centrale dell’elaborato è la pena pecuniaria, un prezioso strumento sanzionatorio che gli antichi greci hanno donato al sistema penale e che, come evidenziato dalla Prof.ssa Eva Cantarella, ha consentito il passaggio dal Mondo della Vendetta al Mondo del Diritto. L’Autrice sostiene quanto affermato dal Prof. Emilio Dolcini fin dagli anni ’70 del Secolo scorso, in tema di introduzione del sistema a tassi giornalieri, aggiungendo nuovi elementi. Il Case study è il modello dei tassi giornalieri del sistema penale peruviano che si avvale anche dell’utilizzo di un software, e che, secondo la visione dell’Autrice, è una strada possibile anche in Italia per avere un effetto benefico a catena sul sistema carcerario nel suo complesso. Molti passi avanti sono stati compiuti in tema di pene pecuniarie attraverso la Riforma Cartabia che ha consentito di risolvere importanti problematiche del quadro normativo precedente, ma il presente scritto si soffermerà sui nodi ancora irrisolti in quanto punta ad affrontare le criticità ancora esistenti. The essay tackles the delicate issue of the prison system in an innovative way. The central element of the paper is the pecuniary penalty, a precious sanctioning tool that the ancient Greeks gave to the penal system and that, as Prof. Eva Cantarella pointed out, allowed the passage from the World of Vengeance to the World of Law. The author supports what Prof. Emilio Dolcini has been saying since the 1970s about the introduction of the daily rate system, adding new elements. The case study is the model of daily rates in the Peruvian penal system, which also makes use of software, and which, in the author's view, is a possible way also in Italy to have a beneficial knock-on effect on the prison system as a whole. Many steps forward have been taken in the area of financial penalties through the Cartabia Reform, which resolved important issues of the previous regulatory framework, but this paper will focus on the still unresolved knots as it aims to address the still existing critical issues.

Digitalization is transforming every aspect of life, it is also transforming deeply medicine. The digitalization era is characterized by a large production of new data streams while existing processes are progressively migrated, such as writing or imaging. The very large and fast-growing amount of data available requires new storage, transport and analytical tools. This paper presents some of them, such as natural language processing, artificial intelligence, and graph databases. A short introduction to the technology of blockchain is also provided, as it is increasingly used in some non-monetary transaction in medicine, such as data exchanges and consent management., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêt en relation avec cet article.

Artificial Intelligence and Language Technologies in the Field of Translation The present paper deals with the opportunities which language technologies and artificial intelligence might offer for translation professionals. The paper also examines and elucidates that these technologies might contain possible problems, hurdles and obstacles. But the researcher reinforces that they also possess the necessary skills, competencies and prerequisites for the successful implementation and use of these technologies as illustrated in the Literature Review and Discussion parts of this research. It has also been shown that the successful implementation of such tools requires both institutional and technical prerequisites. A not inconsiderable problem, however, is the question of the what extent to which and whether the significance of the digital translator can also be adequately illustrated and clarified in the non-institutional private sector. Current developments in this respect unfortunately tend to point to the opposite, i.e. to the degradation of his role to a mere "helper" of the machine that (apparently) does most of the translation work. Translators, then, will ultimately only be able to truly benefit from language technology and artificial intelligence if their status is not further compromised as a result. It is not very helpful if productivity and speed can be increased, but in inverse proportion to this, the fees constantly decrease, because it is made to appear that the computer is now doing most of the work. The end result of this development would be the digital slave rather than the highly professional digital language mediator. The paper projects that language technologies and artificial intelligence potentially offer benefits in the sense that they promote a better understanding and functioning of the symbiosis between humans and machines through the automation and streamlining of certain language-related workflow processes, i.e., a set of tasks that can be performed primarily - or even entirely - through workflow automation and with the help of content management software. Through the various past research on the subject and through secondary data analysis it has been analysed and stated in the paper that the potential acceleration of work processes improves overall efficiency by requiring less time to complete the same tasks. Therefore, this study evidently establishes the argument that artificial intelligence allows human actors to focus on more meaningful tasks associated with quality control, while routine and especially technical tasks are delegated (for the most part) to the machine. This also leaves more time for creative aspects. Artificial intelligence and language technology are not only relevant for translating, but also for interpreting, even if progress in this area is still somewhat slower and a paradigm shift has yet to occur. In this regard, conference interpreting is of particular interest. Problematically, the relevant software has so far lacked the cognitive, cultural, intellectual, and emotional skills that inevitably underlie qualitatively responsive interpretation. But at least it is possible to improve the interpreter's preparation for a meeting or conference. The relevant tools have so far only a supporting role to play. Language technologies and artificial intelligence potentially offer benefits in the sense that they promote a better understanding and functioning of the symbiosis between humans and machines through the automation and streamlining of certain language-related workflow processes, i.e., a set of tasks that can be performed mainly - or even entirely through workflow automation and with the help of content management software. Further development of artificial intelligence, machine translation has recently made significant progress. Nevertheless, it is common practice to first subject the machine-translated text to review or proofreading by a human translator. Post-editing is the process of using a machine-translated text as a basis and having it improved by a human translator. This means that the human translator ultimately creates the final translation.

