No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Artificial Intelligence - A Modern Approach
... Biodiversity contributes to the resilience of ecosystems (Haines and Potschin, 2010), allowing them to adapt to changes and stresses. Additionally, many species provide ecosystem services (Russel and Norvig, 2016) such as pollination, seed dispersal, and pest control that are crucial for human survival and economic activity (Deng et al., 2009) Furthermore, wildlife has intrinsic value, which means that every species has a role in the ecosystem , regardless of its direct utility to humans. Unfortunately, the alarming rates of species extinction and habitat degradation call for innovative approaches to conservation. ...
... Numerous species, like the American bald eagle and the gray wolf (Haines and Potschin, 2010), have rebounded thanks to these efforts. Furthermore, the development of international treaties, like the Convention on International Trade in Endangered Species (CITES), aims (Russel and Norvig, 2016) to regulate the trade of endangered species and their products (Deng et al., 2009), minimizing poaching and illicit trade. ...
... For instance, biased data can lead to skewed outcomes , potentially perpetuating inequalities. Additionally, the deployment of AI in surveillance and military applications (Deng et al., 2009) raises ethical dilemmas that challenge our understanding of privacy and human rights (Russel and Norvig, 2016). ...
The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection and preservation of biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing habitat health and tracking changes over time. AI-driven image recognition tools enable the identification of individual animals from camera trap photos, facilitating more accurate population estimates and behavioral studies. Moreover, predictive analytics play a crucial role in forecasting human-wildlife conflicts and informing proactive management strategies. This synthesis of AI technologies demonstrates their potential to enhance conservation efforts, optimize resource allocation, and ultimately foster more effective wildlife protection initiatives. The ongoing advancement of AI in this field promises to create innovative solutions to some of the most pressing challenges.
... No entanto, como qualquer tecnologia da informação, a Inteligência Artificial não é neutra. Destaca-se o fato de que o uso de ferramentas baseadas em IA pode alterar a forma de viver em sociedade e as decisões tomadas, com base no conteúdo apresentado pela IA. (Russel;Norvig, 2020). ...
... No entanto, como qualquer tecnologia da informação, a Inteligência Artificial não é neutra. Destaca-se o fato de que o uso de ferramentas baseadas em IA pode alterar a forma de viver em sociedade e as decisões tomadas, com base no conteúdo apresentado pela IA. (Russel;Norvig, 2020). ...
... O AM é um subcampo da IA que se dedica à análise da capacidade de aprimorar o desempenho por meio da experiência adquirida (Russel;Norvig, 2020). No AM, o computador é treinado a partir de uma base de dados histórica, a fim de prever resultados em situações similares, entretanto, nem todos os sistemas de IA fundamentam-se em métodos de Aprendizado de Máquina para atingir sua competência. ...
... No entanto, como qualquer tecnologia da informação, a Inteligência Artificial não é neutra. Destaca-se o fato de que o uso de ferramentas baseadas em IA pode alterar a forma de viver em sociedade e as decisões tomadas, com base no conteúdo apresentado pela IA. (Russel;Norvig, 2020). ...
... No entanto, como qualquer tecnologia da informação, a Inteligência Artificial não é neutra. Destaca-se o fato de que o uso de ferramentas baseadas em IA pode alterar a forma de viver em sociedade e as decisões tomadas, com base no conteúdo apresentado pela IA. (Russel;Norvig, 2020). ...
... O AM é um subcampo da IA que se dedica à análise da capacidade de aprimorar o desempenho por meio da experiência adquirida (Russel;Norvig, 2020). No AM, o computador é treinado a partir de uma base de dados histórica, a fim de prever resultados em situações similares, entretanto, nem todos os sistemas de IA fundamentam-se em métodos de Aprendizado de Máquina para atingir sua competência. ...
A era da informação trouxe consigo uma revolução no modo como dados são gerados, coletados, processados e utilizados. No entanto, com essa evolução tecnológica, emergem preocupações significativas sobre a ética e os vieses que podem influenciar essas práticas. Este livro, composto por oito capítulos independentes, oferece uma análise abrangente sobre o tema ética e vieses em dados, abordando diversas perspectivas e contextos. A obra é resultado da disciplina “Ética e Vieses em Dados”, ofertada no primeiro semestre de 2023, aos discentes dos cursos de mestrado e de doutorado do Programa de Pós-Graduação em Ciência da Informação, da Universidade Federal do Paraná (UFPR). O primeiro capítulo do livro explora o fenômeno da infodemia, caracterizado pelo excesso de informação, incluindo desinformação e fake news, que sobrecarregam as pessoas e dificultam a obtenção de informações precisas. A análise foca nos impactos sociais e éticos desta inundação de dados e informações, destacando as implicações para a confiança pública e a tomada de decisão informada. Este capítulo inicial estabelece a base para entender a relevância da ética na manipulação e disseminação de dados, inclusive em tempos de crise global. O segundo capítulo enfoca a ética e os vieses no aprendizado de máquina, discutindo como algoritmos podem incorporar e perpetuar preconceitos presentes nos dados de treinamento. A análise inclui estudos de caso que ilustram a aplicação prática de princípios éticos no desenvolvimento e implementação de sistemas de Inteligência Artificial (IA), além de sugerir metodologias para a mitigação de vieses. Em seguida, no terceiro capítulo, o livro explora os direitos dos indivíduos à revisão de decisões automatizadas, destacando os desafios legais e éticos associados ao uso de sistemas de IA em contextos empresariais. Este capítulo proporciona uma visão detalhada sobre a legislação vigente e a necessidade de supervisão humana para garantir a equidade e a transparência nos processos decisórios automatizados. O quarto capítulo investiga as interações entre as leis de proteção de dados e os vieses em decisões automatizadas, analisando como diferentes jurisdições abordam a proteção contra discriminações algorítmicas. Este segmento enfatiza a importância de políticas robustas para assegurar que os sistemas automatizados respeitem os direitos fundamentais dos cidadãos. Prosseguindo, no quinto capítulo, são analisados o comportamento informacional e a percepção dos usuários