Abstract Introduction: Upper endoscopy is the standard method for diagnosing early-stage gastric cancer. However, according to estimates, up to 20% of tumors are not detected, and their accuracy may be affected by the variability in their performance. In Colombia, most diagnoses take place in advanced stages, which aggravates the problem. Protocols have been proposed to ensure the complete observation of areas prone to premalignant lesions to address variability. Objective: To build and validate an automatic audit system for endoscopies using artificial intelligence techniques. Methodology: In this study, 96 patients from a teaching hospital underwent video-documented endoscopies, spanning 22 stations rearranged to minimize overlaps and improve the identification of 13 key gastric regions. An advanced convolutional network was used to process the images, extracting visual characteristics, which facilitated the training of artificial intelligence in the classification of these areas. Results: the model, called Gastro UNAL, was trained and validated with images of 67 patients (70% of cases) and tested with 29 different patients (30% of cases), which reached an average sensitivity of 85,5% and a specificity of 98,8% in detecting the 13 gastric regions. Conclusions: The effectiveness of the model suggests its potential to ensure the quality and accuracy of endoscopies. This approach could confirm the regions evaluated, alerting less experienced or trained endoscopists about blind spots in the examinations, thus, increasing the quality of these procedures.

Resumen Introducción: La endoscopia digestiva alta es el método estándar para diagnosticar el cáncer gástrico en etapas tempranas. Sin embargo, su precisión puede verse afectada por la variabilidad en su realización, y se estiman hasta 20% de tumores no detectados. En Colombia, la mayoría de los diagnósticos se realizan en etapas avanzadas, lo que agrava el problema. Para abordar la variabilidad, se han propuesto protocolos con el fin de asegurar la observación completa de áreas propensas a lesiones premalignas. Objetivo: Construir y validar un sistema de auditoría automática para endoscopias usando técnicas de inteligencia artificial. Metodología: En este estudio, 96 pacientes de un hospital universitario se sometieron a endoscopias documentadas en video, abarcando 22 estaciones reorganizadas para minimizar solapamientos y mejorar la identificación de 13 regiones gástricas clave. Se utilizó una red convolucional avanzada para procesar las imágenes, extrayendo características visuales, lo que facilitó el entrenamiento de la inteligencia artificial en la clasificación de estas áreas. Resultados: El modelo, llamado Gastro UNAL, fue entrenado y validado con imágenes de 67 pacientes (70% de los casos) y probado con 29 pacientes distintos (30% de los casos), con lo que alcanzó una sensibilidad promedio del 85,5% y una especificidad del 98,8% en la detección de las 13 regiones gástricas. Conclusiones: La eficacia del modelo sugiere su potencial para asegurar la calidad y precisión de las endoscopias. Este enfoque podría confirmar las regiones evaluadas, alertando puntos ciegos en la exploración a los endoscopistas con menos experiencia o en entrenamiento, de tal forma que se aumente la calidad de estos procedimientos.

Abstract This paper presents the strategies and initial implementations of a data culture conversion process at a company in the electricity sector. Solutions are being developed within the scope of the construction management macro-process, which involves multiple sectors of the company, as well as external agents. The strategy is based on implementing Proofs of Concept as tools for converting the data culture within the company and, at the same time, supporting the essential activities of construction management. The proofs of concept aligned with the change in data culture have two components: technology and people. The first consists mainly of intelligent automation and process optimization. The second component involves sensitization, training and developing solutions together with the company's employees. The results involve a reduction in workload since some activities have been partially or totally transferred to the software. Greater assertiveness in estimating numerical data, such as prices and budgets. Increased reliability by reducing human intervention and complex calculations. Greater controllability by monitoring multiple activities and sectors, simplifying management. Finally, the integration of technology and people has shown significant advances in organizational culture, enabling employees to have a greater understanding of advanced tools and methods and to be able to identify opportunities and share solutions within the company. sector macroprocess, macroprocess macro process, macro-process agents time components optimization sensitization companys s software budgets calculations Finally

