Knowledge Diversity at Universities?

In recent years artificial intelligence has entered a new era, which gives rise to many hopes for powerful states such as the United States and China. In this paper, we analyze the importance and role of artificial intelligence in technological development in each of the two countries on the one hand, and its influence on China-American relations in terms of technological and geopolitical conflict. To get the right results, we rely on a literature review of dozens of articles published on the phenomenon in order to compare the power of artificial intelligence between the United States and China where we found that the US still has technological strength, especially in the field of artificial intelligence, but we can say that a large force is beginning pose a threat for it which is China that has great technological capabilities so, we can say that the United States should work more in this field. Also, we found that artificial intelligence has a primary goal in both countries, it helps China to achieve its ambitions to be the leader of the world, and this intelligence, on the other hand, provides protection and security to the United States. This paper is divided into three sections. The first section focuses on the importance of artificial intelligence in achieving China’s ambitions, the second section explains the role of artificial intelligence in the US protection service, and the third section describes the technological and geopolitical conflict resulting from the competition in artificial intelligence between these two countries. Keywords: Artificial intelligence, United States, China, Conflict, leader.

Education data scientists, learning engineers and precision education specialists are new experts in knowledge production in educational research. By bringing together data science methodologies and advanced artificial intelligence (AI) systems with disciplinary expertise from the psychological, biological and brain sciences, they are building a new field of AI-based learning science. This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.

This article outlines areas of action of researchers as tools to interpret for whom the area of Education produces knowledge. It contextualizes the research in Education in Brazil in its peculiar institutional and epistemological conditions. It works with sociological concepts such as scientific field, scientific capitals, and autonomy of the professional field to illuminate aspects of the Brazilian academic context of knowledge production in Education. It associates these concepts with the notions of domain and dialogue with audiences to advance in the interpretation of the contradictions and heteronomies faced by the area. It is grounded on the qualitative analysis of semi-structured interviews with Brazilian researchers recognized for their academic representativeness with productivity grants and a history of leadership in scientific research. It questions how relationships with different social groups support processes of knowledge production and circulation. To account for this questioning, it characterizes the academic-scientific subfield of Education in terms of specific capitals, research audiences, prestige criteria, and political relations. It discusses strategies of dialogue as instrumental categories to interpret phenomena associated with scientific and political legitimacy. As a result, it finds four domains of action in knowledge production in Education: scientific-disciplinary; pedagogical; political-managerial; dialogical socio-educational. It concludes that, when acting in the different domains, these researchers differently order the priorities conferred to scientific rigor and socio-educational intervention.

The production and circulation of knowledge in education increasingly depends on large-scale digital infrastructures. In this article we provide a critical review of the transformation of the contemporary ‘knowledge infrastructures’ underpinning knowledge production and reproduction in education. Contributing to the Special Issue on ‘knowledge crises’ in education, we conceive of recent and ongoing developments in the digitalisation and datafication of education as a ‘knowledge infrastructure crisis’ that is exerting profound effects on education as a field of practice and research. The article examines (1) the knowledge technologies, such as digital platforms and artificial intelligence, being introduced into educational practice, and (2) transformations in knowledge-making systems, including AI research services, that are now shaping educational research. These infrastructural transformations portend the erosion of teachers’ pedagogic autonomy and authority over curricular knowledge by automated technologies, the displacement of research to commercial AI labs and a reconfiguration of educational research as a big data enterprise. As a response to this knowledge infrastructure crisis, we offer (3) a review of alternative infrastructuring projects in the domains of educational practice and research, and their potential to address the growing democratic deficits in educational knowledge production, access and reproduction.

Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.