sobre privacidade e segurança de dados, oferecendo uma visão crítica sobre como as preocupações dos indivíduos podem influenciar a adoção de tecnologias emergentes. Este capítulo é essencial para compreender as dinâmicas entre a confiança do usuário e a implementação de sistemas baseados em dados. O sexto capítulo examina as questões éticas em torno do uso de big data para vigilância e controle social, discutindo os riscos e benefícios destas práticas. A análise é aprofundada com exemplos de como grandes volumes de dados podem ser usados tanto para o bem público quanto para infringir a privacidade e os direitos individuais. No penúltimo capítulo, são discutidas as implicações éticas da automação e substituição de mão de obra humana por sistemas de IA, ao abordar as questões de responsabilidade e os impactos socioeconômicos dessa transição tecnológica. Este segmento oferece uma reflexão sobre as responsabilidades dos desenvolvedores e das organizações que implementam essas tecnologias. Finalmente, o livro encerra-se com uma análise informacional sobre o impacto de ferramentas de IA, como o ChatGPT, na comunidade acadêmica. Este último capítulo investiga como docentes e discentes utilizam estas tecnologias e as implicações éticas associadas ao seu uso na educação e na produção de conhecimento. A obra oferece uma contribuição significativa para o entendimento das complexas relações entre ética, vieses e dados. Cada capítulo, ao abordar diferentes perspectivas e contextos, enriquece o debate e fornece insights valiosos para pesquisadores, profissionais, docentes e estudantes interessados em navegar pelos desafios da ética e vieses em dados. A disciplina, cuja experiência culminou neste livro, evidencia a necessidade de uma abordagem crítica e ética no desenvolvimento e aplicação de tecnologias baseadas em dados. Aos interessados em continuar a discussão após a leitura do livro, há a possibilidade de assistir às aulas da disciplina, que estão disponíveis no YouTube, na playlist “Ética e vieses em dados”, no canal “PPGCI (Gestão da Informação) UFPR”. Estas aulas são, direta ou indiretamente, utilizadas em cada um dos capítulos que compõem este livro.
... In addition, AI has been the subject of an intense innovation policy discussion, as well as public and media attention, which at times has taken on the character of hype. The dynamic and often contradictory course of AI growth has for a long time been the subject of broad research and extensive debate (Ahrweiler, 1995, Nilsson, 2010, Russel and Norvig, 2010, Görz et al. 2021 [1,52,56,32] . A generally accepted interpretation is that this changing course has been significantly shaped by sometimes exaggerated expectations given the often only limited capabilities of the AI systems and methods available at the respective times and resulting disappointments. ...
... In addition, AI has been the subject of an intense innovation policy discussion, as well as public and media attention, which at times has taken on the character of hype. The dynamic and often contradictory course of AI growth has for a long time been the subject of broad research and extensive debate (Ahrweiler, 1995, Nilsson, 2010, Russel and Norvig, 2010, Görz et al. 2021 [1,52,56,32] . A generally accepted interpretation is that this changing course has been significantly shaped by sometimes exaggerated expectations given the often only limited capabilities of the AI systems and methods available at the respective times and resulting disappointments. ...
... Deep RL often struggles to achieve good performance and the trained system may behave unpredictably if the environment differs even slightly from the training data [16]. ...
... A higher ratio indicates a higher MBS resource assigned to handle the load. The ratio is then feedback to the SBS for RL agents to be used in the reward function, equation (16). The proposed adaptive algorithm can be summarized as follows: // Obtain ratios for each local server at SBS for next time slot t+1: In a distributed system where there is no load control over the MBS resources, although it offers flexibility, it can lead to overloading the MBS if all local servers offload heavily. ...
Computation offloading in Internet of Vehicles (IoV) networks is a promising technology for transferring computation-intensive and latency-sensitive tasks to mobile-edge computing (MEC) or cloud servers. Privacy is an important concern in vehicular networks, as centralized system can compromise it by sharing raw data from MEC servers with cloud servers. A distributed system offers a more attractive solution, allowing each MEC server to process data locally and make offloading decisions without sharing sensitive information. However, without a mechanism to control its load, the cloud server’s computation capacity can become overloaded. In this study, we propose distributed computation offloading systems using reinforcement learning, such as Q-learning, to optimize offloading decisions and balance computation load across the network while minimizing the number of task offloading switches. We introduce both fixed and adaptive low-complexity mechanisms to allocate resources of the cloud server, formulating the reward function of the Q-learning method to achieve efficient offloading decisions. The proposed adaptive approach enables cooperative utilization of cloud resources by multiple agents. A joint optimization framework is established to maximize overall communication and computing resource utilization, where task offloading is performed on a small-time scale at local edge servers, while radio resource slicing is adjusted on a larger time scale at the cloud server. Simulation results using real vehicle tracing datasets demonstrate the effectiveness of the proposed distributed systems in achieving lower computation load costs, offloading switching costs, and reduce latency while increasing cloud server utilization compared to centralized systems.
... Just like other technological advancements in the past, while human beings can benefit greatly from new technology, it can also create undesirable and unpredicted outcomes for humanity. The case of fossil fuel combustion machine development causing global warming and environmental issues, and nuclear developments causing nuclear incidents, such as the Chornobyl incident, and increasing the fear of nuclear war are great examples of technological developments' adverse effects (Russel & Norving, 2010;Stein, 2022). Some of the potential risks and ethical issues can be summarized as the autonomous ML ability of AI enhancing its capabilities without any spe-cific algorithms and instruction, which was mentioned in the earlier quote of Steven Hawking's famous warning. ...