Abstract Technological advancement has had a significant impact on written production, especially in Additional Languages (ALs). Although technology has brought new opportunities for AL teaching, it also poses challenges, including concerns about the complexity of writing and the authenticity of students’ work. One such tool is ChatGPT, an artificial intelligence (AI) platform that has been the subject of debate since its popularization in 2022. This study analyses a corpus consisting of six tasks produced by ChatGPT in five languages (German, Spanish, French, Italian, and Portuguese), considering the proficiency levels proposed by the Common European Framework of Reference for Languages (CEFR), totalling 2991 texts and 706,401 words. The data were generated by students in a computer lab at a British university from 100 different profiles on the ChatGPT platform, following the researchers’ instructions. Data analysis employs Systemic Functional Linguistics (SFL) and the concept of lexical density ( Halliday, 1985, 1987, 1993; Halliday; Matthiessen, 2014) to investigate the complexity of the produced texts, as lexical complexity is related to proficiency in writing, where more advanced texts proportionally use more “content words” (nouns, verbs, adjectives, and some adverbs of manner). The results reveal that ChatGPT does not adhere to task instructions regarding the requested word count, thereby impacting the calculation of lexical density, nor does it produce texts that show significant differences in lexical density among additional languages and proficiency levels. production ALs. ALs . (ALs) teaching challenges work AI (AI 2022 German, German (German Spanish French Italian Portuguese, Portuguese , Portuguese) CEFR, CEFR (CEFR) 299 706401 706 401 706,40 words 10 researchers SFL (SFL Halliday 1985 1987 1993 Matthiessen 2014 content nouns, nouns (nouns verbs adjectives manner. manner manner) count (ALs 202 (CEFR 29 70640 70 40 706,4 1 198 199 201 20 2 7064 7 4 706, 19

Resumo O avanço tecnológico tem tido um grande impacto na produção escrita, especialmente em Línguas Adicionais (LAs). Embora a tecnologia tenha trazido novas oportunidades para o ensino de LAs, ela também apresenta desafios, incluindo preocupações sobre a complexidade da escrita e a autenticidade dos trabalhos dos alunos. Uma dessas ferramentas é o ChatGPT, plataforma de inteligência artificial (IA) que tem sido objeto de debates desde sua popularização em 2022. Este estudo analisa um corpus composto por seis tarefas produzidas pelo ChatGPT em cinco idiomas (alemão, espanhol, francês, italiano e português), considerando os níveis de proficiência propostos pelo Quadro Comum Europeu de Referência para Línguas (CEFR), que totalizou 2991 textos e 706,401 palavras. Os dados foram gerados por alunos em um laboratório de informática em uma universidade britânica a partir de 100 diferentes perfis na plataforma do ChatGPT, seguindo instruções dos pesquisadores. A análise dos dados utiliza a linguística sistêmico-funcional (LSF) e o conceito de densidade lexical ( Halliday, 1985, 1987, 1993; Halliday; Matthiessen, 2014) para investigar a complexidade dos textos produzidos, dado que a complexidade lexical está relacionada à proficiência na escrita, na qual textos mais avançados usam proporcionalmente mais “palavras de conteúdo” (nomes, verbos, adjetivos e alguns advérbios de modo). Os resultados revelam que o ChatGPT não segue as instruções das tarefas quanto ao número de palavras solicitadas, impactando, assim, no cálculo da densidade lexical, nem produz textos que mostram diferenças significativas da densidade lexical entre as línguas adicionais e níveis de proficiência. LAs. LAs . (LAs) desafios IA (IA 2022 alemão, alemão (alemão espanhol francês português, português , português) CEFR, CEFR (CEFR) 299 706401 706 401 706,40 10 pesquisadores sistêmicofuncional sistêmico funcional LSF (LSF Halliday 1985 1987 1993 Matthiessen 2014 produzidos conteúdo nomes, nomes (nomes verbos modo. modo modo) solicitadas impactando assim (LAs 202 (CEFR 29 70640 70 40 706,4 1 198 199 201 20 2 7064 7 4 706, 19

ABSTRACT Education 4.0 is an innovative approach that seeks to leverage advanced technology, particularly artificial intelligence (AI), to enhance learning and the student experience in higher education. AI can automate tasks, personalize the learning experience, and improve efficiency in scientific research. However, its implementation poses challenges in terms of adopting new educational models, the necessary investment for implementation, and user training. In order to provide detailed information on the advantages of AI application in teaching-learning processes and scientific research in higher education, a systematic literature review was conducted that explored the fundamentals, benefits, and drawbacks of AI, which was shared with professors and students from the Universidad de Oriente, Cuba, to learn about their perceptions on the topic. The results indicate that AI can enhance the teaching-learning process and improve efficiency in scientific research, but it requires careful, ethical and responsible implementation. Furthermore, it is concluded that AI can facilitate adaptation to each student's pace and learning style, optimize time usage and efficiency in research tasks, and foster the development of professional competencies for the job market. However, it is essential to ensure equity, quality, and ethics in its use.

