Artificial Intelligence Science Research Articles

The Emerging Role of Artificial Intelligence in STEM Higher Education: A Critical Review

Artificial Intelligence (AI) has emerged as a disruptive force with the potential to transform various industries, and the field of higher education is no exception. This critical review paper aims to examine the emerging role of AI in Science, Technology, Engineering, and Mathematics (STEM) higher education. The article explores the impact of AI on teaching and learning methodologies, curriculum design, student engagement, assessment practices, and institutional strategies. The review also highlights the potential benefits and challenges associated with integrating AI into STEM education and identify key areas for future research and development. Overall, this article provides insights into how AI can revolutionize STEM higher education and offers recommendations for harnessing its full potential.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)

Background and objectiveObstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. MethodsWe investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. ResultsWe found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. ConclusionsOur review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

A survey on deep learning-based image forgery detection

Image is known as one of the communication tools between humans. With the development and availability of digital devices such as cameras and cell phones, taking images has become easy anywhere. Images are used in many medical, forensic medicine, and judiciary applications. Sometimes images are used as evidence, so the authenticity and reliability of digital images are increasingly important. Some people manipulate images by adding or deleting parts of an image, which makes the image invalid. Therefore, image forgery detection and localization are important. The development of image editing tools has made this issue an important problem in the field of computer vision. In recent years, many different algorithms have been proposed to detect forgery in the image and pixel levels. All these algorithms are categorized into two main methods: traditional and deep-learning methods. The deep learning method is one of the important branches of artificial intelligence science. This method has become one of the most popular methods in most computer vision problems due to the automatic identification and prediction process and robustness against geometric transformations and post-processing operations. In this study, a comprehensive review of image forgery types, benchmark datasets, evaluation metrics in forgery detection, traditional forgery detection methods, discovering the weaknesses and limitations of traditional methods, forgery detection with deep learning methods, and the performance of this method is presented. According to the expansion of deep-learning methods and their successful performance in most computer vision problems, our main focus in this study is forgery detection based on deep-learning methods. This survey can be helpful for a researcher to obtain a deep background in the forgery detection field.

Gut bacteria and sex differences in colorectal cancer.

Introduction. Differences in gut bacteria that are associated with the occurrence and development of colorectal cancer (CRC) exist between sexes, and males have a higher morbidity of CRC.Gap Statement. Clinical data for the relationship between gut bacteria and sexes in patients with CRC are not available and are needed to support individualized screening and treatment programmes.Aim. To analyse the relationship between gut bacteria and sexes in patients with CRC.Methodology. A total of 6 077 samples recruited by Fudan University's Academy of Brain Artificial Intelligence Science and Technology were included, and the gut bacteria composition mainly shows the top 30 genera. Linear discriminant analysis Effect Size (LEfSe) was used to analyse the differences in gut bacteria. Pearson correlation coefficients were calculated to demonstrate the relationship of discrepant bacteria. CRC risk prediction models were used to rank the importance of valid discrepant bacteria.Results. Bacteroides, Eubacterium and Faecalibacterium were the top three bacteria in males with CRC, while Bacteroides, Subdoligranulum and Eubacterium were the top three bacteria in females with CRC. The abundance of gut bacteria (Escherichia, Eubacteriales, Clostridia, etc.) was higher in males with CRC compared with that in females with CRC. In addition, Dorea and Bacteroides were important CRC-related bacteria (P<0.001). Finally, the importance of discrepant bacteria was ranked based on CRC risk prediction models. Blautia, Barnesiella and Anaerostipes were the top three important discrepant bacteria between males with CRC and females with CRC. The value of AUC was 1.0, the sensitivity was 92.0 %, the specificity was 68.4 %, and the accuracy was 83.3 % in the discovery set.Conclusion. Gut bacteria were correlated with sexes and CRC. It is necessary to consider gender when gut bacteria are used to treat and predict CRC.

Socially intelligent machines that learn from humans and help humans learn.