Artificial intelligence (AI) is a rapidly developing technology with the potential to create profound changes across various domains, from societal structures to economic systems. This study focuses specifically on the impact of AI on strategic management decision-making processes. The primary aim is to explore how AI can enhance the quality of these processes and contribute to improved organizational performance. A literature review method was employed in the research, through which the theoretical foundations of artificial intelligence, its historical development, and its application areas in decision-making processes were thoroughly examined. The findings indicate that AI accelerates decision-making processes, leading to significant time and cost savings, while also minimizing human error. Decision support systems empowered by AI technologies enable organizations to make more accurate, consistent, and data-driven decisions in strategic management. However, to effectively integrate and benefit from these technologies, organizations must be adequately prepared in terms of both technological infrastructure and organizational culture. In this regard, the study emphasizes that AI is not merely a technical tool, but also a strategic asset that can offer competitive advantages when effectively adopted and utilized
... In his article, Turing argued that computers could be taught to think like humans and that a computer which would be designed in a similar way to a child's mind, could be trained through teaching and experience transfer. According to Crevier (1993), Russel and Norvig (2010), the term artificial intelligence was first used by John McCarthy at the Dartmouth Conference held in 1956. Artificial intelligence studies gained momentum after this conference. ...
... Artificial Intelligence (AI): Artificial Intelligence is a collection of technologies, processes, and approaches vital for the current and future growth of a comprehensive and vital economy [7]. AI enables a system to demonstrate capabilities similar to human intelligence, including understanding, reasoning, learning, interacting, and more [8]. ...
The research aims to identify the problems and details of fraud detection methods in bank transactions using machine algorithms and to provide solutions in this field. Qualitative research is conducted for this aim, and the main problems are identified by reviewing previous research. Then, solutions are presented using the Design Science Research Methodology (DSR). The main topics identified from previous research include Data limitation, labeled data, Discovering new fraud patterns, Bias and Costs, and responsibility for false prediction. The proposed research model has been designed using results from previous studies and experts' opinions in this field. Using both supervised and unsupervised algorithms in the transaction registration process, labeling data based on discovered patterns, obtaining customer confirmation in cases where the system detects fraud, training, and continuous improvement of learning models using the generated data online are among the solutions of the suggested model. Also, it is suggested that the issue of reducing the error of false harmful data in the fraud detection process be investigated in future research.
... No contexto de Inteligência Artificial, podem-se definir agentes como entidades computacionais que, inseridos em um ambiente, são capazes de perceber e atuar sobre o mesmo. Um agente computacional possui atributos como operar sob controle autônomo, perceber seu ambiente, persistir por um período de tempo, adaptar-se a mudanças e ser capaz de assumir metas [3]. ...
Simulation of home use of electric energy is a very powerful tool for the purpose of studying, planning and managing at electric energy distribution companies. This paper presents a NetLogo-based multi-agent system for energy consumption simulation in residential areas. Several possible consumers profiles and household appliances with different powers are modeled and simulated using computational agents. Simulation results are presented and discussed.
... Ultimately, this one boils down to the following question 53 : how can an agent reason that (s)he knows how to perform an action? Indeed, a good planning-logic should explain under what 51 For more information on the matter, the reader is advised to have a look at [91]. 52 See [30], [36] and [86] for instance. ...
The present work is devoted to Computability Logic (CoL), the young and volcanic research-project developed by Giorgi Japaridze. Our main goal is to provide the reader with a clear panoramic view of this vast new land, starting from its core knots and making our way towards the outer threads, in a somewhat three-dimensional, spacial gait. Furthermore, through the present work, we provide a tentative proof for the decidability of one of CoL's numerous axiomatisations, namely CL15. Thus, our expedition initially takes off for an aerial, perusal overview of this fertile steppe. The first chapter introduces CoL in a philosophical fashion, exposing and arguing its main key points. We then move over to unfold its semantics and syntax profiles, allowing the reader to become increasingly more familiar with this new environment. Landing on to the second chapter, we thoroughly introduce Cirquent Calculus, the new deductive system Japaridze has developed in order to axiomatise Computability Logic. Indeed, this new proof-system can also be a useful tool for many other logics. We then review each of the 17 axiomatisations found so far. The third chapter zooms-in on CL15, in order to come up with a possible solution to its open problem. We outline its soundness and completeness proofs; then provide some few deductive examples; and, finally, build a tentative proof of its decidability. Lastly, the fourth chapter focuses on the potential and actual applications of Computability Logic, both in arithmetic (clarithmetic) and in Artificial Intelligence systems (meaning knowledgebase and planning-and-action ones). We close our journey with some final remarks on the richness of this framework and, hence, the research-worthiness it entails.
... (Agent) [34] , , , ...
... As agents are, in fact, essentially abstractions of robots (see the way concept of agent is presented in, e.g., [Russel and Norvig 2009]), one should expect that at some point in time the two areas of Robotics and Multiagent Systems will come closer than they are, at the moment, and the social mechanisms investigated in the area of Multiagent Systems will be instantiated in appropriate ways in terms of societies of autonomous robots. ...
This paper concerns material agent societies, that is, agent societies inhabited by material agents. The paper introduces the concept of energy system of a material agent society, as the organizational sub-systems responsible for the coordination of the production, distribution and consumption of energy in material agent societies. Two models (labor-based and work-based) for energy systems are formally defined, and the conditions of their equilibrated functioning formulated. The possible relation between economic and political systems, and energy systems, in material agent societies, is briefly indicated.
... The field of AI emerged in the 1950s when computer scientists began to investigate the possibility of developing robots that could learn and think like humans, following McCarthy coining the term "artificial intelligence" in 1956 and organising the Dartmouth Conference, which is widely regarded as the genesis of AI [11]. AI does not refer to a single technology, but rather to a set of technologies and methodologies, including machine learning, natural language processing, data mining, neural networks, and algorithms, in which computers that perform cognitive functions similar to human minds, such as learning and problem-solving [12]. ...