In our world, technological development has become inherent in all walks of life and is characterized by its speed in performance and uses. This development required the emergence of new technologies that represent a future revolution for a fourth industrial revolution in various fields, which contributed to finding many alternatives and innovative technical solutions that shortened time and space in terms of making Machines are smarter, more accurate, and faster in accomplishing the tasks intended for them, and we find the emergence of what is called artificial intelligence (artificial intelligence), which is the technology of the future, which is one of the most important outputs of the fourth industrial revolution, and artificial intelligence has become a comprehensive term for applications that perform complex tasks that required in the past human inputs such as Communicating with customers via the Internet and various digital applications, as it saved a lot of time, effort, and money thanks to its various capabilities, specifically in the field of graphic design. in graphic design The study aimed to identify the effectiveness of artificial intelligence in contemporary digital graphic design and to benefit from its applications through its employment in the field of graphic design. And the techniques used to design and develop artificial intelligence applications 4- The importance of artificial intelligence and its effectiveness in developing graphic design skills. Its effective capabilities enable and facilitate contemporary digital graphic design processes. The research also came out with conclusions, recommendations and proposals that came from it. Results: 1- Artificial intelligence for the graphic designer shortens the implementation of various designs and analyzes problems with accuracy and high speed, which allows achieving creativity and developing his skills. 2- Artificial intelligence can process a huge amount of data and information in a short period of time compared to time spent on traditional digital design. 3- Artificial intelligence enables the graphic designer to find solutions to unfamiliar problems that he faces when executing the design work. The researcher came out with conclusions, including: 1 - The revolution in the world of artificial intelligence in the future will make applications capable of carrying out various tasks of graphic design and most areas of our lives. 2- Artificial intelligence helps users create new patterns and different designs with less experience and knowledge in the field of design. The researcher also recommended directing graphic designers and students to work and make use of artificial intelligence techniques in the various stages of design to save time and effort and avoid making mistakes. The researcher also suggested studying the applications of artificial intelligence and their uses in the digital graphic achievement. The search was also included in the list of Arab and foreign sources

O presente texto apresenta alguns pontos relevantes acerca dos problemas da ciência e da produção de conhecimento em Educação assinalados em diferentes obras escritas pela Dra. Maria Célia Marcondes de Moraes. Diversos problemas teóricos preocuparam a autora, em especial, o do ceticismo vigente e as profundas contradições nas quais os sentidos e conceitos – que significam movimentos teórico-práticos do real – se apresentam com sutis e marcadas inversões impactando o campo e a pesquisa educacional. Com base em uma perspectiva ontológica materialista e crítica Moraes indagou e aprofundou estas questões, e em especial, discutiu o papel do ser educação em sua máxima expressão.

In this article, I argue that key characteristics of educational knowledge both constrain and enable the work of educational researchers, as producers of this knowledge, in distinctive ways. Educational knowledge is soft (vs. hard), applied (vs. Pure), and provides use value (vs. exchange value). As a result, knowledge production in education is organized in a manner that is structurally egalitarian and substantively divergent. Some consequences of this are negative. For example, educational researchers find themselves unable to speak authoritatively about their field and feel pressure to imitate unproductive forms of intellectual practice. Other consequences are positive: For example, they have the potential for speaking to a wide lay audience and for participating in a relatively open and unregulated mode of scholarly production.

Artificial intelligence is a kind of “universal global neural network” which connects different things easily with their algorithms and software. Artificial intelligent is the intelligent machine which works better than human beings. It is replacing the work done by human beings from machines. This technology is rapidly increasing day-by-day. Already we have so many applications of this technology are used tremendously. Machine learning, problem solving and perception are the part of artificial intelligence. Keyword: Machine learning, neural network, automation, artificial intelligence Cite this Article Manali Joshi, Alfa Jain, Sagar Soni. A Review Paper on Importance of Artificial Intelligence. Current Trends in Signal Processing . 2019; 9(2): 1–5p.

