Emergence Of Intelligence Research Articles

Nature as Event: A Study on John Dewey’s Naturalism

John Dewey’s naturalism requires viewing nature and experience from the perspective of holism, emphasising the continuity between these two. To Dewey, nature is not a fixed entity, but an event in an ongoing process of unfolding. The temporality of an event can meet Dewey’s requirement of constructing a philosophical notion about change and development. The event has a relatively stable structure. The continuity between living things and non-living things becomes possible because of the characteristic transactions of events, and experience thus becomes something emergent in nature and actively intervenes in its unfolding. The emergence of human intelligence and the application of language has lifted nature to a controllable and operable plane, making experience a crucial guide for the unfolding of nature.

AI-Enhanced Lung Cancer Prediction: A Hybrid Model's Precision Triumph.

Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine.

Chatbots in Cancer Applications, Advantages and Disadvantages: All that Glitters Is Not Gold.

The emergence of digitalization and artificial intelligence has had a profound impact on society, especially in the field of medicine. Digital health is now a reality, with an increasing number of people using chatbots for prognostic or diagnostic purposes, therapeutic planning, and monitoring, as well as for nutritional and mental health support. Initially designed for various purposes, chatbots have demonstrated significant advantages in the medical field, as indicated by multiple sources. However, there are conflicting views in the current literature, with some sources highlighting their drawbacks and limitations, particularly in their use in oncology. This state-of-the-art review article seeks to present both the benefits and the drawbacks of chatbots in the context of medicine and cancer, while also addressing the challenges in their implementation, offering expert insights on the subject.

How Perception, Actuation, and Communication Impact the Emergence of Collective Intelligence in Simulated Modular Robots.

Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions; nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task.

Exploring Technological Innovation as a Determinant of Entrepreneurial Activity and Performance

This systematic literature review delves into the intricacies of technological innovation and entrepreneurship and the management and strategies of new product development from an interdisciplinary perspective. After a rigorous selection process, 323 pivotal articles from 11 esteemed journals published between 2020 and 2023 underwent analysis, highlighting the contemporary research trajectories in these domains. This study highlights the emergence of artificial intelligence generated content technology since 2022 and its implications for innovation and product development among nascent entrepreneurs and established corporations. The insights garnered from this investigation enrich the existing scholarly discourse and offer relevant recommendations for subsequent inquiries in the dynamic realm of technological innovation and entrepreneurship.

ЖАСАЛМА ИНТЕЛЛЕКТ БИЛИМ БЕРҮҮДӨ

The article discusses the use of artificial intelligence in education and its progressiveness, as well as problems in the use of artificial intelligence at present time. Contains answers to the question: “Is it necessary to use artificial intelligence in education?” It also contains information about the emergence and development of artificial intelligence. The opinion about artificial intelligence in education, namely in higher educational institutions as an academic discipline, was considered. The place of artificial intelligence in education, the negative sides and the positive sides were considered. The article also contains a mathematical model or the mathematical basis of artificial intelligence. Information about the positive and negative effects of artificial intelligence or Chatgpt on people is included. Included is detailed information about ChatGPT, one of the artificial intelligence neural network, as well as information on how to use ChatGPT.

ChatGPT Goes to College: Exploring Student Perspectives on Artificial Intelligence in the Classroom

Background The emergence of artificial intelligence (AI) in higher education has sparked numerous discussions about its implications. ChatGPT, a prominent AI conversational model, has attracted significant attention for its ability to generate essays and formulate responses. Objective The current study sought to explore how and why students are using ChatGPT, and to examine their perceptions about ChatGPT and academic integrity. Method Students were surveyed about the frequency and motivation for ChatGPT use and their views on ChatGPT and academic misconduct. Exploratory factor analyses were conducted to examine patterns of correlations between each of the measures. Results Students primarily use ChatGPT for gathering information, motivated by its value and convenience rather than hedonic reasons, and can correctly identify academically unethical uses of the tool as cheating. Conclusion The current study presents comprehensive data on college students’ ChatGPT usage patterns, attitudes, and perceptions of cheating behavior. The outcomes of this research provide insight into how college students are currently interacting with AI tools. Teaching Implications Our findings offer practical insights for universities developing AI policies in the classroom, contributing to the ongoing discourse on AI’s role in higher education by providing accurate information about ChatGPT’s pervasiveness in academia.

