Progress In Artificial Intelligence Research Articles

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID-19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real-time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next-generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever-increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

Application Status and Prospects of Artificial Intelligence in Peptic Ulcers.

Peptic ulcer (PU) is a common and frequently occurring disease. Although PU seriously threatens the lives and health of global residents, the applications of artificial intelligence (AI) have strongly promoted diversification and modernization in the diagnosis and treatment of PU. This minireview elaborates on the research progress of AI in the field of PU, from PU’s pathogenic factor Helicobacter pylori (Hp) infection, diagnosis and differential diagnosis, to its management and complications (bleeding, obstruction, perforation and canceration). Finally, the challenges and prospects of AI application in PU are prospected and expounded. With the in-depth understanding of modern medical technology, AI remains a promising option in the management of PU patients and plays a more indispensable role. How to realize the robustness, versatility and diversity of multifunctional AI systems in PU and conduct multicenter prospective clinical research as soon as possible are the top priorities in the future.

Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments.

One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.

Intake monitoring in free-living conditions: Overview and lessons we have learned

The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior. These allow the study of eating behavior outside the laboratory in free-living conditions, without the need for video recordings and laborious manual annotations. In this paper, we present a high-level overview of our recent work on intake monitoring using a smartwatch, as well as methods using an in-ear microphone. We also present evaluation results of these methods in challenging, real-world datasets. Furthermore, we discuss use-cases of such intake monitoring tools for advancing research in eating behavior, for improving dietary monitoring, as well as for developing evidence-based health policies. Our goal is to inform researchers and users of intake monitoring methods regarding (i) the development of new methods based on commercially available devices, (ii) what to expect in terms of effectiveness, and (iii) how these methods can be used in research as well as in practical applications.

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment.

The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.

Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review.

Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging. We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability. The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions. The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future. Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.

Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review.

The physiological and neuroregulatory mechanism of propofol is largely based on very limited knowledge. It is one of the important puzzling issues in anesthesiology and is of great value in both scientific and clinical fields. It is acknowledged that neural networks which are comprised of a number of neural circuits might be involved in the anesthetic mechanism. However, the mechanism of this hypothesis needs to be further elucidated. With the progress of artificial intelligence, it is more likely to solve this problem through using artificial neural networks to perform temporal waveform data analysis and to construct biophysical computational models. This review focuses on current knowledge regarding the anesthetic mechanism of propofol, an intravenous general anesthetic, by constructing biophysical computational models.

A Golden Decade of Deep Learning: Computing Systems & Applications

Abstract The past decade has seen tremendous progress in the field of artificial intelligence thanks to the resurgence of neural networks through deep learning. This has helped improve the ability for computers to see, hear, and understand the world around them, leading to dramatic advances in the application of AI to many fields of science and other areas of human endeavor. In this essay, I examine the reasons for this progress, including the confluence of progress in computing hardware designed to accelerate machine learning and the emergence of open-source software frameworks to dramatically expand the set of people who can use machine learning effectively. I also present a broad overview of some of the areas in which machine learning has been applied over the past decade. Finally, I sketch out some likely directions from which further progress in artificial intelligence will come.

Digging Deeper Into the Stories [Editor’s Remarks

Deep learning has been key to remarkable progress in artificial intelligence. Recently, I came across some intriguing applications of deep learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Salvator Mundi</i> is the most expensive painting ever sold in the world; however, it is still in debate whether the painting should be attributed to Leonardo da Vinci in whole, in part, or not at all. To solve this puzzle, an attorney developed a convolutional neural network that can analyze the likelihood of a painting being drawn by the supposed artist. The results showed that most of the crucial parts of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Salvator Mundi</i> were likely done by da Vinci. Paleontology is also benefiting from deep learning. Paleontologists commonly use CT scans to identify and compare fossils, but the segmentation of CT images is often subjective and time-consuming. Researchers thus came up with the idea of constructing neural networks to enable objective and efficient segmentation of dinosaur fossil scans. It is astonishing how digging deeper into the stories through deep learning can lead to such improvement to research.

