Field Of Intelligence Research Articles

Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models

Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. The results show that latent diffusion models combined with vector quantized variational autoencoder outperform other generative augmentation strategies. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects, without the need to acquire costly and time-consuming annotations. The findings of the work will lead to easier, faster, and less expensive modeling of personalized cranial implants. This is beneficial to numerous people suffering from cranial injuries. The work constitutes a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.

Governing with Intelligence: The Impact of Artificial Intelligence on Policy Development

As the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary data and an extensive bibliographic review. By analyzing the existing literature, government reports, and relevant case studies, this study seeks to uncover the opportunities, challenges, and ethical considerations associated with leveraging AI in the formulation and implementation of public policies. This research will delve into the potential benefits of AI-driven policy analysis, such as enhanced decision-making processes, data-driven insights, and improved policy outcomes. Additionally, it will explore the risks and concerns surrounding AI’s influence on policy, including potential biases, privacy implications, and the need for transparency and accountability. The findings of this study will contribute to the ongoing discourse on the responsible and effective integration of AI in public policy development, fostering informed decision-making and promoting the ethical use of this transformative technology.

Exploring the power of KANs: Overcoming MLP limitations in complex data analysis

In the field of artificial intelligence, the requirements of models that can adeptly explore informative features of complex data sets has led to the emergence of advanced neural network architectures. Beyond the perceptron-based architectures that are currently in widespread use, a variety of innovative and cutting-edge designs have been proposed. This paper thoroughly explores the critical role of Kolmogorov-Arnold Networks (KAN) in overcoming the limitations of traditional Multilayer Perceptron (MLP) models, particularly highlighting KANs significant advantages in handling complex nonlinear and high-dimensional data. By analyzing the mathematical foundations and neural network architecture of KAN, and comparing it with MLP, the paper demonstrates KANs outstanding performance in time series analysis and image classification. The research indicates that KAN has distinct advantages in addressing high-dimensional nonlinear data. The paper also summarizes the current research progress on KAN and discusses its enormous potential in future machine learning and real-world applications, pointing out possible future research directions.

Application of Machine Learning in Education: Recent Trends Challenges and Future Perspective

In recent times, Machine learning (ML) is one of the most valuable fields of artificial intelligence (AI) that is transforming education. The application of ML in education provides a promising benefit both to the scientists and researchers and this is the focus of this study. This paper reviews recent trends and advancements of ML in education focusing on areas such as personalisation of learning, predictive analytics, plagiarism detection, intelligent tutoring systems, gamification of learning and recommendation systems. After conducting the literature review we found out the current benefits and challenges of ML in education. The paper also provides insight into the applications and provide the recommendations to address the challenges of ML in the field of education.

Modeling the impact of BDA-AI on sustainable innovation ambidexterity and environmental performance

Data has evolved into one of the principal resources for contemporary businesses. Moreover, corporations have undergone digitalization; consequently, their supply chains generate substantial amounts of data. The theoretical framework of this investigation was built on novel concepts like big data analytics—artificial intelligence (BDA-AI) and supply chain ambidexterity’s (SCA) direct impacts on sustainable supply chain management (SSCM) and indirect impacts on sustainable innovation ambidexterity (SIA) and environmental performance (EP). This study selected employees of manufacturing industries as respondents for environmental performance, sustainable supply chain management, big data analytics, artificial intelligence, and supply chain ambidexterity. The results from this study show that BDA-AI and SCA significantly affect SSCM. SSCM has significant associations with SIA and EP. Finally, SIA has a significant impact on EP. According to the results indicating the indirect impacts, BDA-AI has significant indirect relationships with SIA and EP by having SSCM as the mediating variable. Furthermore, SCA has significant indirect associations with SIA and EP, with SSCM as the mediating variable. Additionally, both BDA-AI and SCA have significant indirect associations with EP, while SIA and SSCM are mediating variables. Finally, SSCM has an indirect association with EP while having SIA as a mediating variable. The findings of this paper provide several theoretical contributions to the research in sustainability and big data analytics artificial intelligence field. Furthermore, based on the suggested framework, this study offers a number of practical implications for decision-makers to improve significantly in the supply chain and BDA-AI. For instance, this paper provides significant insight for logistics and supply chain managers, supporting them in implementing BDA-AI solutions to help SSCM and enhance EP.