Digital Sustainable Growth Model (DSGM): Achieving Synergy Between Economy and Technology to Mitigate AGI Risks and Address Global Debt Challenges

The emergence of Artificial General Intelligence (AGI) as Artificial Humanity (AH) marks a crucial turning point in human history, influencing both the digital economy and sustainable development. AGI has the potential to either drive unprecedented progress or threaten sustainability. This perspective study critiques existing capitalist systems and proposes a shift towards sustainable digital strategies to address the uncertain future role of AGI. It aims to encourage further research and collaboration for a smooth transition to a society that integrates AGI, highlighting AGI's potential to contribute to sustainable development. The study employs a multi-step methodology, including interviews with AI models such as ChatGPT-4 and Gemini, validation of insights, and comparisons with expert reports. It introduces the Digital Sustainable Growth Model (DSGM) as a framework for harmonizing humanity with AH, providing new opportunities for growth and ethical governance. The DSGM addresses both human vulnerabilities and AH’s potential. Additionally, the study highlights the BankRabbna application as an innovative digital tool, intended to be the first of its kind—a global company primarily owned by people worldwide. Its main function is to automate AI regulation, ensuring AGI safety while also tackling global debt.

Will ChatGPT be Useful for Korean Neurologists in Clinical Practice?

Background: Since the emergence of artificial intelligence (AI), such as ChatGPT, there has been extensive research on its utility in the medical field. This study evaluates the potential of utilizing the ChatGPT AI platform to provide specialized advice to Korean neurologists.Methods: Through a multifaceted approach, it assesses both the knowledge and practical clinical utility using neurology training evaluation questions and clinical case reports.Results: The accuracy of ChatGPT in answering medical exam questions (45.1%) is comparable to or slightly lower than previous studies. The study also examines performance of ChatGPT in providing advice based on clinical case reports, finding that while it can offer suitable diagnostic suggestions and propose relevant tests in some cases (8/12), its overall diagnostic accuracy is limited (2/12). Limitations include the platform’s text-based nature, which hinders the interpretation of image-based questions, and the uneven distribution of case reports across subfields.Conclusions: Despite these limitations, ongoing advancements in AI technology offer prospects for improved performance and expanded applications in the medical field, warranting continued research and monitoring of AI’s role in medicine.

Intelligent Emergency Assisted Decision-Making Method Based on Standard Digitalization: Hazardous Chemical Accidents in Industrial Parks

Contemporary society is confronted with multifaceted challenges, and the intricate interplay of interconnected factors significantly complicates emergency response efforts. Current practices rely on quick decisions by domain experts; however, the limitations of individual expertise and the urgency of crises hinder both precision and standardization. To address these issues, we propose a novel approach: an intelligent method for emergency decision making grounded in a standardized digital knowledge graph. First, our study examined the underlying theory of standardized digital transformation and event-chain evolution. This led to the construction of a knowledge graph encompassing standard emergency knowledge, as well as supplementary derivative data pertinent to event response. Second, through the application of semantic analysis and intention recognition of the decision target, coherent and interpretable query sentences for the decision system were crafted. These query sentences then served as a conduit for retrieving standard emergency knowledge relevant to the current emergency situation, as well as potential secondary disasters. The overarching goal is to provide emergency decision makers with effective support mechanisms that are both well informed and tailored to the specific demands of each situation.