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL)by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human-machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans byproactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Autopoiesis of the artificial: from systems to cognition

In the seminal work on autopoiesis by Varela, Maturana, and Uribe, they start by addressing the confusion between processes that are history dependent and processes that are history independent in the biological world. The former is particularly linked to evolution and ontogenesis, while the latter pertains to the organizational features of biological individuals. Varela, Maturana, and Uribe reject this framework and propose their original theory of autopoietic organization, which emphasizes the strong complementarity of temporal and non-temporal phenomena. They argue that the dichotomy between structure and organization lies at the core of the unity of living systems. By opposing history-dependent and history-independent processes, methodological challenges arise in explaining phenomena related to living systems and cognition. Consequently, Maturana and Varela reject this approach in defining autopoietic organization. I argue, however, that this relationship presents an issue that can be found in recent developments of the science of artificial intelligence (AI) in different ways, giving rise to related concerns. While highly capable AI systems exist that can perform cognitive tasks, their internal workings and the specific contributions of their components to the overall system behavior, understood as a unified whole, remain largely uninterpretable. This article explores the connection between biological systems, cognition, and recent developments in AI systems that could potentially be linked to autopoiesis and related concepts such as autonomy and organization. The aim is to assess the advantages and disadvantages of employing autopoiesis in the synthetic (artificial) explanation for biological cognitive systems and to determine if and how the notion of autopoiesis can still be fruitful in this perspective.

Chomsky’s Theory of Mind: Concepts and Contents

Nowadays, it is very debatable among philosophers, psychologists, linguists and artificial intelligence scientists that what mind actually is, where it exists and how it works? Chomsky, the great philosopher and the main exponent of cognitive revolution tries to sketch the description of mind, its nature, mental processes, its structure as well as its relation with its other cognitive modules. It also examines innate and acquired knowledge of mind. In this paper, I shall argue about the capacities of the mind, why Chomsky called mind capacity as different from its ability and other faculties. This paper also describes the theories that are connected with the field of mind i.e, mind and language, linguistic mentalism, Modular mind, Cartesian mind, mind-body problem, Philosophical grammar, and thought processes like reasoning and perceptions. Firstly, I shall argue about, does Chomsky consider mind different from brain, consciousness and thought? Secondly, does Chomsky consider mind as a separate concept or he regards it as the constituent of language only? And thirdly, how Chomsky responds to rationalists and empiricists?

Artificial intelligence on the advance to enhance educational assessment: Scientific clickbait or genuine gamechanger?

AbstractContributions in the Special IssueThe special issue assembles papers centring around log data analysis, natural language processing, and machine learning used to advance educational assessment. They demonstrate how semi‐ and unstructured data such as log and text data can, despite their challenging nature, be handled appropriately to benefit educational assessment. In this editorial, we contextualize the special issue's contributions within the diverse field of modern technology‐based assessments.Reflection on TerminologyMoreover, we raise concerns about nowadays' use of the term artificial intelligence (AI) in scientific communication. While the contribution of AI to scientific progress is indisputable, the mere use of methods that have evolved within AI research does not necessarily render tools or studies AI‐related. We argue that academics have the social responsibility to adopt accurate terminology, given it is integral to scientific rigour and proper scientific communication.ImplicationsIn view of the inflationary use of the term AI in science, we propose a scheme to locate one's research in the field by focusing on (1) the type of data, (2) the processing involved, and (3) the output of a study and the actions derived from it, which are situated within the (4) scope of a study.

SYSTEMATIC DISCUSSION OF ARTIFICIAL INTELLIGENCE’S PERSONALITY IN CRIMINAL LAW AND THE PUNISHABILITY FOR IMPROPER OMISSION BY THE GROUP ACTING AS THEIR GUARANTOR POSITION

The rapid development of artificial intelligence (AI) in the twenty-first century not only makes our life more convenient but also brings some invisible concerns. Through the study of several typical AI applications, it is found that today's AI cannot realize its full functionality without its users. Therefore, users still bear most of the legal liability for damage to legally-protected interests caused by AI. However, despite the continuous development of AI, the ways of remedy for damages to legally-protected interests, that may occur as a result of the crime of omission, arising during the logical writing of program codes are rarely conclusive. In addition to distinguishing the main framework between AI, machine learning, and deep learning in criminal law, this research arguing, based on philosophical jurisprudence, concludes that although AI has yet to meet the personality characteristics in criminal law due to its lack of independence, AI scientists or legal persons representing it must hold the guarantor position in an improper omission. This will render the scientists and legal persons punishable and relief petitions possible, supplying a considerable legal basis for future formulation of legal norms on AI based on the principle of nulla poena sine lege (no crime without law, or principle of legality).