RESUMEN La Educación 4.0 es un enfoque innovador que busca aprovechar la tecnología avanzada, en particular la inteligencia artificial (IA), para mejorar el aprendizaje y la experiencia de los estudiantes en la educación superior. La IA puede automatizar tareas, personalizar la experiencia de aprendizaje y mejorar la eficiencia en la investigación científica. No obstante, su aplicación plantea desafíos en cuanto a la adopción de nuevos modelos educativos, la inversión necesaria para su implementación y la capacitación de los usuarios. Con el objetivo de proporcionar información detallada a estudiantes y profesores sobre las ventajas de la aplicación de la IA en los procesos de enseñanza-aprendizaje y de investigación científica en la educación superior, se realizó una revisión sistemática de la literatura que exploró los fundamentos, beneficios y desventajas de la IA, la que fue socializada con profesores y estudiantes de la Universidad de Oriente, Cuba, para conocer sus percepciones sobre el tema. Los resultados indican que la IA puede potenciar el proceso de enseñanza-aprendizaje y mejorar la eficiencia en la investigación científica, pero requiere de una implementación cuidadosa, ética y responsable. Además, se concluye que la IA puede facilitar la adaptación al ritmo y estilo de aprendizaje de cada estudiante, optimizar el uso del tiempo y la eficiencia en tareas de investigación, y favorecer el desarrollo de competencias profesionales para el mundo laboral. No obstante, es esencial garantizar la equidad, calidad y ética en su uso.

Abstract Background and Aims: Gastrointestinal (GI) endoscopy has known a great evolution in the last decades. Imaging techniques evolved from imaging with only standard white light endoscopes toward high-definition resolution endoscopes and the use of multiple color enhancement techniques, over to automated endoscopic assessment systems based on artificial intelligence. This narrative literature review aimed to provide a detailed overview on the latest evolutions within the field of advanced GI endoscopy, mainly focusing on the screening, diagnosis, and surveillance of common upper and lower GI pathology. Methods: This review comprises only lit-erature about screening, diagnosis, and surveillance strategies using advanced endoscopic imaging techniques published in (inter)national peer-reviewed journals and written in English. Studies with only adult patients included were selected. A search was performed using MESH terms: dye-based chromo-endoscopy, virtual chromoendoscopy, video enhancement technique, upper GI tract, lower GI tract, Barrett’s esophagus, esophageal squamous cell carcinoma, gastric cancer, colorectal polyps, inflammatory bowel disease, artificial intelligence. This review does not elaborate on the therapeutic application or impact of advanced GI endoscopy. Conclusions: Focusing on current and future applications and evolutions in the field of both upper and lower GI advanced endoscopy, this overview is a practical but detailed projection of the latest developments. Within this review, an active leap toward artificial intelligence and its recent developments in GI endoscopy was made. Additionally, the literature is weighted against the current international guidelines and assessed for its potential positive future impact.

Resumo Introdução/objetivos: A endoscopia digestiva conheceu uma grande evolução nas últimas décadas, tendo as técnicas de imagem evoluído de imagens com luz branca para endoscópios de alta definição com possibilidade de uso de várias técnicas de melhoramento de cores e até sistemas automatizados apoiados em inteligência artificial. Esta revisão narrativa da literatura visa fornecer uma visão detalhada das últimas evoluções no campo da endoscopia avançada, focando principalmente no rastreio, diagnóstico e vigilância. Métodos: Pesquisa da literatura sobre estratégias de rastreio, diagnóstico e vigilância utilizando técnicas avançadas de imagem endoscópica publicadas em revistas internacionais revistas por pares e escritas em inglês. Foram selecionados estudos apenas com doentes adultos e foi realizada pesquisa utilizando termos MESH: cromoendoscopia com corante, cromoendoscopia virtual, técnicas de melhoramento de vídeo, tubo digestivo superior, tubo digestivo inferior, esófago de Barrett, carcinoma de células escamosas, cancro gástrico, pólipos colorretais, doença inflamatória intestinal e inteligência artificial. Conclusões: Esta revisão avaliou de uma forma prática os últimos desenvolvimentos no cam-po da imagem avançada em endoscopia digestiva, avaliando-se também as perspetivas futuras e o potencial impacto da inteligência artificial.