Commercial chatbot monitoring: Approaches focused on automated conversation analysis

Purpose of the study: The purpose of this study is to review and analyze current automated techniques for monitoring chatbot conversations in the field of Conversational Artificial Intelligence. It aims to highlight the challenges and limitations of these techniques and provide insights into various metrics used to measure chatbot performance, with the goal of enhancing it. Methodology: The study employs a comprehensive literature review of existing automated techniques for monitoring chatbot conversations. Then, focusing on state-of-the-art approaches, the study introduces a division into numerical metrics (performance statistics and user engagement) and linguistic metrics (conversation analysis). Within conversation analysis, which is crucial for improving chatbot responses and accurately recognizing user intentions, the study identifies and presents three leading methods. Main Findings: The paper highlights that, while current chatbot numerical conversation metrics allow for continuous monitoring and enhancement of chatbot performance, there is still room for improvement in the automated linguistic analysis of chatbot conversations. Furthermore, monitoring chatbot conversations in an automatic way in order to implement adequate corrective actions, is an essential task for refining chatbot efficiency through continuous learning and adaptation. Applications of the study: The findings of this study have practical applications for businesses employing chatbots. By understanding the potential of current automated monitoring techniques and addressing their limitations, commercial chatbot systems can be improved for the benefit of customer satisfaction. Novelty/Originality of the study: The paper provides readers with the novel knowledge necessary to understand key metrics used to measure chatbot conversations from both numerical and linguistic perspectives. It adds value by guiding readers on how monitoring numerical metrics helps analyze chatbot interactions and explains how the automated linguistic analysis of chatbot conversation content is utilized in leading approaches.

Trends in the application of artificial intelligence technologies in the military and technical sphere

The role and significance of artificial intelligence technologies in the military-technical sphere are determined. The principles of using artificial intelligence technologies in the activities of the armed forces are outlined. Attention is focused on the threats and risks posed by the use of artificial intelligence in the military-technical sphere. The peculiarities of the legislative provision of the military use of artificial intelligence technologies in the USA are highlighted. Detailed aspects of the technological implementation of artificial intelligence during the execution of military tasks in the context of the American experience. The conceptual foundations of the russian use of artificial intelligence technologies of a military nature are revealed. The institutional capabilities and achievements of the russian federation in the field of technological support of the needs of the army in the field of artificial intelligence have been determined. The scope and directions of the russian army's innovative developments using artificial intelligence technologies are outlined. It has been updated that unmanned systems are singled out as a special priority for the application of technologies in the field of artificial intelligence of the russian federation. The main global trends in the use of artificial intelligence technologies in the military sphere are revealed. Further directions of improving the field of military use of artificial intelligence technologies have been identified. It was concluded that the development, introduction and approval by the world community of criteria for the responsible use of artificial intelligence for military purposes will contribute to the construction and formation of an international consensus on the responsible handling and use of artificial intelligence technologies.

Hybrid network via key feature fusion for image restoration

In the field of artificial intelligence, combining transformers and convolutional neural networks (CNNs) to improve performance has become a popular solution for various image restoration tasks. However, the hyperparameters related to feature levels are empirical, leading to the inevitable presence of redundant features that hinder effective image restoration. Additionally, the current method of fusing global and local information is simple and direct, failing to fully exploit the potential of hybrid architectures. To address this issue, we propose a key feature fusion hybrid network (KF2H-Net) that reduces redundancy and dynamically fuses key features. On one hand, we create different learnable selection mechanisms within the hybrid network’s various units to choose global key features and local key features, enhancing the depth perception and selection capabilities for different features. On the other hand, through a parameter fusion module for dynamic feature fusion, we refine the multi-feature fusion method to emphasize the more critical features for image restoration. In order to verify the general performance of the proposed KF2H-Net, we specially selected three typical scenarios (underwater, low-light, and haze) for testing. KF2H-Net represents a novel approach to hybrid models addressing practical applications in the field of artificial intelligence. Extensive experiments show that KF2H-Net achieves state-of-the-art performance across different scenarios.