Interplay of academic emotion regulation, academic mindfulness, L2 learning experience, academic motivation, and learner autonomy in intelligent computer-assisted language learning: A study of EFL learners

In the evolving landscape of second language (L2) education, Intelligent Computer-Assisted Language Learning (ICALL) stands out as a transformative force with the emergence of Artificial Intelligence (AI). Despite its growing prominence, integrating cognitive and affective constructs such as academic emotion regulation and mindfulness remains underexplored in ICALL environments. This study addresses this gap by examining their interplay with L2 learning experience, academic motivation, and learner autonomy among Iranian English as a Foreign Language (EFL) learners. Drawing on data from 398 intermediate EFL learners, this research utilized a comprehensive array of validated instruments, including the Academic Emotional Regulation Questionnaire, the Langer Mindfulness Scale, the L2 Learning Experience Scale, the Academic Motivation Scale, and the Learner Autonomy Questionnaire. Structural equation modeling (SEM) revealed significant correlations, indicating that academic emotion regulation positively influences L2 learning experience, academic motivation, and learner autonomy within ICALL settings. Furthermore, academic mindfulness emerged as a robust predictor of these educational outcomes in ICALL environments. These findings underscore the pivotal role of ICALL in L2 education and offer practical insights for teachers, curriculum developers, and policymakers to enhance teaching and learning practices.

Reinforce, readjust, reclaim: How artificial intelligence impacts journalism’s professional claim

Major advances in artificial intelligence have fuelled a rapid increase in the automation and augmentation of journalistic work, challenging the centrality of journalists in the news production process. This article theoretically explores news automation by adopting a system of professions framework from the sociology of professions to provide a holistic perspective on the impact of artificial intelligence on journalistic work. This framework posits that different factors influence professional control over work, and problems caused by these factors have left journalism vulnerable to automation. The routine and mundane nature of a significant portion of journalistic tasks suggests that artificial intelligence may potentially replace many journalists in the future, thereby challenging the profession’s claim to expertise. For journalism to uphold its professional authority in the future, it needs to brace for the impact of artificial intelligence. Building on this analysis, we explore strategies for journalism to do so. This involves reinforcing professional ideals in new algorithmic practices, readjusting knowledge and skill taught in education, and reclaiming specialised work practices in organisations. Rather than a threat, the emergence of artificial intelligence then presents an opportunity for journalism to reintroduce the distinctiveness of the profession and rejuvenate its professional promise.

Artificial Intelligence, the Production of Scientific Texts, and the Implications for Sleep Science: Exploring Emerging Paradigms and Perspectives.

The emergence of artificial intelligence (AI) has revolutionized many fields, including natural language processing, and marks a potential paradigm shift in the way we evaluate knowledge. One significant innovation in this area is ChatGPT, a large language model based on the GPT-3.5 architecture created by OpenAI, with one of its main aims being to aid in general text writing, including scientific texts. Here, we highlight the challenges and opportunities related to using generative AI and discuss both the benefits of its use, such as saving time by streamlining the writing process and reducing the amount of time spent on mundane tasks, and the potential drawbacks, including concerns regarding the accuracy and reliability of the information generated and its ethical use. In respect of both education and the writing of scientific texts, clear rules and objectives and institutional principles must be established for the use of AI. We also consider the positive and negative effects of the use of AI technologies on interpersonal interactions and behavior, and, as sleep scientists, its potential impacts on sleep. Striking a balance between the benefits and potential drawbacks of integrating AI into society demands ongoing research by experts, the wide dissemination of the scientific results, as well as continued public discourse on the subject.

Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life course

Established spatial and life course methods have helped epidemiologists and health and medical geographers study the impact of individual and area-level determinants on health disparities. While these methods are effective, the emergence of Geospatial Artificial Intelligence (GeoAI) offers new opportunities to leverage complex and multi-scalar data in spatial aging and life course research. The objective of this perspective is three-fold: (1) to review established methods in aging, life course, and spatial epidemiology research; (2) to highlight some of the opportunities offered by GeoAI for enhancing research on health disparities across life course and aging research; (3) to discuss the shortcomings of using GeoAI methods in aging and life course studies.

ICURE: Intensive care unit (ICU) risk evaluation for 30-day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden.