The paper intends to propose a reading of Ian McEwan’s latest novel, Machines Like Me (2019), qua insightful reflection on the topic of Artificial Intelligence and its bearings on different aspects of human life, from interpersonal relationships to moral behaviour. At the same time, the novel also engages in a reflection on the value and the prospects of literature, whose very premises might be called in question in a posthuman context. Set in a 1980s world whose contours radically deviate from historical facts – the atomic bomb was never dropped; Kennedy was not killed in Dallas; the Beatles are still a band; and Alan Turing has survived the conviction for homosexuality and successfully carried on his studies on AI – the novel introduces the simulacrum in the form of an android, Adam, a hyper-sophisticated machine that enters the characters’ life and upsets it thoroughly. The troublesome relationship with Adam forces Charlie, the protagonist-narrator, to ponder on his own system of values, posing questions about the Other which inevitably end up throwing new light – but, above all, casting new doubts – on the Self and on what it ultimately means to be human. By way of his “What if novel” set in an alternative past, McEwan tackles pressing issues of our present, while trying to envisage a future that is not far to come. The paper intends to explore both the ethical and the metaliterary level of the story on the background of the contemporary theoretical debate on transhumanism and posthumanism, pointing to the simulacrum as the uncanny catalyser of the major topics the author intends to tackle.

Formalization of natural language metaphors is a notorious problem in artificial intelligence and in other overlapping domains. It is semantic vagueness that makes metaphors resistant to formulaic or algorithmic descriptions. Great effort has been invested into modeling metaphors computationally but the issue remains methodologically uncertain and needs further research. This paper works on a practical solution to the problem how metaphorical meaning can be represented in a way suitable for computation. The research agenda of this paper is interdisciplinary; it brings together an algebraic heuristic-driven theory for metaphors developed in artificial intelligence and an applied theory of meaning that comes from cognitive linguistics. This agenda postpones theoretical speculation and argument and is solely solution-focused, which contributes to the value of this paper’s attempt to bridge the cognitive science disciplines whose compatibility, though declared, is seldom demonstrated in a piece of practical research. This paper works with metaphors of human emotions that are linguistically manifested in modern English discourse. Emotions by virtue of their ineffability as qualia are rich in metaphorical conceptualizations and serve the research agenda well. This paper in a meaningful way exposes and ranks designated properties of the FEAR, SADNESS, HAPPINESS, and RELAXATION/SERENITY concepts and arranges these properties into general-purpose ontologies that explicitly specify metaphorically preferred emotion conceptualizations and are good candidates for computation. In prospect, this paper will account for some theoretical aspects of the research and probe the algorithmic and repetitive nature of schemas that license metaphorical expressions in natural language.

The paper focuses on the prospects of recognizing artificial intelligence as a subject of crime and the presence of artificial intelligence as Mens Rea and Actus Reus. The paper aims to do the following: (1) study the international experience of the criminal justice response to crimes committed with the use of artificial intelligence based on the systematic and comparative analysis, and (2) answer the question of the possibility of recognizing artificial intelligence as a subject of crime. The research is based on a wide range of international sources and data from international organizations, national legislation, and scientific literature. Within the study, we have used the following research methods: historical, comparative, formal-legal, and functional methods, and a systematic approach. As a result, we have found that artificial intelligence has the ability to implement all three components of guilt: act, direct causal link, and occurrence of socially dangerous consequences. Consequently, we have concluded about the possible definition of artificial intelligence as a special subject of crime. Therefore, we have proposed considering some of its fundamental properties as possible criminological prerequisites for recognizing artificial intelligence as a special subject of crime.

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Il contributo si propone di indagare i meccanismi di automazione nel processo civile nelle procedure concorsuali, attraverso l'analisi della normativa di riferimento sia del processo telematico che del Codice della crisi d’impresa e dell’insolvenza. A tal fine, si prenderà in considerazione il grado di automazione dei procedimenti, evidenziandone le criticità e le funzioni non ancora implementate; si procederà inoltre ad una comparazione con i sistemi processuali di Germania, Francia e Spagna; si tratteggeranno infine le conclusioni in vista di prospettive future utili ad una maggiore armonizzazione tra procedimenti giurisdizionali e tecnologia. The paper aims to investigate the mechanisms of automation in civil and insolvency procedures, through the analysis of the reference legislation of both the telematic process and the New Insolvency Law. To this end, the degree of automation of proceedings will be taken into consideration, highlighting critical issues and functions not yet implemented; a comparison will be made with the procedural systems of Germany, France and Spain; conclusions will be drawn in view of future outlooks useful for a greater harmonisation between judicial proceedings and technology.