ASPECT: Answer Set rePresentation as vEctor graphiCs in laTex

Abstract Logic programming is a declarative programming paradigm that finds extensive use in the field of Artificial Intelligence (AI). As a result, it has become a valuable tool used in university courses for teaching students AI techniques. Besides Prolog language, the more recent Answer Set Programming (ASP) language turns out to be a powerful tool for developing advanced applications due to the expressiveness of the language and the availability of efficient solving systems. Unfortunately, the output of ASP solvers can be difficult to interpret, since it is a set of atoms, often long and verbose. This is most true in the case of students learning the language or in the case of experts developing applications for complex real-world problems. For these reasons, the ability to produce, when possible, a graphical representation of the solver output becomes useful to ensure easier interpretation of the results. In this paper we present ASPECT, a sub-language of ASP in which the user can directly define, in an intuitive and declarative way, a graphical representation of the answer set. The ASPECT atoms can be converted into the popular LaTeX markup language to produce vector graphics. The documents produced by ASPECT are easy to embed in documents such as scientific articles, course handouts and presentations. Also, the development of user-friendly interfaces is critical for wider use of similar technologies in the industrial sector as well. Moreover, ASPECT is also extended to deal with temporal information, and provide graphical animations of answer sets that enclose the temporal dimension, such as in planning problems. Finally, we advocate the use of ASPECT to create complex and animated presentations starting from a declarative specification.

Study of resistive properties and neural response of ZrO2/TiO2 heterojunction nanowire array (NWA) RRAM

Nowadays, the researcher has an eye on enhancing the performance of resistive random-access memory (RRAM) and exploring new demands and applications, particularly in the field of artificial intelligence and neural computing. It is crucially important for accurately replicating the observed plasticity within synapses to achieve superior RRAM performance in low-dimensional nanomaterials. In this work, we focus on addressing certainly unsatisfactory electrical properties of RRAM based on titanium oxide nanowire array (TiO2 NWA), for example, short retention time (∼103 s). Theoretically and experimentally, a novel and solution-based RRAM structure, namely ZrO2/TiO2(NWA), has been conducted. With the help of the ZrO2 insertion layer, the RRAM device shows impressive resistive switching characteristics, including 200 current–voltage(I-V) sweeps without degradation, increased cycling endurance (>105 cycles), and a long retention time (∼9 × 104 s). These findings hold significant implications for digital computing systems. Moreover, the successful implementation of the proposed device enables the emulation of fundamental synaptic biological features such as non-linear transmission properties, learning experiences behavior, short-term potentiation (STP) and long-term potentiation (LTP). This research provides evidence of the immense potential of the Ag/ZrO2/TiO2(NWA)/FTO RRAM device in bipolar non-volatile memory (NVM) and biomimetic neuromorphic systems.

Attention-aware with stacked embedding for sentiment analysis of student feedback through deep learning techniques.

Automatic polarity prediction is a challenging assessment issue. Even though polarity assessment is a critical topic with many existing applications, it is probably not an easy challenge and faces several difficulties in natural language processing (NLP). Public polling data can give useful information, and polarity assessment or classification of comments on Twitter and Facebook may be an effective approach for gaining a better understanding of user sentiments. Text embedding techniques and models related to the artificial intelligence field and sub-fields with differing and almost accurate parameters are among the approaches available for assessing student comments. Existing state-of-the-art methodologies for sentiment analysis to analyze student responses were discussed in this study endeavor. An innovative hybrid model is proposed that uses ensemble learning-based text embedding, a multi-head attention mechanism, and a combination of deep learning classifiers. The proposed model outperforms the existing state-of-the-art deep learning-based techniques. The proposed model achieves 95% accuracy, 97% recall, having a precision of 95% with an F1-score of 96% demonstrating its effectiveness in sentiment analysis of student feedback.