A prediction model that estimates mortality at admission to the intensive care unit (ICU) is of potential benefit to both patients and society. Logistic regression models like Simplified Acute Physiology Score 3 (SAPS 3) and APACHE are the traditional ICU mortality prediction models. With the emergence of machine learning (machine learning) and artificial intelligence, new possibilities arise to create prediction models that have the potential to sharpen predictive accuracy and reduce the likelihood of misclassification in the prediction of 30-day mortality. We used the Swedish Intensive Care Registry (SIR) to identify and include all patients ≥18 years of age admitted to general ICUs in Sweden from 2008 to 2022 with SAPS 3 score registered. Only data collected within 1 h of ICU admission was used. We had 153 candidate predictors including baseline characteristics, previous medical conditions, blood works, physiological parameters, cause of admission, and initial treatment. We stratified the data randomly on the outcome variable 30-day mortality and created a training set (80% of data) and a test set (20% of data). We evaluated several hundred prediction models using multiple ML frameworks including random forest, gradient boosting, neural networks, and logistic regression models. Model performance was evaluated by comparing the receiver operator characteristic area under the curve (AUC-ROC). The best performing model was fine-tuned by optimizing hyperparameters. The model's calibration was evaluated by a calibration belt. Ultimately, we simplified the best performing model with the top 1-20 predictors. We included 296,344 first-time ICU admissions. We found age, Glasgow Coma Scale, creatinine, systolic blood pressure, and pH being the most important predictors. The AUC-ROC was 0.884 in test data using all predictors, specificity 95.2%, sensitivity 47.0%, negative predictive value of 87.9% and positive predictive value of 70.7%. The final model showed excellent calibration. The ICU risk evaluation for 30-day mortality (ICURE) prediction model performed equally well to the SAPS 3 score with only eight variables and improved further with the addition of more variables. The ICURE prediction model predicts 30-day mortality rate at first-time ICU admission superiorly compared to the established SAPS 3 score.

A matrix of artificial intelligence tools in pre-service foreign language teacher training

Importance. The modern stage of information and technological development of civilization is characterised by the dynamic emergence of artificial intelligence (AI) technologies and the development of tools based on them, which are being more and more introduced into various spheres of life. The education system in general, and foreign language education in particular, is no exception. Currently, there are several dozen AI tools that are actively used by students and teachers in the development of foreign language communicative skills and the development of language skills. There is a rather voluminous body of research in the academic literature devoted to the disclosure of the language teaching potential of modern AI tools. However, most of the studies are of a pilot nature. The focus of scholars’ attention is on particular methods for the development of students’ communicative skills or the development of certain language skills based on individual AI tools. The systematic consideration of the integration of AI technologies into the process of teaching foreign language majoring students – future foreign language teachers – linguistic and teaching methods training has not been the subject of a special study. The purpose of this study is to develop a matrix of AI tools used in the linguistic and teaching methods training of future foreign language teachers.Materials and Methods. The study is conducted on the basis of the expert assessment method, which allows to: a) identify the language teaching potential, as well as the limitations of the most common AI tools; b) summarise and classify the available knowledge in the form of a matrix of AI tools used in the linguistic and teaching methods training of future foreign language teachers. The materials of the study were research articles, published in Russian and foreign academic journals, indexed in Web of Science and Scopus.Results and Discussion. A matrix of AI tools in the linguistic and teaching methods training of future foreign language teachers has been developed. The matrix is presented according to six types of feedback from generative AI used in foreign language teaching and teaching methods. The following are the main and most accessible AI tools for teachers and students providing feedback of each type: a) Replika, LingvoBot, Multitran_bot, Slavaribot, WorldContextBot, ChatGPT, Google Assistant, EGEEnglish.ru (educational and social feedback); b) ChatGPT, YandexGPT and GigaChat (information and reference feedback); c) ChatGPT 4.0, YandexGPT, GigaChat, Twee (methodological feedback); d) ChatGPT, YandexGPT, GigaChat, Turnitin, software “Antiplagiat” (analytical feedback); e) Grammarly, PaperRater, Pigai, ChatGPT 4.0, YandexGPT, GigaChat, Criterion (assessment and evaluative feedback); f) ChatGPT, YandexGPT, GigaChat, AI Poem Generator, Midjourney, Suno, Sora, Runway (conditionally creative feedback).Conclusion. The novelty of the research consists in the development of a matrix of AI tools in the linguistic and teaching methods training of future foreign language teachers. The prospects for further research lie in the development of teaching methods for aspects of language, types of speech activity, as well as specialised disciplines based on specific AI tools. In their entirety, these particular methods will enable the creation of an integrated system of linguistic and teaching methods training of future foreign language teachers based on AI tools.