There are many ways to define the field of Artificial Intelligence. Here is one way for Artificial Intelligence is "The Study of the computations that make it possible to perceive, reason, act and predict the future possible outcomes”. Deep learning, which is a popular research area of artificial intelligence (AI), enables the creation of end-to-end models to achieve promised results using input data. Deep learning techniques have been successfully applied in many problems such as arrhythmia detection, skin cancer classification, breast cancer detection, brain disease classification, pneumonia detection, COVID-19 from chest X-ray images, and CT scan images. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of diseases. However, the chest CT classification involves a radiology expert and considerable time, which is valuable when any infection is growing at a rapid rate. Therefore, automated analysis of chest CT images is desirable to save the medical professionals precious time that shows the importance of Artificial Intelligence neural networks which is used to classify the infected patients as infected (+ve) or not (−ve). There is a vital need to detect the disease at an early stage and save the patient from the disease. Convolutional neural networks (CNN) are a powerful tool that comes under the platform of Neural Networks – Artificial Intelligence inspired by the human brain, which is extensively utilized for image classification. The hierarchical structure and efficient feature extraction characteristics from an image make CNN a dynamic model for image classification. Initially, the layers are organized into three dimensions: width, height, and depth. The neurons in a given layer do not attach to the entire set of neurons in the later layer, but only to limited neurons of it. Finally, the output is diminished to a single vector of probability scores, coordinated alongside the depth dimension. In a Convolutional Neural Network, the linear function that is used is called a convolutional layer. Each node in the hidden layer extracts different features by using image processing feature detectors. For example, in the first layer, the first node may extract the horizontal edges of an image, the second node may extract vertical edges and etc. These features are extracted using a kernel. The bottom is the original image and the top is the output of the convolutions. It is also worth noting that the output of the convolutions reduces the dimension of the original image, The next step the pooling layer happens tends to be computed after the convolutional layer. The reason why pooling is done is to further reduce the dimensions of the convolutional layer and just extract out the features to make the model more robust. AI could help to rapidly diagnose diseases if proper attention given in collecting the data. Citation Format: Subash Kumar. Importance of artificial intelligence, machine learning deep learning in the field of medicine on the future role of the physician [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-056.

The demand for artificial intelligence in the world is increasing day by day in the gastronomy sector. In this study, the importance of artificial intelligence in the gastronomy sector is emphasized and the studies on this subject are mentioned. Since the studies in the literature are limited, studies on all sub-branches related to artificial intelligence-based gastronomy and tourism are mentioned. The number of studies related to gastronomy and artificial intelligence in the literature is limited. Therefore, in this study, artificial intelligence applications used in the gastronomy sector are detailed under the subheadings of cuisine, promotion, health, and forecasting. Accordingly, approximately 40 articles were analyzed considering these sub-headings. In light of the information obtained from these studies, artificial intelligence systems to be developed in the gastronomy sector will provide great gains. Moreover, it is estimated that meeting consumer expectations and using innovative technologies in the gastronomy sector will increase the popularity of gastronomy science.

Concern expressed by government and other funding agencies and consumers of research, about the quality and relevance of research in the field of education affects not only the kind of research is conducted but also the way in which we educate researchers. The economic imperative for ‘value for money’ from research and researchers has, for instance, led to the education of research students to be seen increasingly in terms of training in a range of generic skills that can be applied to the investigation of a range of forms of research problem in a variety of contexts. Whilst breadth in the education of researchers has clear advantages, both for the careers of individual researchers and the wider research community, there is some tension between this approach and the more established view of a research degree as an induction into a narrow domain of knowledge and the production of a highly specialised academic identity. There are further developments that erode this notion of specialisation, for instance the growth of mixed method research, which has the potential to challenge the polarisation of qualitative and quantitative research, and shifts in the sites and agents of educational research signified by the growth of professional doctorates, which could further challenge the university as a dominant institution in the production of educational knowledge. In this paper I will explore what these developments mean for the teaching of research and consider how we can work collaboratively to develop both professional researchers and researching professionals, and reconcile the acquisition of skills with induction into specialised knowledge domains. This will involve exploration of both an overarching framework for thinking about the processes of doing research and specific examples of practice. Underlying the approach taken is a general commitment to research education, rather than to training and the teaching of methods, and the desire to ensure dynamism and diversity in educational research, in terms of approach, substantive focus and theoretical orientation, and of sites, practices