Preface

Against the backdrop of the rapidly evolving global energy transition, power, grid and energy storage technology are receiving unprecedented attention as key forces in promoting cleaner, smarter and more efficient energy sources. The Proceedings of the 2024 2nd International Conference on Power, Grid and Energy Storage (PGES 2024) brings together the latest research results and cutting-edge explorations of experts and scholars in the field of power energy from all over the world, demonstrating the vigorous vitality and unlimited potential of this field. The PGES 2024 took place virtually in Kunming, China from June 21st to 23rd, 2024. The conference was organized in order to provide an international academic forum for promoting research and other academic activities as well as developing research cooperation in field of power, grid and energy. It is worth mentioning that the number of international participants in this conference hit a record high. Experts and scholars from Canada, Malaysia, Italy, New Zealand, China, and many other countries and regions gathered together to contribute to the development of power, grid and energy. They brought cutting-edge technologies and research results in their respective fields, injecting new vitality and momentum into the conference. The conference fully demonstrated the characteristics of globalized exchanges. Participants from different countries and regions promoted international academic exchanges and cooperation by sharing their research achievements and experiences. At the same time, the conference also invited a number of internationally renowned experts and scholars to give keynote speeches, bringing participants cutting-edge academic views and profound insights. The keynote speakers include Hossam A. Gabbar (full Professor of Ontario Tech University, Canada), Tao Lin (Professor of Wuhan University, China), M A Hannan (Professor of Sunway University, Malaysia), Pierluigi Siano (Full Professor of University of Salerno, Italy), Udaya Madawala (Professor of The University of Auckland, New Zealand), as well as Guobing Zhou (Professor of North China Electric Power University, China). In addition, the conference also organized oral and poster presentations, which provided participants with more opportunities for in-depth exchanges and discussions. These activities not only helped to enhance the friendship and cooperation among the attendees, but also laid a solid foundation for promoting international exchanges and cooperation in the field of information education and artificial intelligence. The topics of papers collected and undergone rigorous peer review in the Proceedings of PGES 2024 can be classified as follows: Power Systems and Automation, Energy Storage Equipment and Systems, Smart Grid Information Engineering, Electrical Power Generation Technology, Power Transmission and Distribution, Communication and Control in The Power System, etc. We would like to express our sincere thanks and gratitude to all the keynote speakers, authors, committee members and other colleagues for the success of PGES 2024, which has given rise to this present volume of selected papers. We would also like to acknowledge the staff of Journal of Physics: Conference Series for their effective work to make this Proceedings published. The Committee of PGES 2024 List of Committee Member is available in this Pdf.

К вопросу об эпистемологии, сознании и интеллекте

This paper is devoted to issues of epistemology, which has recently acquired par­ticular relevance in the national philosophy. The role of cultural-historical episte­mology in the modern theory of knowledge is revealed, where the authors hold the idea that knowing the origin and development of knowledge itself, and its cultural conditioning in specific historical experiences contributes to a better un­derstanding of its significance and the scope of use. The authors present their vi­sion of the problem of transition from gnosiology to epistemology as from a strict prescribed methodology to the choice of one’s ways and methods of con­ducting science, where a significant role is given to such disciplines as computer science and logic. In this context, the relationship between consciousness and in­telligence with the theory of knowledge, in particular with cultural-historical epistemology, is also analyzed, since both the philosophy of consciousness and epistemology seek to understand the nature of knowledge and beliefs based on our conscious experience. This approach, considering the influence of social norms, values and cultural traditions on our cognition and thinking, stimulates the development of the theoretical basis of epistemology and its practical appli­cation. The authors conclude that modern research in the field of consciousness and artificial intelligence and their application in the methodology of epistemo­logy contribute not only to rethinking views on the nature of subjectivity but also to establishing the objectivity of our judgments. Ultimately, this leads to the inte­gration of collective and individual consciousness as an important factor in un­derstanding the world and forming scientific knowledge

ВНЕДРЕНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ПРАКТИКУ ВЫСШЕЙ ШКОЛЫ

The article provides an analysis of domestic and foreign publications on the use of artificial intelligence in higher education. The question of the possibility of using artificial intelligence technologies in higher education is raised. The possibilities and risks associated with the introduction of artificial intelligence into the educational process are noted. Among the main threats to the education system are issues related to the ethical use of technology, academic dishonesty on the part of students, a decrease in their motivation, ability to reason, and the emergence of intellectual laziness. The results of surveys of teachers and students devoted to the study of attitudes towards the possibilities of using new technologies in the learning process are presented. Students tend to have a more positive assessment of the use of artificial intelligence in higher education, while concerns, skepticism and fear are noted on the part of the teaching staff. Teachers assess their knowledge and skills in the field of artificial intelligence as insufficient and are ready to improve their skills in this area. The ambiguous attitude of the university administration to the use of artificial intelligence in writing scientific and diploma projects is shown, from a categorical ban on the use of these technologies to experiments on the best application in the form of competitions with incentives for students.

The internationalization of higher education and labor market transformations study current and future digital capabilities

The fourth industrial revolution that the world is witnessing has constituted a global turning point in which artificial intelligence systems have begun to invade all fields, including the higher education system as a means of investing in human wealth by qualifying for employment. morocco is not immune to these changes, and therefore the problem of the extent to which Moroccan higher education keeps pace can be addressed. to international developments to acquire employability skills imposed by the international labor market to face external competition for positions by asking the problematic question: Does higher education in Morocco prepare the student to keep pace with employment developments in light of a changing world in the direction of internationalization, artificial intelligence, and digitization that has begun to invade the world of work? The aim of this research paper is to study the extent to which Moroccan university students master new global employability skills, to achieve this goal, we adopted the quantitative approach using questionnaire technology, distributing it to a number of students their number is 45 male and female students the research results showed that Moroccan students have partial mastery of international employability skills, and the university has taken steps toward improving education, but it is still at the beginning of the road, and needs to make more efforts in this field. Therefore, it is necessary to integrate these skills into university curricula. with the necessity of disseminating training curricula within universities and public institutions to provide equal opportunities for students while unifying the vision the future of skills required in the labor market. It also supports, encourages and assists students in training in developed and pioneering countries in the field of artificial intelligence, while granting incentive guarantees to return to the homeland, involving the private sector in exchanging experiences with universities by expanding the field training base.