... Conceitualmente [15] [18], entende-se por agente autônomo como um sistema fundamentado em uma base de conhecimento, que percebe um ambiente, seja este um mundo físico, representação gráfica, uma coleção de outros agentes ou ambientes complexos; raciocina para interpretar estas percepções; desenvolve inferências; resolve problemas e determina ações, agindo sobre o ambiente para realizar um conjunto de metas ou tarefas para as quais foi projetado. A aplicação que possui mais de um agente é chamada de um sistema multi-agente (SMA). ...
A colaboração baseada em ontologias é um tema interessante, pois permite modelar o conhecimento dos agentes permitindo a interação e troca de informações. Este artigo tem por objetivo mostrar a aplicação dos mecanismos de interoperabilidade existentes atualmente, aplicados na modelagem dos agentes colaborativos através de ontologias. A aplicação dos mecanismos sobre a ontologia do ambiente e as dos agentes pode produzir um ambiente dinâmico propício à colaboração. São abordados os principais mecanismos de interoperabilidade: combinação, integração, alinhamento e mapeamento e sua aplicação em sistemas colaborativos.
... Um agente computacional possui atributos como operar sob controle autônomo, perceber seu ambiente, persistir por um período de tempo prolongado, adaptar-se a mudanças e ser capaz de assumir metas. [Russel, Norvig 2003] Na simulação baseada em agentes, há a característica de haverem muitos agentes interagindo uns com os outros [Parunak 1998], ou seja, é uma simulação de um sistema multiagente (SMA). Com este tipo de simulação pode-se analisar, de que forma cada agente, o qual pode representar uma pessoa, bactéria, inseto ou outro qualquer, interage com o outro e com o seu ambiente. ...
Este artigo apresenta nosso estudo para desenvolvimento de modelos de simulação de mercados econômicos. Todas as características dos modelos de mercado giram em torno de conceitos da Teoria Microeconômica a qual explica o comportamento dos agentes produtores e consumidores. Este modelo é desenvolvido utilizando-se técnicas de Simulação baseada em Agentes. A ferramenta de modelagem e simulação utilizada é o NetLogo, que provê recursos para suporte a simulações participativas.
... Considering, for example, "Statistics is the art of learning from data" [17] and machine learning as a branch of artificial intelligence (AI), the main difference concerns who is learning (human or machine), and many techniques are shared between them. For example, logistic regression is considered as a machine learning approach in one of the key academic textbooks on AI [18], even if it is clearly and widely used in statistics too. It is not the aim of this article to delve into the distinction between statistics and machine learning. ...
Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning regimen, and prediction of post-transplant outcomes. Machine learning approaches, including decision trees, random forests, and neural networks, have demonstrated potential in improving donor compatibility algorithms, mortality and relapse prediction, and GVHD risk stratification. Integrating “omics” data with ML models has enabled the identification of novel biomarkers and the development of highly accurate predictive tools, supporting personalized treatment strategies. Despite promising advancements, challenges persist, including data standardization, algorithm interpretability, and ethical considerations regarding patient privacy. While ML holds promise for revolutionizing HSCT management, addressing these barriers through multicenter collaborations and regulatory frameworks remains essential for broader clinical adoption. In addition, the potential of ML can cope with some challenges such as data harmonization, patients’ data protection, and availability of adequate infrastructure. Future research should prioritize larger datasets, multimodal data integration, and robust validation methods to fully realize ML’s transformative potential in HSCT.
... Proverbs also use improvisation to deal with unstructured problems making analogy applicable to exploring IS solutions as possible proverb problem solutions and vice versa. Cognitive psychology models human thinking as information processing system [27]. Viewing proverbs as ISs and cognitive information processing systems can be used to model AI proverb reasoning systems. ...
Computing and design disciplines knowledge is embedded in computers that are pervasive, used by everyone. To maximize benefits to everyone these disciplines should increase embedded indigenous research and practice knowledge like proverbs in artefacts. The study’s goal was find proverb how proverbs are being applied and research that has been done in computing and design disciplines; and how it can be synthesized. Proverbs are bridges for crossing chasms between theory and practice, design and technology, cultures, disciplines and society driven by their transdisciplinarity. Transdisciplinary literature review method was used. The findings are proverbs are being applied and researched in several areas of computing and design.
This work proposes to consider some safety conditions for humans in the environment, due to their relation to thermodynamic non-equilibrium, understanding this criterion as the possibility of estimating changes in entropy in the ecosystem. As a method of analysis, the regularities of development of such systems are taken sequentially—in the field of thermodynamic equilibrium, weak thermodynamic nonequilibrium, and strongly nonequilibrium systems To calculate such ratios in entropy it is proposed to use the equations of L. Onsager, the regularities that form the basis of I. Prigozhin's theorem, as well as formulas for calculating the entropy of K. Shannon, which determine the relationship with entropy for information systems. Two large groups of ecosystems that have an impact on the state of the environment were analyzed, namely: inorganic or metal-metalloid on the one hand, and a group of systems with a carbon-protein base, with the initial assumption of some competition between them. It is shown that man, as a representative of the second group, in the process of his development encountered a number of technogenic processes that aggravate its claims to thermodynamic disequilibrium and, thus, reduce its opportunities for long-term development and reduce its impact on technogenic safety factors. These include irreversible processes of man-made pollution of the environment, the use of non-renewable mineral and organic resources, the manifestation of technological inertia in production, the reduction of biodiversity due to the fault of humans, etc. One of these reasons is gradually becoming the dependence of man on the properties and qualities of the global information space, as a new man-made factor and its derivatives, which, under certain conditions, can become capable of leading to a dangerous relationship between man and HIP, as parts of the environment - the inability of a person to minimize entropy in the process of his activity. Features of the development of artificial intelligence and HIP As influential man-made factors in the environment, they provide opportunities for comparison of two new man-made bases that belong to separate components of the environment, and which are accepted for analysis in the work, in relation to their equal competition, which man has not yet lost, but will not win
Nesse artigo serão analisadas as interações dos agentes em um ambiente compartilhado por outros agentes, produto das trocas sociais, utilizando como estudo de caso da “A Horta San Jerónimo”, que é uma horta urbana de auto-consumo, localizada na cidade de Sevilla, Espanha. Os estudos serão sustentados na transferência de confiança com base na reputação e na relação de dependência.