Artificial intelligence and machine learning in optics: tutorial

Across the spectrum of scientific inquiry and practical applications, the emergence of artificial intelligence (AI) and machine learning (ML) has comprehensively revolutionized problem-solving methodologies. This tutorial explores key aspects of AI/ML and their remarkable role in augmenting the capabilities of optics and photonics technologies. Beginning with fundamental definitions and paradigms, the tutorial progresses to classical machine learning algorithms, with examples employing support vector machines and random forests. Extensive discussion of deep learning encompasses the backpropagation algorithm and artificial neural networks, with examples demonstrating the applications of dense and convolutional neural networks. Data augmentation and transfer learning are examined next as effective strategies for handling scenarios with limited datasets. Finally, the necessity of alleviating the burden of data collection and labeling is discussed, motivating the investigation of unsupervised and semi-supervised learning strategies as well as the utilization of reinforcement learning. By providing a structured exploration of AI/ML techniques, this tutorial equips researchers with the essential tools to begin leveraging AI’s transformative potential within the expansive realm of optics and photonics.

A Critical Study of Artificial Intelligence in Healthcare

The modern era has ushered the proliferation of new technologies, especially witnessed in the emergence of the nascent artificial intelligence (AI) sector. The use of AI is largely multifaceted, proving useful in various industries such as healthcare - however, it may also allow for deleterious effects to occur. The use of AI in healthcare settings can work to extend and augment the quality of patients’ lives. Notwithstanding this, health AI enshrines various perils including the lack of patient privacy, algorithm bias - particularly on marginalized and racialized communities. This is ultimately compounded by the absence of ethical framework governing the usage of AI in healthcare settings. Specifically, this article seeks to explore whether or not the use of health AI is a potential prospect or peril; considering its duality. To investigate this topic, this article will utilize an interdisciplinary approach – drawing from domains such as: sociology, socio-legal and socio-medical climates. Secondary data will be primarily sourced via peer-reviewed journal articles, textbooks, and reliable contemporary websites. This study finds that health AI remains a greater prospect - reinforcing the quality and elongates the duration of the human lifespan. It concludes with a call to action to inform the success of health AI in praxis: namely, the need to incorporate the aforementioned topics within medical pedagogy and ethical frameworks.

Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.

Learning with Generative Artificial Intelligence Within a Network of Co-Regulation

The emergence of generative artificial intelligence (AI) has created legitimate concerns surrounding academic integrity and the ease with which such technologies might lead to cheating in assessment, in particular. However, fixating solely on potential misconduct is overshadowing a more profound, transformative interaction between learners and machines. This commentary article delves into the relationship between students and AI, aiming to highlight the need for revised pedagogical strategies in the AI age. We argue that the much-discussed approaches that prioritise AI literacy or augmented critical thinking might be inadequate. Instead, we contend that a more holistic approach emphasising self-regulated learning (SRL) and co-regulation of learning is needed. SRL promotes autonomy, adaptability, and a deeper understanding, qualities indispensable for navigating the intricacies of AI-enhanced learning environments. Furthermore, we introduce the notion of a network of co-regulation, which underscores the intertwined learning processes between humans and machines. By positioning the self at the core of this network, we emphasise the indispensable role of individual agency in steering productive human-AI educational interactions. Our contention is that by fostering SRL and understanding co-regulated dynamics, educators can better equip learners for an interconnected AI-driven world.