Herbal plant leaves classification for traditional medicine using convolutional neural network

<p>The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.</p>

Threat intelligence named entity recognition techniques based on few-shot learning

In today’s digital and internet era, threat intelligence analysis is of paramount importance to ensure network and information security. Named Entity Recognition (NER) is a fundamental task in natural language processing, aimed at identifying and extracting specific types of named entities from text, such as person names, locations, organization names, dates, times, currencies, and more. The quality of entities determines the effectiveness of upper-layer applications such as knowledge graphs. Recently, there has been a scarcity of training data in the threat intelligence field, and single models suffer from poor generalization ability. To address this, we propose a multi-view learning model, named the Few-shot Threat Intelligence Named Entity Recognition Model (FTM). We enhance the fusion method based on FTM, and further propose the FTM-GRU (Gate Recurrent Unit) model. The FTM model is based on the Tri-training algorithm to collaboratively train three few-shot NER models, leveraging the complementary nature of different model views to enable them to capture more threat intelligence domain knowledge at the coding level.FTM-GRU improves the fusion of multiple views. FTM-GRU uses the improved GRU model structure to control the memory and forgetting of view information, and introduces a relevance calculation unit to avoid redundancy of view information while highlighting important semantic features. We label and construct a few-shot Threat Intelligence Dataset (TID), and experiments on TID as well as the publicly available National Vulnerability Database (NVD) validate the effectiveness of our model for NER in the threat intelligence domain. Experimental results demonstrate that our proposed model achieves better recognition results in the task.

Do Generative Artificial Intelligence Company Strategies of ‘Moving Fast and Breaking Things’ in Civil Society Cancel Their Social Licence to Operate? A Nurse’s Evaluation of Chatbot Impacts

A rapid expansion of the computer technology industry, particularly in the field of artificial intelligence, has ignited a global concern that warrants our immediate action. As nurses, our professional values frameworks compel us to protect public health and address national and global health issues. When industry activities adversely affect the social wellbeing of civil society and social institutions, it is important to evaluate them against their industry’s ‘social license to operate, which is a measure of public trust, credibility, and the legitimacy of their industrial and corporate citizenship status. The central question is, do computer technology companies continue to have a social license to operate in civil society? Nurses are encouraged to evaluate the computer technology industry’s recent ‘generative artificial intelligence’ chatbot activities against its tacit undertaking to be good corporate citizens in return for social acceptance of their operations and behaviour. An evidence-based overview of chatbot impacts on societies, environmental sustainability and human rights provide a basis for evaluation. Basic computer technology terminology and relevant concepts are explained. This article is a direct call to action for clinical nurses and those involved in research, education, management, and policy. We have a duty to critically assess the claims made by chatbot technology vendors in both practice and social contexts. If these vendors integrate chatbot technologies with existing machine learning used in nursing and healthcare technologies it could result in detrimental effects beyond user control. By influencing decisions on technology adoption, we can ensure the implementation of safeguards, protect patient safety and social well-being, and uphold the integrity of nursing values. A closing discussion of impacts of computer industry trust deficits on healthcare and research reflects the author’s concerns and conclusions about the central question. Readers may draw other conclusions and perhaps use the issues and evidence presented here to stimulate further investigations.

Artificial Intelligence: Predicting Perioperative Problems.

The rapidly developing field of artificial intelligence (AI) may soon equip clinicians with algorithms that model and predict perioperative problems with extreme accuracy. Here, we outline emerging AI applications in preoperative risk stratification and intraoperative event prediction, where algorithm performance has been shown to outstrip commonly used conventional risk prediction tools. While offering an enticing view of a novel perioperative practice with superhuman foresight, AI's limited scope and lack of transparency remain key challenges for widespread adoption. As yet it is unclear whether machine learning alone can influence human clinical practice to exert real-world effects on patient outcomes.