Este artigo apresenta um estudo de caso onde personalidades são definidas em agentes, baseadas no modelo de redes Bayesianas de emoções. Os resultados demonstram que, dependendo das personalidades definidas, o desempenho do agente é afetado, aproximadamente como na vida real.
Embora existam modelos teóricos para a modelagem de emoções, estes por si só não são suficientes para uma simulação precisa em meios computacionais. Deste modo, este trabalho propõe a construção de uma rede Bayesiana baseada em um modelo de emoções, o modelo OCC, a qual é aplicada a um ambiente multiagentes, de forma a simular emoções em agentes. O uso combinado de redes Bayesianas e da estrutura do modelo OCC procura adicionar imprevisibilidade ao modelo originalmente determinista, bem como a aplicação do modelo proposto para um sistema multiagentes propicia o estudo da influência das emoções sobre as ações e o comportamento de agentes.
La forma en la que las empresas operan y toman decisiones se está transformando en los tiempos recientes a partir del uso de la Inteligencia artificial (IA), principalmente por su idoneidad para analizar datos a gran escala, aprender de ello y realizar tareas complejas con rapidez. Muhos docentes en Instituciones de Educación Superior, están pendientes de las estrategias que se proponen para atender este fenómeno. Para esta investigación, en primer lugar, se aplicó un cuestionario a una muestra de empresarios y docentes para conocer su apreciación desde las profesiones de la Administración y el Diseño. Dicho cuestionario tuvo un alcance en dos países: México y Colombia durante el segundo trimestre del año 2024. A continuación, se analizaron y discutieron los resultados para encontrar cómo es que el uso de IA influye o debiera influir en la formación de universitarios, futuros colaboradores, proveedores, y emprendedores. Las conclusiones y recomendaciones de esta investigación se orientan a reforzar que el desafío es aprovechar la IA, sin pensar en que va a reemplazar o desplazar a las personas en las organizaciones o generar una dependencia tecnológica; sino más bien, en la posibilidad de potenciar su productividad y agilizar la toma de decisiones con mayor oportunidad y precisión. Finalmente, se invita a la reflexión en el ineludible uso ético y responsable de IA para dar cabida a futuras investigaciones de su impacto en la educación.
The construction industry is currently witnessing a paradigm shift, thanks to the advancements in artificial intelligence (AI) that make it possible to improve site management, increase safety, and further increase efficient construction methods. The scope of this paper focuses on the impact of AI-based technologies in adapting the construction industry and the modification of construction processes. AI technologies such as indicative analytics, machine learning algorithms, and automated project management work to solve major issues like trying to complete a project over budget, mismanaged resources, and pollution. Through AI-powered technologies, various stakeholders can make decisions, increase productivity, and pursue environmentally friendly practices, all of which protect the environment more effectively. For example, effective predictive analytics can provide base realistic estimates and timeframes for projects to avoid costly delays and overruns. Machine learning ensures effective deployment of resources, including materials, labor, and equipment to limit waste. AI powered automation of mundane but repetitive tasks using robotics and drones takes the responsibility away from an operator, enhances safety by taking the operator out of a hazardous site, and increases the pace of construction project delivery systems. In addition, AI reinforces sustainable construction through enhancing energy efficiency, minimizing carbon footprints, and accelerating the adoption of green construction materials. Notwithstanding these breakthroughs, the research also discusses some of the underlying factors that impeded the widespread use of AI, such as high entry costs, technological barriers, and opposition from the sector. Furthermore, tackling the supply-side skill deficit among construction professionals through training and education is of great concern. It is these elements that will enable the construction industry to unleash the full advantages of AI, unleashing the unprecedented benefits of technology, indeed the phrase greatest benefits. Therefore, a focus should be placed on achieving a competitive non-obsolescent construction industry. In doing so, the industry will pave the way for the emergence of new competitive spaces: more efficient, sustainable, and firmer constructions.
With the rapid advancement of information technology and Artificial Intelligence (AI), numerous cutting-edge techniques and technologies have been developed to improve English learning generally and English for Specific Purposes (ESP) specifically. This study's combination of the use AI technology by ESL students bring a niche in the field. It also examines ESP students in a developing country overwelmed by war. Therefore, this article presents a quantitative study on Yemeni architectural engineering students to identify their perceptions of their proficiency in utilizing AI tools in their ESP studies. In addition, the study attempted to determine what concerns these students may have when using AI applications. To collect data, a close-ended questionnaire was administered to 43 fourth year students at the Department of Architectural Engineering at Hadhramout University, Yemen in the academic year 2024-2025. The findings indicated that the majority of the students are below the average efficiency (M=2.65) level in using most of the AI applications. The students, however, rated themselves as efficient in handling few AI-based applications such as ChatGPT (M= 4.44), Microsoft Bing Copilot (M= 3.88) and Google Gemeni 56 Al-Tamimi Atef1, Ben-Hadi Wagdi2: AI Technology in the ESP Context: Uses and Concerns among Architectural Engineering Students at Hadhramout University Academic Success This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International License. Available online at https://revue.univ-oran2.dz/Revue/IJMLSP/index.php/IJMLSP (M= 3.74). The study also showed that Yemeni EFL students have high concern regarding the use of AI technology (M=3.57). For instance, the current analysis emphasized several challenges and concerns involving ethical issues related to data privacy, plagiarism, overreliance, cheating and laziness among learners, and the tendency to provide misleading or inaccurate information. This study could contribute to raise architectural engineering students' awareness and knowledge about their lacks in using AI technologies. Knowing their strengths and weaknesses in using various AI tools would be beneficial so that future learning may be enriched if learners maintain their strengths and try to improve and stretch their weaknesses. Moreover, based on the findings, the study presented recommendations for the professors, curricula designers and engineering students at Hadhramout University.