ABSTRACT Currently the “pandemic” of diabetes mellitus is noted. The incidence and prevalence of diabetes and diabetic retinopathy, the most common microvascular complications of diabetes, are exponentially growing due to increased life expectancy in many parts of the world. The increasing number of people suffering from diabetic retinopathy not only highlights medical issues, but also an economic burden, representing a medical and social challenge. It is extremely important to identify a disease as soon as possible and successfully treat it. Technological progress results in developing Artificial Intelligence systems capable of detecting diabetic retinopathy. Current screening will be cost effectively based on the use of advanced digital technologies, in particular teleretinal screening systems. At present, we may consider teleophthalmology and Artificial Intelligence with automatic analysis of fundus photos as a Millennium-minded impactful tool for increasing discoverability and manageability of diabetic retinopathy, especially in filling the gap of inaccessibility to hard-to-reach areas, which enforces highly professionally effective time- and cost-saving care everywhere to provide the best possible care for the patients. pandemic “pandemic noted world issues burden challenge it technologies present Millenniumminded Millennium minded hardtoreach hard reach areas time costsaving saving patients

RESUMO Atualmente, observa-se a “pandemia” do diabetes mellitus. A incidência e a prevalência do diabetes e da retinopatia diabética, as complicações microvasculares mais comuns do diabetes, estão crescendo exponencialmente devido ao aumento da expectativa de vida em muitas partes do mundo. O número cada vez maior de pessoas que sofrem de retinopatia diabética não apenas destaca problemas médicos, mas também um ônus econômico, representando um desafio médico e social. É extremamente importante identificar uma doença o mais rápido possível e tratá-la com sucesso. O progresso tecnológico resulta no desenvolvimento de sistemas de Inteligência Artificial capazes de detectar a retinopatia diabética. A triagem atual será econômica com base no uso de tecnologias digitais avançadas, em especial os sistemas de triagem telerretiniana. No momento, podemos considerar a teleoftalmologia e a Inteligência Artificial com análise automática de fotos de fundo de olho como uma ferramenta de impacto do milênio para aumentar a capacidade de descoberta e de manejo da retinopatia diabética, especialmente para preencher a lacuna da inacessibilidade a áreas de difícil acesso, o que impõe um atendimento altamente profissional e eficaz, com economia de tempo e de custos, em todos os lugares, para oferecer o melhor atendimento possível aos pacientes. Atualmente observase observa se pandemia “pandemia mellitus mundo médicos econômico social tratála tratá la sucesso avançadas telerretiniana momento acesso eficaz custos lugares pacientes

Abstract Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations. PLF. . (PLF) monitored cuttingedge cutting edge realtime time (AI intelligence manner (ML (DL layers, layers , layers) respectively livestockrelated activities behavior health environment decisionmaking. decisionmaking making. making decision-making processing acquisition transferring storage community circumstances context MLDL mentioned situations (PLF

In an era where the prevalence of data is striking, the importance of adopting advanced computational techniques, such as artificial intelligence and machine learning, becomes fundamental in the training process of tomorrow’s physicists. This article introduces an innovative approach to physics education, incorporating machine learning into the classic study of the simple pendulum. Key concepts of machine learning techniques are introduced and a broad dataset is collected through accurate observation of the pendulum’s motion under different initial conditions. From these data, a regression algorithm is employed to create a model of the pendulum system with satisfactory statistical indices. This pedagogical model contrasts with traditional physics teaching methods and opens a new horizon for understandding what it means to teach physics in the information age. striking tomorrows tomorrow s physicists education pendulums conditions indices age

Em uma era onde a prevalência de dados se faz marcante, a importância da adoção de técnicas computacionais avançadas, como a inteligência artificial e o aprendizado de máquina, torna-se fundamental no processo de formação dos físicos de amanhã. Este artigo introduz uma abordagem inovadora para o ensino de física, incorporando o aprendizado de máquina ao estudo clássico do pêndulo simples. São introduzidos os conceitos das técnicas de aprendizado de máquina e coletado um conjunto amplo de dados por meio da observação acurada do movimento do pêndulo em diferentes condições iniciais. A partir desses dados, é empregado um algoritmo de regressão para criar um modelo do sistema do pêndulo com índices estatísticos satisfatórios. Este modelo pedagógico se contrasta com os métodos tradicionais de ensino de física e abre um novo horizonte para a compreensão do que significa o ensino de física na era da informação. marcante avançadas tornase torna amanhã simples iniciais satisfatórios informação

RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes’ training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes’ explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes’ training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

ABSTRACT The Pan American Health Organization/World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools—pairing AI with human judgment—to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search queries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO’s knowledge management team, and the Librarian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches.