Knowledge, attitudes and practices towards artificial intelligence (AI) among radiologists in Saudi Arabia

BackgroundAs the science of artificial intelligence (AI) continues to grow, its incorporation into the medical profession has become more widespread globally. Incorporating AI into healthcare systems has the ability not only to improve diagnosis and treatment procedures, but also to optimize resource allocation. ObjectiveThis study aims to get a deeper understanding of radiologists' perspectives on the incorporation of AI in the area of radiology in Saudi Arabia. The purpose of this qualitative study is to provide recommendations that will enhance the growth and performance of the nation's radiography workforce. MethodsWe conducted a cross-sectional study using an online survey to collect data on the demographic features, knowledge, perceptions, and practices linked to AI among radiologists and subspecialties. A suitable sample of 129 radiologists was selected and the data were analyzed using chi-squared testing to compare factors. ResultsOur study indicates that out of 129 radiologists, 89 (69.0%) respondents, who had heard about AI, had a basic knowledge of artificial intelligence/deep learning/machine learning. While the majority of the participants had a positive outlook, 17.0% of participants indicated concern that AI will replace their jobs. ConclusionThe findings of the study provide a comprehensive overview of the current level of understanding and utilization of AI in radiology among the radiologists in the region. The results suggest that radiologists have a basic knowledge of AI, and they are eager to incorporate it into their field; however, there is a need for further training to increase the radiologists' applications in the field.

From “what” to “how”: Extracting the Procedural Scientific Information Toward the Metric-optimization in AI

In response to the exponential growth of the volume of scientific publications, researchers have proposed a multitude of information extraction methods for extracting entities and relations, such as task, dataset, metric, and method entities. However, the existing methods cannot directly provide readers with procedural scientific information that demonstrates the path to the problem's solution. From the perspective of applied science, we propose a novel schema for the applied AI community, namely a metric-driven mechanism schema (Operation, Effect, Direction). Our schema depicts the procedural scientific information concerning “How to optimize the quantitative metrics for a specific task?” In this paper, we choose papers in the domain of NLP for our study, which is a representative branch of Artificial Intelligence (AI). Specifically, we first construct a dataset that covers the metric-driven mechanisms in single and multiple sentences. Then we propose a framework for extracting metric-driven mechanisms, which includes three sub-models: 1) a mechanism detection model, 2) a query-guided seq2seq mechanism extraction model, and 3) a task recognition model. Finally, a metric-driven mechanism knowledge graph, named MKGNLP, is constructed. Our MKGNLP has over 43K n-ary mechanism relations in the form of (Operation, Effect, Direction, Task). The human evaluation shows that the extracted metric-driven mechanisms in MKGNLP achieve 81.4% accuracy. Our model also shows the potential for creating applications to assist applied AI scientists to solve specific problems.

Artificial intelligence designer for optical Fibers: Inverse design of a Hollow-Core Anti-Resonant fiber based on a tandem neural network

In this work, artificial intelligence (AI) is trained to “study” optical fibers as an AI optical fiber scientist. The dataset is constructed on the structural parameters and confinement loss of hollow-core anti-resonant fibers and is divided into different datasets. An effective approach to overcome the non-uniqueness challenge of inverse designs is the tandem neural network (T-NN), which consists of a forward prediction model and an inverse design model, which are trained by different datasets. The impact of the hyperparameter on the performance of the proposed model is also studied. For the forward prediction model, the mean square error reaches the lowest value of 0.0007 with 6 hidden layers; the nodes of each hidden layer are 800, and the corresponding coefficient of determination R2 is 0.9154. Moreover, the R2CL of the T-NN can reach the maximum value of 0.9881 for the dataset-1-0. The comparison between the target, prediction result, and calculation result proves that the proposed approach is an effective way to design hollow-core anti-resonant fibers intelligently. Compared with current numerical simulation methods, the presented AI model based on the T-NN is more “intelligent”. This study can both accelerate the design of hollow-core anti-resonant fibers and provide guidance on the development of AI scientists in optics.

Expert System for Diagnosing Cow Disease Using Web-Based Forward Chaining Method

Media Technology and Information is currently developing quite quickly and well. Some people and even almost all of the surrounding community are familiar with technology, especially in the science of artificial intelligence. With this expert system, it can help alleviate the need when it is difficult to meet experts, especially livestock specialist diseases. The purpose and benefits of this expert system is to make it easier for researchers to identify livestock disease problems based on the symptoms suffered by cattle. The identification method used in this system is the Forward Chaining Method and collects symptom data from several sources and verified journals. The desired result of this research is the reduction of disease or outbreaks that occur in Livestock Cattle &#x0D; and provide prevention and treatment solutions so as not to be attacked by diseases and outbreaks..