Artificial intelligence (IA) is the subject of much research, but also many fantasies. It aims to reproduce human intelligence in its learning capacity, knowledge storage and computation. In 2014, the Defense Advanced Research Projects Agency (DARPA) started the restoring active memory (RAM) program that attempt to develop implantable technology to bridge gaps in the injured brain and restore normal memory function to people with memory loss caused by injury or disease. In another IA's field, computational ontologies (a formal and shared conceptualization) try to model knowledge in order to represent a structured and unambiguous meaning of the concepts of a target domain. The aim of these structures is to ensure a consensual understanding of their meaning and a univariant use (the same concept is used by all to categorize the same individuals). The first representations of knowledge in the AI's domain are largely based on model tests of semantic memory. This one, as a component of long-term memory is the memory of words, ideas, concepts. It is the only declarative memory system that resists so remarkably to the effects of age. In contrast, non-specific cognitive changes may decrease the performance of elderly in various events and instead report difficulties of access to semantic representations that affect the semantics stock itself. Some dementias, like semantic dementia and Alzheimer's disease, are linked to alteration of semantic memory. We propose in this paper, using the computational ontologies model, a formal and relatively thin modeling, in the service of neuropsychology: 1) for the practitioner with decision support systems, 2) for the patient as cognitive prosthesis outsourced, and 3) for the researcher to study semantic memory.

In sub-Saharan Africa, there is a significant unmet need for emergency care, with a shortage of trained providers. One model to increase the number of providers is to task-share: roles traditionally filled by clinicians are shared with lay workers who have received task-specific training. Separately, there has been much recent interest in the possible implications of artificial intelligence (AI) on healthcare. This paper proposes that, by combining the task-sharing model with AI, it is possible to design an Emergency Unit (EU) that shares the tasks currently undertaken by physicians and nurses with lay providers, with the activities of lay providers guided and supervised by AI. The proposed model would free emergency care clinicians to focus on higher-acuity and complex cases while AI-supervised routine care is provided by lay providers. The paper outlines the model for such an implementation and considers the potential benefits to patient care, as well as considering the risks, costs, effect on providers, and ethical questions. The paper concludes that, AI and healthcare workers can operate as a team, with significant potential to augment human resources for health in sub-Saharan Africa.

In this paper, we applied the artificial intelligence technique (SVM Classifier) to compare the performance of two different technologies of PV modules (class to class and backsheet to glass) after five (05) months of operation in Algeria under the same weather conditions (moderate and humid climate) . We have a database for the outdoor monitoring of these two PV modules, consisting of data (Isc, Voc, Pmax, Imp, Vmp, Tm, Tamb, G, WD, WS, Date, Time) which are variables data, where the SVM creates the groups or class according to the conditions that we entered, after which it produces heatmaps that help us in reading the results and making the decision easily, unlike the classic methods which are very difficult. This method is applicable for comparison between several solar panels or several photovoltaic PV plants. It is enough just to give the database.

The most important property, a distinctive feature of any intelligent system, is its decision-making ability. In this case, the more complex the problem to be solved, the more and more diverse the initial data, and the more critical it is that the decision to be made was comprehensively considered and evaluated. In many cases, simultaneously arriving various initial data, if considered separately, and decisions based on such consideration lead to completely different results, often contradicting each other. Therefore, in the process of development and implementation of artificial intelligence (AI), it is especially important to investigate the “mechanism” of decision-making in conditions of the inconsistency of incoming initial data and the need to establish some generalizing rule, according to which it is possible to find a harmonizing solution taking into account various influencing factors. It is evident that when establishing the rules of decision-making, it is necessary to strive for a “positive” result from the point of view of the problem being solved. This undoubtedly requires analyzing the consequences of the decision made in a set time scale, which can be provided by appropriate feedback that will allow us to make the necessary corrective actions. Artificial intelligence in modern forms of practical realization has, as a rule, a digital embodiment. It should be taken into account that the digital representation of data inevitably shows an inaccurate display of initial values when processes of a continuous nature are considered and analyzed. Since a digital model has certain limitations and characteristic properties when analyzing and processing initial data, it is logical to assume that for this reason, there can be some general approach, some general rule, according to which a decision is made in the conditions of diverse initial data and the need to take into account the relevant consequences after the decision is made. This paper attempts to find a decision-making mechanism, harmonizing it according to the incoming external and available internal input data.

In today's professional communication, a constant expansion of vocabulary is necessary to keep up with developments in technology and science. This paper looks at the current topic of artificial intelligence in relation to applications such as ChatGPT and chatbots. The investigation will also explore the potential of artificial intelligence in promoting and supporting linguistic skills. Additionally, the discussion will cover the implementation of these AI tools into foreign language curricula and classroom practices. Specific requirements for teachers and students are addressed under the aspect of future competencies, which represent a particular challenge given the high speed of diverse developments. This study contributes to the ongoing discussion regarding the role of artificial intelligence in higher education. Overall, this evaluation offers insights into a highly topical issue of AI application that will significantly influence linguistic and pedagogical practice in the future.

This paper explores the evolving perception of artificial intelligence (AI) systems in our increasingly AI-driven world, focusing on conversational AI or chatbots. With the rise of human-like robotic AI systems, there is a growing need to understand the identity these machines assume as they take on roles previously reserved for humans, such as caring for the elderly and educating children. The central thesis posits that comprehending the identity of such AI systems necessitates considering the aspect of our own identity that we shape and project onto them in our interactions with these systems. In an era defined by AI, these inquiries into identity and AI-human interactions are of paramount importance and are also vital for the very development of AI.