RESUMEN El centro virtual contra la infodemia para la Región de las Américas de la Organización Panamericana de la Salud/Organización Mundial de la Salud (OPS/OMS) es un proyecto liderado por el Departamento de Evidencia e Inteligencia para la Acción en la Salud de la OPS y el Center for Health Informatics de la Universidad de Illinois, centro colaborador de la OPS/OMS en sistemas de información para la salud, con la participación de personal y consultores de la OPS en toda la Región. Su objetivo es crear un conjunto de herramientas que combinen inteligencia artificial (IA) y los criterios humanos para apoyar a los ministerios de salud y las instituciones relacionadas con la salud en la respuesta a la infodemia. Los funcionarios de salud pública recibirán formación sobre las amenazas emergentes detectadas por el centro y recomendaciones sobre cómo abordarlas. El centro virtual está estructurado en tres equipos paralelos: detección, evidencia y respuesta. El equipo de detección empleará una combinación de consultas mediante búsqueda avanzada, aprendizaje automático y otras técnicas de IA para evaluar más de 800 millones de publicaciones nuevas en las redes sociales al día con el fin de detectar amenazas emergentes en el ámbito de la infodemia tanto en inglés como en español. El equipo de evidencia hará uso del motor de búsqueda federado EasySearch y, con el apoyo de la IA, el equipo de gestión del conocimiento de la OPS y la red Librarian Reserve Corps, determinará cuáles son las fuentes autorizadas más pertinentes. El equipo de respuesta utilizará un enfoque vinculado al diseño para difundir las estrategias recomendadas sobre la base de las ciencias del comportamiento, la narración de historias y el diseño de la información.

RESUMO O Centro Virtual Anti-Infodemia para as Américas (AIVCA, na sigla em inglês) da Organização Pan-Americana da Saúde/Organização Mundial da Saúde (OPAS/OMS) é um projeto liderado pelo Departamento de Evidência e Inteligência para a Ação em Saúde da OPAS e pelo Centro de Informática em Saúde da Universidade de Illinois, EUA (Centro Colaborador da OPAS/OMS para Sistemas de Informação para a Saúde), com a participação de funcionários e consultores da OPAS de toda a região. Seu objetivo é desenvolver um conjunto de ferramentas — combinando a inteligência artificial (IA) com o discernimento humano — para ajudar os ministérios e instituições de saúde a responder às infodemias. As autoridades de saúde pública aprenderão sobre as ameaças emergentes detectadas pelo centro e obterão recomendações sobre como responder. O centro virtual está estruturado com três equipes paralelas: detecção, evidência e resposta. A equipe de detecção utilizará consultas de pesquisa avançada, machine learning (aprendizagem de máquina) e outras técnicas de IA para filtrar mais de 800 milhões de novas postagens públicas nas redes sociais por dia, a fim de identificar ameaças infodêmicas emergentes em inglês e espanhol. A equipe de evidência usará o mecanismo de busca federada EasySearch, com apoio de IA, da equipe de gestão de conhecimento da OPAS e do Librarian Reserve Corps (LRC), para identificar as fontes abalizadas mais relevantes. A equipe de resposta usará uma abordagem de design para comunicar estratégias de resposta recomendadas com base em abordagens de ciência comportamental, narração de histórias e design da informação.