George Boole was the first to describe a formal language for logic reasoning in 1847. The next milestone in artificial intelligence history was in 1936, when Alan M. Turing described the Turing-machine. Warren McCulloch and Walter Pitts created the model of artificial neurons in 1943, and it was in 1944 when J. Neumann and O. Morgenstern determined the theory of decision, which provided a complete and formal frame for specifying the preferences of agents. In 1949 Donald Hebb presented a value changing rule for the connections of the artificial neurons that provide the chance of learning, and Marvin Minsky and Dean Edmonds created the first neural computer in 1951. Artificial intelligence (AI) was born in the summer of 1956, when John McCarthy first defined the term. It was the first time the subject caught the attention of researchers, and it was discussed at a conference at Dartmouth. The next year, the first general problem solver was tested, and one year later, McCarty?regarded as the father of AI?announced the LISP language for creating AI software. Lisp, which stands for list processing, is still used regularly today. Herbert Simon in 1965 stated: “Machines will be capable, within twenty years, of doing any work a man can do.” However, years later scientists realized that creating an algorithm that can do anything a human can do is nearly impossible. Nowadays, AI has a new meaning: creating intelligent agents to help us do our work faster and easier (Russel & Norvig, 2005; McDaniel, 1994; Shirai & Tsujii, 1982; Mitchell, 1996; Schreiber, 1999). Perceptrons was a demonstration of the limits of simple neural networks published by Marvin Minsky and Seymour Papert in 1968. In 1970, the first International Joint Conference on Artificial Intelligence was held in Washington, DC. PROLOG, a new language for generating AI systems, was created by Alain Colmerauer in 1972. In 1983, Johnson Laird, Paul Rosenbloom, and Allen Newell completed CMU dissertations on SOAR.
This paper examines the transformative role of Artificial Intelligence (AI) in school leadership and management. It examines how AI can enhance decision-making, personalize learning experiences, improve digital pedagogy, and increase administrative efficiency. Additionally, the study discusses the challenges and ethical concerns associated with AI integration in educational leadership, such as bias and transparency. To address these issues, the review proposes strategies for implementing AI tools that align with educational goals while promoting equity and accountability. Furthermore, it reviews relevant literature on digital pedagogy, which extends traditional teaching methods into the digital space. A clear understanding of technological possibilities and their practical applications is essential for effectively integrating AI into education.
The digital single market is the site of major social and technological upheavals. Big data, artificial intelligence (AI), and platforms pose fundamental challenges for legislators and global businesses alike. This chapter provides an overview of the salient features, challenges, and research gaps in the analysis and regulation of the digital single market, setting the stage for the in-depth contributions in this volume.
Purpose of the work is to search for new ideas of metamodeling of physical and digital reality and the development of interdisciplinary means that increase creative and innovative potential. Methodology includes methods of interdisciplinary analysis and cognitive metamodeling of physical and digital reality. Results. New paradigms, hybrid universals and universities are proposed, as well as the method of formation of integral thinking. Their complementarity opens up new opportunities to increase the efficiency of visual communications dynamics. The connection of the generalized principle of N. Bohr's supplement with the tectonic ordering of fractal nature and the complementarity of the principles of bionics, heuristics and cognitive science, which opens fundamentally new opportunities for the use of different approaches in the design and subject environment of human life, has been revealed. The proposed metamodels of creative thinking, covering key types of design – informational, dynamic and visual. New opportunities for the development of the theory and practice of environment design and its subject filling on the basis of hybrid structures and metamodels are substantiated. Scientific novelty. The first developed meta-methodology of integration of creative personalities, which contributes to the development of creative potential, improvement of mental health and functional safety. Practical significance. An innovative methodology for identifying the peculiarities of knowing the harmony of fractal nature and its application in the process of development of meta-thinking as the ability to know, analyze and change your thinking have been developed. The integrative coefficient of cognitive distortions and integrative criteria for assessing the creative potential of the individual are introduced. An innovative methodology for identifying obvious and hidden cognitive distortions with the help of a AI-transformer is proposed. The complementarity of these results is the basis of a new idea of self-organization of creative personalities, which is aimed at integration of education in post-war Ukraine into a single European space of higher education and the multiplication of human and intellectual capital of Ukraine.
Η παρούσα διπλωματική εργασία επικεντρώνεται στην αξιολόγηση των μοντέλων Τεχνητής Νοημοσύνης (AI) που εφαρμόζονται στις συναλλαγές κρυπτονομισμάτων. Ο βασικός στόχος της έρευνας είναι να εκτιμήσει την απόδοση και την αποτελεσματικότητα αυτών των μοντέλων, συγκρίνοντάς τα με τις στρατηγικές που χρησιμοποιούν οι ανθρώπινοι επενδυτές. Η ανάλυση εστιάζει στα πλεονεκτήματα, τους περιορισμούς και τις πιθανές εφαρμογές των μοντέλων AI στο χώρο των επενδύσεων .