This study conducts a bibliometric analysis of global trends in 'artificial intelligence studies in veterinary medicine'. The analysis aims to summarise the publications of researchers from various disciplines related to artificial intelligence in veterinary medicine, thereby predicting future trends of AI in this field. The primary objective of the study is to investigate publications pertaining to artificial intelligence in veterinary medicine worldwide and to analyse trends and future developments in this area. This bibliometric study examines artificial intelligence research in veterinary medicine conducted worldwide from 1990 to 2024. To achieve this, a search using the keywords 'artificial intelligence' and 'veterinary medicine' was performed in the Web of Science (WOS) database, resulting in the identification of 1497 studies. After excluding irrelevant publications and those outside the scope of articles, a total of 1400 articles were included in the analysis. The data collection process utilised titles, author names, publication years, journal names, and citation counts. All textual data were analysed using VOSviewer software to ensure accuracy and reliability. In this study, analyses conducted through text mining and data visualisation techniques (e.g., bubble maps) facilitated a clearer understanding of the results. This study presents information about 1400 articles obtained from the WOS database and a total of 44,700 citations for these articles. The average number of citations per article is 32, with an H-index of 74. A rapid increase in both the number of articles and citations has been observed since 2019. The majority of the articles (30%) were published in the fields of veterinary sciences, artificial intelligence, and computer sciences. The United States, Taiwan and the United Kingdom are the leading countries, accounting for 84% of the published articles in this field. Additionally, 12% of the articles were published in the area of veterinary sciences, and 85% of the articles fall within the SCI-Expanded category. The findings of our study indicate that there are numerous active researchers in the field of artificial intelligence in veterinary medicine and that research in this area is steadily increasing. This bibliometric analysis highlights global trends and significant works in artificial intelligence within veterinary medicine, providing valuable insights into the future directions of research in this field. As the analysis aims solely to identify trends and patterns in the literature, it does not intend to evaluate the applicability of the subject matter. Analysis of Global Trends: This study comprehensively analyses the global trends and effects of research on artificial intelligence in veterinary medicine. In this context, it contributes to the identification of significant changes and developments in the literature. Rapidly Spreading Research: Research on artificial intelligence in veterinary medicine has rapidly expanded in recent years, and this trend is expected to continue. The increase in studies indicates an expansion of knowledge and applications in this field. Diagnostic and Therapeutic Tools: Artificial intelligence research serves as a valuable tool in veterinary medicine, particularly in improving the diagnosis and treatment processes for various diseases. This contributes to the development of more effective methods for animal health and care. Increasing Number of Publications: The number of studies on artificial intelligence in veterinary medicine worldwide is increasing each year. Notably, after the Covid-19 pandemic, there has been a significant rise in publications in this field. This indicates that the importance of artificial intelligence in both human and animal health has grown, with the pandemic intensifying research interest. Prominent Countries: Among the countries examined in the study, the United States, Taiwan, England, and Germany emerged as leaders in this research area. Conversely, it was noted that some countries have very few or no academic publications in the field of artificial intelligence in veterinary medicine.

This scientific work provides information about the importance of artificial intelligence in modern media education technologies in institutions of higher education. Artificial intelligence in media education in Uzbekistan offers unique opportunities for students and educators. Initially, artificial intelligence (AI) is employed for personalizing the educational process by adapting learning materials to the individual needs of each student. This contributes to more effective comprehension of the material and improved academic performance.

The importance of artificial intelligence in neural networks cannot be overstated. Neural networks are complex systems that are difficult for humans to understand. By using artificial intelligence, the neural networks can be trained to recognize patterns and make predictions. Neural networks are a key part of artificial intelligence (AI). Their ability to simulate the workings of the human brain and learn from data makes them well-suited for a number of applications. However, there are still some challenges that need to be addressed in order for neural networks to be truly effective. This research study will examine the role of neural networks in AI, and discuss about some of the challenges that need to be addressed in order to become truly effectivee This research study utilizes MediaPipe to describe the importance of artificial intelligence in neural networks.