Der Beitrag beschäftigt sich kritisch mit dem Wortschatzerwerb beim akademischen Schreibprozess im fortgeschrittenen DaF-Bereich in Zusammenspiel mit der Entwicklung einer angebrachten Sprachbewusstheit und einer aktuellen und umfassenden digitalen Kompetenz. Ausgangspunkt ist die Beobachtung, dass im akademischen L2-Schreibprozess das umfangreiche Informationspotenzial sowohl der ein-, zwei- und mehrsprachigen lexikografischen und lexiko-grammatischen Online-Ressourcen sowie anderer sprachtechnologischer Werkzeuge nur ungenügend bekannt ist. Dementsprechend werden die vielzähligen digitalen Sprachressourcen nicht optimal für Methoden der Vermittlung von Wortschatz und der Handlung mit ihm, z.B. beim akademischen Schreiben herangezogen. Andererseits nehmen die digitalen Medien immer mehr Raum in unserem Alltag ein und stellen auch sprachtechnologisch hoch entwickelte Werkzeuge für verschiedene L2-Kom-petenzbereiche, wie z.B. das Übersetzen bereit. Ziel des Beitrages ist es daher, sowohl Wege für die sinnvolle Nutzung der vorhandenen Internetressourcen aufzuzeigen als auch ganz neue auf künstlicher Intelligenz beruhende Wege zu skizzieren. An ausgewählten Ressourcen soll exemplarisch das umfangreiche Informationspotenzial aufgezeigt sowie Möglichkeiten der Nutzung beim akademischen Schreiben diskutiert werden. Ein Ausblick verweist auf die Notwendigkeit, sich auf revolutionäre technologische Veränderungen im Bereich der Fremdsprachdidaktik einzustellen.

The paper deals with vocabulary acquisition in the academic writing process in advanced GfL in interaction with the development of appropriate language awareness and wide-ranging up-to-date digital literacy. The starting point is the observation that in the academic L2 writing process the extensive information potential of monolingual, bilingual and multilingual lexicographic and lexico-grammatical online resources as well as other language technology tools is not adequately known. Accordingly, the many digital language resources are not optimally used for methods of teaching vocabulary and acting with it, e.g. in academic writing. On the other hand, digital media occupy more and more space in our everyday life and also provide highly developed tools based on linguistic technology for various L2 competence areas, such as translation. The aim of this paper is therefore both to show ways of making sensible use of existing internet resources and to outline entirely new ways based on artificial intelligence. Selected resources will be used as examples to demonstrate the extensive information potential and to discuss possibilities for use in academic writing. An outlook points to the necessity of adapting to revolutionary technological changes in the field of foreign language didactics.

Science-related subjects including chemistry, physics, mathematics, engineering, and medicine have all benefited greatly from advanced technology, particularly Artificial Intelligence (AI). Recently, the significance of AI in promoting chemistry research to improve the chemical industry has evolved. The goal of the researchers is to advance drug development by creating novel medications at a minimal cost. AI assists in achieving this goal. Other areas of chemistry that will benefit from AI include compound solubility, optimizing reaction conditions, and providing production methods for challenging target molecules. Researchers from Massachusetts Institute of Technology (MIT) have discovered a potent new antibiotic molecule using a machine-learning system. Typically, AI may produce ten times more antibody sequence clusters than a lab-based approach alone. Systems containing hundreds of interacting ions and electrons can now be modeled using an approximation of the physical laws. This is because modern algorithms and supercomputers make these applications possible.

Mineral Resources estimation plays a crucial role in the profitability of the future of mining operations. The conventional geostatistical methods used for grade estimation require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering as well as clean validated data to build accurate block models. However, the geostatistical models are sensitive to changes in data and would have to be rebuilt on newly acquired data with different characteristics, which has proved to be a time-consuming process. Machine learning methods have in recent years been proposed as an alternative to the geostatistical methods to alleviate the problems these might suffer from in Mineral Resource estimation. In this paper, a systematic literature review of machine learning methods used in Mineral Resource estimation is presented. This has been conducted on such studies published during the period 1990 to 2019. The types, performances, and capabilities, of several machine learning methods have been evaluated and compared against each other, and against the conventional geostatistical methods. The results, based on 31 research studies, show that the machine learning-based methods have outperformed the conventional grade estimation modelling methods. The review also shows there is active research on applying machine learning to grade estimation from exploration through to exploitation. Further improvements can be expected if advanced machine learning techniques are to be used.

