Digital Resurrection: Challenging the Boundary between Life and Death with Artificial Intelligence

The advancement of Artificial Intelligence (AI) poses challenges in the field of bioethics, especially concerning issues related to life and death. AI has permeated areas such as health and research, generating ethical dilemmas and questions about privacy, decision-making, and access to technology. Life and death have been recurring human concerns, particularly in connection with depression. AI has created systems like Thanabots or Deadbots, which digitally recreate deceased individuals and allow interactions with them. These systems rely on information generated by AI users during their lifetime, raising ethical and emotional questions about the authenticity and purpose of these recreations. AI acts as a mediator between life, death, and the human being, enabling a new form of communication with the deceased. However, this raises ethical issues such as informed consent from users and the limits of digital recreation. Companies offer services like the Digital Resurrection of deceased individuals and the generation of hyper-realistic avatars. Still, concerns arise about the authenticity of these representations and their long-term emotional impact. Interaction with Thanabots may alter perceptions of death and finitude, leading to a potential “postmortal society” where death is no longer viewed as a definitive end. Nevertheless, this raises questions about the value of life and the authenticity of human experiences. AI becomes a bridge between the living and the dead, partially replacing rituals and mystical beliefs. As technology advances, there will be a need for greater transparency in interacting with AI systems and ethical reflections on the role of these technologies in shaping perceptions of life and death. Ultimately, the question arises of whether we should allow the dead to rest in peace and how to balance the pursuit of emotional relief with authenticity and respect for the memory of the deceased. A deeper ethical consideration is needed on how AI alters traditional notions of life, death, and communication in contemporary society. In this research, an interdisciplinary approach was utilized to conduct a comprehensive systematic review of the recent academic literature, followed by a detailed analysis of two key texts. Central ideas were extracted, and recurring themes were identified. Finally, a reflective analysis of the findings was conducted, yielding significant conclusions and recommendations for future research

Perspectiva de la ética en la inteligencia artificial

ForthcomingArtificial intelligence (AI) is a technology that is increasingly present in the life of the human being. The number of unaccountable use cases of AI that have emerged recently demonstrate that AI technology must be an urgent issue on the agenda of the public, private and educational institutions. For this reality, various international organizations have sought to establish ethical guidelines for AI. In that journey, challenges emerge to be able to find agreements that suit developers and users of AI. As for it, corporate and governmental interests, secondary legislation, economics, cultural horizon, and adoption of various ethical positions, hinder a dialogue that reflects ethical principles of AI. In this sense, it is proposed the analysis of concepts that can help to dialogue on ethics of AI, through the study and classification of the main international postulates in the matter to generate adequate communication channels that between the community that discusses how to have a use responsible, institutional, and social of AI

Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System

Performing control is necessary for processes where a variable needs to be regulated. Even though conventional techniques are widely preferred for their implementation, they present limitations in systems in which the parameters vary over time, which is why methods that use artificial intelligence algorithms have been developed to improve the results given by the controller. This work focuses on implementing a position controller based on fuzzy logic in a real platform that consists of the base of a 3D printer, the direct current motor that modifies the position in this base, the power stage and the acquisition card. The contribution of this work is the use of genetic algorithms to optimize the values of the membership functions in the fuzzification of the input variables to the controller. Four scenarios were analyzed, in which the trajectory and the weight of the system were modified. The results obtained in the experimentation show that the rising and setting times of the proposed controller are better than those obtained by similar techniques that were previously developed in the literature. It was also verified that the proposed technique reached the desired values even when the initial conditions in the system changed

Analysis of Emergency Remote Education in COVID-19 Crisis Focused on the Perception of the Teachers

This descriptive study intends to identify the satisfaction perception among the teachers of the Universidad del Valle de México (UVM) concerning the use of the Microsoft Teams platform in the transition from traditional model (face-to-face) to 100% online education [Emergency Remote Teaching (ERT)]. The proposal aims to determine the perspectives of teachers regarding the use of the Microsoft Teams platform during the crisis caused by COVID-19. UVM has 6938 full-time teachers and part-time teachers who collaborated in educational programs during January-June 2020 in the 33 campuses of UVM. And an instrument was developed and applied using finite population sampling, UVM perspective of teachers, which was distributed via Google Forms. The feasibility of the data collection instrument was determined by the Cronbach’s Alpha coefficient, with a result of 0.926. The data collection period was aligned with the first isolation period: 23 March to 20 April. The results in the perception of teacher satisfaction in the different sections of the instrument established an agreement in the answers (very satisfied or satisfied) regarding values that were higher than 60% in terms of satisfaction using the equipment. The analysis of the data collected was performed to verify the proposed hypothesis with the R version 4.0 software. A G-test was performed with the Logverosimilitude coefficient to test whether the categorical variables were independent (qualitative variables that are not defined continuously). The Krammer coefficient of association was then calculated to measure the correlation

Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System

Performing control is necessary for processes where a variable needs to be regulated. Even though conventional techniques are widely preferred for their implementation, they present limitations in systems in which the parameters vary over time, which is why methods that use artificial intelligence algorithms have been developed to improve the results given by the controller. This work focuses on implementing a position controller based on fuzzy logic in a real platform that consists of the base of a 3D printer, the direct current motor that modifies the position in this base, the power stage and the acquisition card. The contribution of this work is the use of genetic algorithms to optimize the values of the membership functions in the fuzzification of the input variables to the controller. Four scenarios were analyzed, in which the trajectory and the weight of the system were modified. The results obtained in the experimentation show that the rising and setting times of the proposed controller are better than those obtained by similar techniques that were previously developed in the literature. It was also verified that the proposed technique reached the desired values even when the initial conditions in the system changed

Tendency on the Application of Drill-Down Analysis in Scientific Studies: A Systematic Review

With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: hk→H=SSE(C),wherek>0and∃X, executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems

Teachers’ Perception in Selecting Virtual Learning Platforms: A Case of Mexican Higher Education during the COVID-19 Crisis

In this project, an analysis is made of the perception of teachers of Higher Education Institutions (HEI) regarding the use of Virtual Learning Platforms (VLP) in the transition from the Traditional Educational Model (face-to-face) to Emergency Remote Teaching (ERT). A statistical analysis of teachers’ views on the academic environment caused by the COVID-19 crisis is carried out for the change of educational scenarios from school to home through VLP, the support for teaching–learning knowledge of VLPs and the received training, and the main problems during the transition period. Through convenience sampling, data was collected for a statistical study using a developed instrument (Data collection was designed through the Google Forms application and distributed to public and private HEI teachers). The results of the study showed that more than 60% of respondents had experience using Moodle, Google Classroom, and Blackboard; 80% of teachers had training from their institution for the use of virtual platforms; and in 60% of cases, higher education institutions allowed them to choose the VLP. In addition, the main issues they faced were connectivity, student attitude, and student attendance at class sessions. Fisher’s test was conducted to determine the relationship in the variables analyzed by identifying that there are differences of teachers in perception depending on age

Teachers’ Perception in Selecting Virtual Learning Platforms: A Case of Mexican Higher Education during the COVID-19 Crisis

In this project, an analysis is made of the perception of teachers of Higher Education Institutions (HEI) regarding the use of Virtual Learning Platforms (VLP) in the transition from the Traditional Educational Model (face-to-face) to Emergency Remote Teaching (ERT). A statistical analysis of teachers’ views on the academic environment caused by the COVID-19 crisis is carried out for the change of educational scenarios from school to home through VLP, the support for teaching–learning knowledge of VLPs and the received training, and the main problems during the transition period. Through convenience sampling, data was collected for a statistical study using a developed instrument (Data collection was designed through the Google Forms application and distributed to public and private HEI teachers). The results of the study showed that more than 60% of respondents had experience using Moodle, Google Classroom, and Blackboard; 80% of teachers had training from their institution for the use of virtual platforms; and in 60% of cases, higher education institutions allowed them to choose the VLP. In addition, the main issues they faced were connectivity, student attitude, and student attendance at class sessions. Fisher’s test was conducted to determine the relationship in the variables analyzed by identifying that there are differences of teachers in perception depending on age

A Hands-On Laboratory for Intelligent Control Courses

This research focused on developing a methodology that facilitates the learning of control engineering students, specifically developing skills to design a complete control loop using fuzzy logic. The plant for this control loop is a direct current motor, one of the most common actuators used by educational and professional engineers. The research was carried out on a platform developed by a group of students. Although the learning techniques for the design and implementation of controllers are extensive, there has been a delay in teaching techniques that are relatively new compared to conventional control techniques. Then, the hands-on laboratory offers a tool for students to acquire the necessary skills in driver tuning. In addition to the study of complete systems, the ability to work in a team is developed, a fundamental skill in the professional industrial area. A qualitative and quantitative analysis of student learning was carried out, integrating a multidisciplinary project based on modern tools