Deep Learning Opacity in Scientific Discovery

AbstractWhile philosophers have focused on epistemological and ethical challenges of using artificial intelligence (AI) in science, scientists have focused largely on opportunities. I argue that this disconnect between philosophical pessimism and scientific optimism is driven by failures to critically examine the practice of AI-infused science. To appreciate the epistemic justification for AI-powered breakthroughs, philosophers must analyze the role of AI as part of a wider process of discovery. I demonstrate the importance of this with two cases from the scientific literature, and show that epistemic opacity need not diminish AI’s capacity to lead scientists to significant and justifiable breakthroughs.

Recent Trends in The Use of Artificial Intelligence Techniques and Their Impact on Investment, Development and Growth for Developed and Developing Countries Under Corona Pandemic

This research aims to identify the modern trends in the use of artificial intelligence technology and its impact on investment, development and growth for countries in light of the Corona pandemic, as well as to identify the concept of artificial intelligence technology, and the concept of investment, and the importance of this research stems from the fact that it discusses the modern technology that enters all fields of science, which is Artificial intelligence technology, which has applied in most of the sciences, if not all, as the use of artificial intelligence contributes to advancing the economy forward. The historical and descriptive analytical method is used in this study which is appropriate to the nature of the study. One of the most important findings of the research is that the advanced technologies and advanced in the science of artificial intelligence will provide a lot of investment, development and growth opportunities for countries. Keywords: investment, economic development, artificial intelligence growth, the economy.

Who makes AI? Gender and portrayals of AI scientists in popular film, 1920-2020.

It is well established both that women are underrepresented in the field of artificial intelligence (AI) and that media representations of professions have impact on career choices and prospects. We therefore hypothesised that women are underrepresented in portrayals of AI researchers in influential films. We tested this by analysing a corpus of the 142 most influential films featuring AI from 1920 to 2020, of which 86 showed one or more AI researchers, totalling 116 individuals. We found that nine AI professionals in film were women (8%). We further found that none of the 142 AI films was solely directed by a woman. We discuss a number of explanations for the paucity of women AI scientists in the media, including parallels between film and real-life gender inequality, the construction of the AI scientist as male through gendered narrative tropes, and the lack of female directors.

Artificial Intelligence Impact on Legal, Education and General Rights of Children

The science of Artificial Intelligence (AI) has a wide range of methodologies, breakthroughs in machine learning, deep learning, and encourages a paradigm change in almost all areas of the IT sector. AI is utilised in towns for traffic management and safety of the people; in the infirmaries, through applications in equipment that help doctors diagnose diseases; through the application of algorithms in education, which allow for individualised learning. The emergence of AI is a global phenomenon linked to what is frequently referred to as the 4th Industrial Revolution. Children are also impacted by the increasing integration of artificial intelligence-based technologies into modern life. The possible influence of AI on children warrants special consideration. We already know that artificial intelligence will affect practically every part of our life, despite the fact that there are still many unsolved questions about it. However, children will be more affected in many ways due to factors such as how they are born, the resources and learning techniques that are available to them, as well as the jobs that they will be trained for. This truth carries enormous opportunities and peril. The issue is that when AI hurts children, the harm will follow them into adulthood and last for a very long period. However, AI for kids has enormous potential for enhancing learning, development, safety, and opportunity. All of these conversations must start right away since children's healthy development is essential to the future prosperity of every country, and disappointing our children's generation would be extremely costly. The purpose of the current article is to examine how AI may, indirectly or directly, affect the lives of children and their educational processes. Keywords: Artificial Intelligence, Deep Learning, Education, Children Rights.