Η μεθοδολογία περιλαμβάνει τη συλλογή δεδομένων συναλλαγών κρυπτονομισμάτων και την εφαρμογή αλγορίθμων AI, όπως δίκτυα βαθιάς μάθησης (Deep Learning) και τεχνικές μηχανικής μάθησης (Machine Learning). Τα αποτελέσματα αυτών των μοντέλων συγκρίνονται με στρατηγικές ανθρώπινων επενδυτών μέσω προσομοιώσεων που αξιολογούν την ακρίβεια πρόβλεψης, την ταχύτητα εκτέλεσης και τη συνολική αποδοτικότητα .
Η ανάλυση αποκάλυψε ότι τα μοντέλα AI υπερέχουν σε όρους ταχύτητας και ικανότητας ανάλυσης μεγάλων όγκων δεδομένων σε πραγματικό χρόνο. Ωστόσο, παρατηρήθηκαν ορισμένοι περιορισμοί, όπως το πρόβλημα της υπερπροσαρμογής (overfitting) σε συγκεκριμένα σύνολα δεδομένων και η αδυναμία προσαρμογής σε απρόβλεπτες μεταβολές της αγοράς. Αντίθετα, οι ανθρώπινες στρατηγικές βασίζονται σε εμπειρική γνώση και στην κατανόηση μη ποσοτικών παραγόντων της αγοράς .
Συμπερασματικά, η εργασία προτείνει ότι ένας υβριδικός τρόπος συναλλαγών, που συνδυάζει την ταχύτητα και την ακρίβεια των μοντέλων AI με τη διαίσθηση και την εμπειρία των ανθρώπων, μπορεί να προσφέρει την πιο ολοκληρωμένη στρατηγική για τις συναλλαγές κρυπτονομισμάτων. Τα ευρήματα της έρευνας έχουν ιδιαίτερη σημασία για επενδυτές, εταιρείες χρηματοοικονομικής τεχνολογίας (FinTech) και ερευνητές που ενδιαφέρονται να βελτιστοποιήσουν τις επενδυτικές στρατηγικές .
Este artigo apresenta um estudo de caso onde é criado um jogo simples, onde os agentes têm personalidades baseadas no modelo de redes Bayesiana de emoções. Os resultados mostram que, dependendo as personalidades definidas, diferentes situações ocorrem, e com isso a jogabilidade é alterada.
Humanos necessitam de leis que regulem as suas ações. Da mesma forma, quando aplicamos normas em sociedades de agentes, percebemos que tende a aumentar o desempenho do sistema segundo critérios que o projetista quiser. A proposta é avaliar o efeito que um tipo de norma de proibição de ações pode ter no desempenho de agentes reativos simples e agentes baseados em modelos com regras condição-ação, em ambientes de tarefas parcialmente observáveis. Estes agentes têm como princípio inibir aquelas regras, em seus conjuntos de regras condição-ação, que contenham alguma ação proibida como consequente. Os agentes demonstraram capacidade de entender estas normas específicas e evitar a realização de ações proibidas.
Este artigo apresenta a proposta de integração de sinais fisiológicos em sistemas multiagentes, com o intuito de tornar as aplicações mais realistas e integrar o usuário ao sistema desenvolvido. Para tanto, um dispositivo de coleta de sinais fisiológicos foi desenvolvido em Arduino e integrado ao sistema multiagente via protocolo Javino.
Sistemas inteligentes são, em muitos casos, sistemas complexos, onde o cumprimento da tarefa pode demandar a ação integrada e coordenada de diversos agentes. Além disso, a complexidade das ações individuais pode demandar a utilização de diversas técnicas. Dentre a gama de problemas que apresentam alto grau de complexidade e cuja solução demanda diversos subsistemas destacam-se os jogos digitais. Este trabalho apresenta a implementação de uma arquitetura multinível para a composição de agentes cooperativos, que consiste em uma composição de técnicas de inteligência artificial. Para validar a proposta, os agentes competem em um jogo de captura à bandeira.
In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education. One such technology is ChatGPT, a powerful large language model developed by OpenAI. In this paper, the authors consider the possibilities and limitations of applying artificial intelligence, primarily ChatGPT, in the educational process. In addition to general definitions, this popular technology is briefly presented, as well as the possibilities of its application. Consideration is mainly carried out from the educational aspect, with the role of the teacher still in the centre. At the same time, appropriate didactic, pedagogical, and methodical possibilities were presented, with which the teacher can be innovative and even more successful, effectively applying ChatGPT in everyday educational work. Of course, the limitations arising from the application of artificial intelligence are also presented. In this article, the benefits and risks related to the use of ChatGPT in education are discussed. The article argues that ChatGPT has at least five main benefits, such as creating learning assessment, enhancing pedagogical practice, offering virtual personal tutoring, creating an essay or research article outline, and brainstorming ideas. However, there are risks related to academic integrity issues, unfair learning assessment, inaccurate information, and over-reliance on AI. The article offers a set of recommendations for the effective use of ChatGPT for educational purposes. As a final result, we get a more realistic picture of this current topic, which also shows the pedagogical aspects and does not diminish the role of the teacher.
Este ensaio aborda a necessidade de infraestruturas tecnológicas públicas na educação como parte da soberania digital de um país, dado o avanço de serviços baseados em inteligência artificial (IA) ofertados por corporações de tecnologia estadunidenses. Utilizando método bibliográfico, examina documentos sobre sistemas atuais de infraestrutura para a educação digital no contexto brasileiro, reivindicações de pesquisadores e ativistas, e a crescente dependência de empresas privadas por redes de ensino. Conclui-se que é urgente criar um projeto político de soberania digital para a educação que considere a participação social e os modelos federados para a colaboração entre instituições.