Abstract When AI or robotics assist a professional, they are tools. In medicine, the doctrine of “competent human intervention” has shifted liability away from those who make devices and toward the professionals who use them. However, the professional in such scenarios should not bear the entire burden of responsibility. Tools can be defective, and vendors of defective, complementary AI and robotics should be held responsible for negligence. The burden of proof will still be on the plaintiff to demonstrate that not only a skilled medical professional, but also the maker of the tools used by such a professional, should be held liable for a preventable adverse outcome. When AI and robotics replace, rather than merely assist, a skilled medical professional, the burden should shift. The vendor of such computational systems needs to take on responsibility for errors and accidents. In the medical field, there has long been a standard of competent professional supervision of the deployment of advanced technology. When substitutive automation short-circuits that review, it is both defective and unreasonably dangerous. Nevertheless, at the damages phase of litigation, the vendor of the substitutive AI should be entitled to explain how damages should be mitigated based on its AI’s performance relative to the extant human- or human-machine based standard of care. Such responsibility for explanation will serve an important information-forcing function in areas where public understanding is often limited by trade secrecy. As law and political economy methods demonstrate, law cannot be neutral with respect to markets for new technology. It constructs these markets, making certain futures more or less likely. Distinguishing between technology that substitutes for human expertise and that which complements professionals is fundamental not just to labor policy and the political economy of automation, but also to tort law.

Resumen Cuando la IA o la robótica ayudan a un profesional, son herramientas. En medicina, la doctrina de la “intervención humana competente” ha desplazado la responsabilidad de quienes fabrican dispositivos hacia los profesionales que los utilizan. Sin embargo, el profesional en tales escenarios no debe cargar con todo el peso de la responsabilidad. Las herramientas pueden ser defectuosas, y los proveedores de IA y robótica complementaria defectuosa deben ser considerados responsables por negligencia. La carga de la prueba aún recaerá en el demandante para demostrar que no solo el profesional médico capacitado, sino también el fabricante de las herramientas utilizadas por dicho profesional, debe ser considerado responsable de un resultado adverso prevenible. Cuando la IA y la robótica reemplacen, en lugar de simplemente ayudar, a un profesional médico calificado, la carga debería cambiar. El proveedor de dichos sistemas computacionales debe asumir la responsabilidad por errores y accidentes. En el campo de la medicina, ha existido durante mucho tiempo un estándar de supervisión profesional competente del despliegue de tecnología avanzada. Cuando la automatización sustitutiva cortocircuita esa revisión, es defectuosa e irrazonablemente peligrosa. Sin embargo, en la fase de daños del litigio, el proveedor de la IA sustitutiva debe tener derecho a explicar cómo se deben mitigar los daños en función del desempeño de su IA en relación con el estándar de atención existente basado en humanos o máquinas humanas. Tal responsabilidad de explicación cumplirá una importante función de obtención de información en áreas donde la comprensión del público a menudo se ve limitada por el secreto comercial. Como demuestran los métodos del derecho y la economía política, el derecho no puede ser neutral con respecto a los mercados de nuevas tecnologías. Construye estos mercados, haciendo ciertos futuros más o menos probables. Distinguir entre la tecnología que sustituye a la experiencia humana y la que complementa a los profesionales es fundamental no solo para la política laboral y la economía política de la automatización, sino también para la responsabilidad civil extracontractual.

SUMMARY Epilepsy is a condition that frequently coexists with significant morbi-mortality levels, seriously affects the quality of life and, in up to one third of patients, is refractory to a variety of treatment approaches. Artificial intelligence (AI) has largely benefitted the study, treatment, and prognosis of patients with epilepsy through the course of recent years. These achievements applied the fields of diagnosis, automated seizure prediction, advanced seizure monitoring and electroencephalogram, use of genetics in diagnosis and management, imaging algorithms in the treatment, neuromodulation, and robotic surgery. This review conveys the actual and future directions of AI. a branch of science that has shown promising results in the treatment and diagnosis of patients with epilepsy.

RESUMEN La epilepsia es una enfermedad que frecuentemente conlleva significativos niveles de morbi-mortalidad, afecta seriamente la calidad de vida y, en cerca de un tercio de los pacientes, es refractaria a diversos tratamientos. La inteligencia artificial (IA) ha beneficiado el estudio, tratamiento y pronóstico de los pacientes con epilepsia a través de los años. Estos logros abarcan diagnóstico, predicción de crisis automatizada, monitoreo avanzado de crisis epilépticas y electroencefalograma, uso de recursos genéticos en manejo y diagnóstico, algoritmos en imagen y tratamiento, neuromodulación y cirugía robótica. La presente revisión explica de forma práctica los avances actuales y futuros de la inteligencia artificial, rama de la ciencia que ha mostrado resultados prometedores en el diagnóstico y tratamiento de pacientes con epilepsia.