PROFESSIONAL DEVELOPMENT OF SCIENCE AND MATHEMATICS TEACHERS USING ARTIFICIAL INTELLIGENCE

The content of the article emphasizes the importance of training teachers of natural and mathematical fields to use artificial intelligence in their professional activities and developing their competencies to prepare students for modern challenges and opportunities provided by this technology. The analysis of scientific and methodical literature made it possible to determine the advantages and disadvantages of using AI in the educational process of institutions of general secondary education and to propose directions for improving the qualifications of teachers of natural sciences and mathematics. The use of artificial intelligence in science and mathematics education has the potential to improve student achievement outcomes and provide a more personalized learning experience, but with consideration of the potential risks and limitations of artificial intelligence in education, such as the risk of over-reliance on technology, data security. The analysis of the results of the survey of teachers confirms the readiness of science and mathematics teachers for professional development, mastery of advanced training programs for the use of AI. A necessary condition for the introduction of AI is the alignment of the artificial intelligence model with the vision of teachers regarding education, in particular the possibilities of achieving educational goals, alignment with advanced practices in education. The definition of artificial intelligence tools for the science and mathematics field of general secondary education based on the introduction of theoretical research methods made it possible to generalize the directions of professional development of teachers of mathematics and science fields on the implementation of AI and to form the content lines of the educational and professional program of teacher training. The study considers the importance and directions of training teachers for the use of artificial intelligence in postgraduate education and highlights ideas and approaches that will help to effectively introduce these technologies into the educational process, contributing to the improvement of the quality of education and the preparation of the younger generation for the challenges of the future.

Artificial Intelligence: Applications and Reflections [In Arabic

Artificial intelligence is the main important result of the Fourth Industrial Revolution because of its multiple uses in many fields such as industry, economy, technology and education. It should also allow opportunity to unlimited innovations and lead to more industrial revolutions, bringing about a fundamental change in human life.&#x0D; Therefore, this research paper aims to shed light on artificial intelligence as one of the most important fields in the modern period and demonstrate its most applications and various consequences. To achieve this, the descriptive approach was followed to identify the concepts associated with artificial intelligence, as well as the identification of its main important effects, in addition to the analytical approach to analyse the most important reflections of artificial intelligence techniques.&#x0D; This study concluded that the use of artificial intelligence applications in many fields has become an undeniable fundamental role in our world today. and that we must prepare to enter the Fourth Industrial Revolution by investing in human and artificial intelligence. Even though the science of artificial intelligence remains a vast field, many of its theories are still being researched and developed.

First opera about Artificial Intelligence: Miroslav Srnka “Singularity”

Relevance of the study. Scientists have been working on the research and practical implementation of artificial intelligence (AI) since the middle of the last century. In 2021, this research area celebrated a series of anniversaries that highlight an 85-year history of the impact of artificial intelligence on human consciousness. Various historical stages of the development of artificial intelligence in science, technology and culture are considered. In 2021, the first opera dedicated to this topic appeared. The Czech composer Miroslav Srnka tried to disguise the seriousness of the problem in the genre of opera-comedy, opera-farce. Through the prism of the game, the artist warns against the danger of the future mastery of AI by the human brain. Not only scientists from all branches of science, but also musicians are asking Facebook, Google and other megacorporations about the ultimate goal of billions of dollars invested in AI R&amp;D.&#x0D; Main objectives of this study are 1) to outline the chronology of the historical stages of artificial intelligence research; 2) to present the problems of AI in different fields of science and culture; 3) to analyze the first opera created in 2021 about artificial intelligence. Miroslav Srnka's A Space Opera for Yuong Voises is a musician's reflection on the problem of the hypothetical future of humanity in the era of transhumanism.&#x0D; Research methodology. The work uses a complex application of historical, comparative and textological approaches and methods to reveal the specifics of the embodiment of non-musical modern electronic technologies in the genre of opera.&#x0D; The results of directorial artifacts. In the Science-Fiction-Opera of the Czech composer Miroslav Srnka the experience of digital communication of people in the future and the problems of the hypothetical future of humanity in the era of transhumanism, are presented like in a 'distorting mirror'. Ten years ago, a systematic survey of four different expert groups has been made which included more than 550 scientists. The results were published in a collection of articles by futurists Nick Buström and Vincent C. Müller (researchers at Oxford and Eindhoven University of Technology) named “Future Progress in Artificial Intelligence: A Survey of Expert Opinions”. When asked how soon mankind will master powerful AI, the average response rate of experts showed that 50% of the scientists answered positively to the likelihood of a strong AI availability in the period from 2040 to 2075. We therefore have a good chance to become witnesses of the hypothetical future predicted by futurists, science fiction writers and the authors of the opera “Singularity”, if we believe in the myth of the singularity. However, a large group of scientists rejects the hypothesis of the initiator of the opera “Singularity”, Ray Kurzweil that “The Singularity is Near” and doubts that the future of the singularity can be predicted. The opera “Singularity” is the first musical and theatrical experiment on the interaction and fusion of artificial intelligence (AI) with emotional intelligence (EI). From their inception theater, myths and religious rituals have always been the theme of art, reflecting the universe, legends, cults of society, predicting the future. The opera “Singularity” will take its place in the history of futurology as a musical and theatrical artifact about the myth of a global world in the 21st century.