Purpose: to present an overview of research and technologies of artificial intelligence based on neural networks and large linguistic models. Methodology: the key method for the study is the desk method of thematic literature reviews. Findings: the main trends in the development of artificial intelligence were identified, the hidden dangers and the imperfections of modern systems implementation were analyzed and determined. Originality and contribution: the study shows a critical approach and the analysis of the ever-increasing information noise in the field of promoting generative artificial intelligence as the result of the author’s supervision and implementation of research projects initiated by the Russian Foundation for Basic Research and the Russian Science Foundation, as well as the continuous expert work in the Russian Association of Artificial Intelligence. The paper integrates several plenary reports of the author on this topic.
The paper suggests distinguishing between the methodologies of conceptual analysis and notional analysis. The differ-ence lies in how the notion and concept are interpreted. In the paper, a notion is viewed as an individual opinion or un-derstanding of something, whereas a concept represents a general notion or abstract idea. It is assumed that many simi-lar concepts exist with varying scopes and contents. A concept is treated as an objectified notion developed and solidi-fied through social experience. The primary representation of the results of established conceptual analysis methods is shown to be a semantic network, where the vertices are concepts and the arcs represent relationships between them. However, it is noted that classical set theory and semantic networks are insufficient to formalize notions and concepts; instead, the theory of structures must be employed. Conceptual analysis is described as a technique for synthesizing formal concept descriptions using Boolean and Cartesian construction operations. The challenges of applying concep-tual analysis within the framework of the theory of structures are outlined. The development of notional analysis is linked to the demand for precise, unambiguous, and consistent notion descriptions, driven by the variability of their properties. Notional analysis is defined as a method for formal normal descriptions using generalization and associa-tion operations. Generalization entails combining notions such that the entities of the generalized notions become enti-ties of the resulting notion. Association involves merging notions, with each entity of the resulting notion including one entity from each of the associated notions. Notional analysis addresses issues encountered when describing concepts using a corpus-based approach by generating diverse descriptions of notions. It is demonstrated that both conceptual and notional analyses are rooted in the theory of structures. The paper identifies areas where these analyses are effec-tively applied and provides an example of using notional analysis in the design of a technical device.
The post-COVID-19 era has marked a transformative shift in human resource management (HRM), driven by the rapid adoption of artificial intelligence (AI). This paper explores the pivotal role of AI in reshaping HRM through automation, data analytics, and digitalization. By streamlining repetitive tasks, enabling data-driven decision-making, and fostering seamless digital transformation, AI addresses the evolving needs of hybrid and remote workplaces. Drawing from contemporary research and case studies, this study examines AI's impact on recruitment, performance management, employee engagement, and learning systems. The findings reveal significant improvements in operational efficiency, employee satisfaction, and strategic alignment, while also highlighting challenges related to bias, privacy, and organizational adaptation. The paper concludes with recommendations for ethical and effective integration of AI in HRM practices.
La implementación de la Inteligencia Artificial (IA) en diferentes áreas laborales ha sido percibida como una situación de amenaza u oportunidad, ello particularmente con el personal administrativo de empresas de Lima. Al respecto, se plantea el objetivo de analizar la percepción sobre la aplicación de la IA, desde el personal administrativo de una empresa de Lima metropolitana. Para ello, se desarrolló una investigación cualitativa, de diseño de análisis temático reflexivo, se entrevistó a 10 trabajadores administrativos de una empresa industrial, a quienes se les aplicó una entrevista semiestructurada. Para el análisis de información se utilizó el software ATLAS.ti 23. A partir de lo anterior se identificaron tres temas de relevancia, las preocupaciones y desafíos tanto por el temor del reemplazo del factor humano y preocupación por el manejo de datos, en lo que corresponde al cuidado ético de los mismos; beneficios y oportunidades, tales como la eficiencia en la toma de decisiones, la automatización de procesos, la eficiencia en los procesos, y el tiempo disponible para otras tareas; y la seguridad laboral, que comprende una actitud positiva hacia la IA, así como lo indispensable del factor humano. Se puede concluir que los participantes expresan preocupación por el posible reemplazo laboral y la seguridad de datos con la IA. No obstante, ven la IA como una herramienta valiosa para colaborar, mejorar la eficiencia y aumentar la productividad mediante la automatización. Además, la IA puede optimizar el uso del tiempo y los recursos, generando nuevas oportunidades y facilitando el desarrollo profesional.
Este artigo pretende examinar os principais marcos históricos do desenvolvimento da Inteligência Artificial (IA), analisando como essas inovações impactam o campo educacional. Para isso, propomos a construção de uma linha do tempo ilustrativa que servirá como base para a análise sobre como cada um desses marcos impactou o campo educacional. A metodologia se fundamentou em um enfoque bibliográfico-documental, e é analisada pela abordagem historiográfica de Certeau (2015). Os resultados incluem marcos desde 1842 até 2024 e, em seguida, o estudo evidencia muito mais que um progresso tecnológico, pois está se formando uma construção social que está moldando o futuro da educação.
This research paper was titled: (Artificial Intelligence in Education), and through it, the researchers sought to explore the main role that artificial intelligence represents as a modern and advanced technical achievement in the fields of education and learning at its various stages. The paper was divided into six sections, the first of which represents the methodological framework of the research, the second on the concept of artificial intelligence in education and its importance, the third included applications of artificial intelligence in education, the fourth shed light on the use of artificial intelligence in education and the challenges, controls, effects and ethical concerns that accompany it, the fifth was devoted to future trends in artificial intelligence in education in the Kingdom, and the sixth contained the results, recommendations and references. The researchers concluded with a number of results, the most prominent of which are: the importance of combining artificial intelligence and teachers to achieve the best educational results, the positive impact that artificial intelligence-supported tools achieve in improving the quality of education, and the role of artificial intelligence in reducing routine burdens on teachers and enabling them to focus on aspects of creative teaching.
ResearchGate has not been able to resolve any references for this publication.