ARTS: autonomous research topic selection system using word embeddings and network analysis

The materials science research process has become increasingly autonomous due to the remarkable progress in artificial intelligence. However, autonomous research topic selection (ARTS) has not yet been fully explored due to the difficulty of estimating its promise and the lack of previous research. This paper introduces an ARTS system that autonomously selects potential research topics that are likely to reveal new scientific facts yet have not been the subject of much previous research by analyzing vast numbers of articles. Potential research topics are selected by analyzing the difference between two research concept networks constructed from research information in articles: one that represents the promise of research topics and is constructed from word embeddings, and one that represents known facts and past research activities and is constructed from statistical information on the appearance patterns of research concepts. The ARTS system is also equipped with functions to search and visualize information about selected research topics to assist in the final determination of a research topic by a scientist. We developed the ARTS system using approximately 100 00 articles published in the Computational Materials Science journal. The results of our evaluation demonstrated that research topics studied after 2016 could be generated autonomously from an analysis of the articles published before 2015. This suggests that potential research topics can be effectively selected by using the ARTS system.

Complex Scene Segmentation Network Based on Multi-scale Encoding-decoding Architecture

Abstract With the progress of artificial intelligence, the study of scene segmentation for complex scene understanding is of great significance. Due to the large number of activities, there are many target categories, large scale changes, many mutual occlusions, difficult target recognition, and large data labeling costs. In order to achieve accurate understanding of the complex scene, this paper proposes to add a scale adaptive feature module on the basis of Encode-Decode, so that the network can make good use of the features and context information of each level to effectively adapt to changes in target size. At the same time, we use the scale size function to weight encode different levels of features, which improves the prediction accuracy of pixels in the intersection area of each class. Experiments conducted on Cityscapes, Put_campus and PASCAL VOC 2012 datasets show that the method in this article is improved by about 1% compared with the three segmentation algorithms such as FCN, PSPNet, and Deeplabv3 +.

Impact of AI on employment in manufacturing industry

Artificial intelligence (AI) is the most significant technological revolution since we entered the 21st century. It has become a new focus of public attention and international competition. Industrial integration with AI technology not only brings vast opportunities for transformation and upgrading of enterprises but also has an impact on employment structure. Focusing on the fusion of the manufacturing industry integrating AI, we analyze the integration progress of AI and segmented manufacturing industries, describe a supply-and-demand situation of labor market with different skills, and discuss the impact of AI technology on manufacturing employment theoretically. Then we construct the propensity score matching–difference-in-difference model, divide intelligent manufacturing enterprises into various categories, and inspect the influences on the employment structure of different segmented manufacturing enterprises before and after integrating AI technology. Finally, we put forward efficient methods of transformation and upgrading of manufacturing enterprises and practical suggestions to solve problems on employment structure.

AI-Based Secure Construction of University Information Services Platform

The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university’s secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively.

Artificial Intelligence for Education, Proctoring, and Credentialing in Cardiovascular Medicine.

Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of cardiovascular medicine. This review discusses the past, present, and future of artificial intelligence in education, remote proctoring, credentialing, research, and publication as they pertain to cardiovascular procedures. This review describes the benefits and limitations of artificial intelligence and machine learning and the exciting potential of integrating advanced simulation, holography, virtual reality, and extended reality into disease diagnosis and patient care, as well as their roles in cardiovascular research and education. Nonetheless, much of the available data resides in electronic medical records or within industry-sponsored proprietary programs that are not compatible or standardized for current clinical application. Many areas in cardiovascular medicine would benefit from the introduction or increased use of artificial intelligence. Web-based artificial intelligence applications could be used to address unmet needs for education, on-demand procedural proctoring, credentialing, and recredentialing for interventionists and physicians in remote locations. Further progress in artificial intelligence will require further collaboration among computer scientists and researchers in order to identify and correct the most relevant problems and to implement the best data-based approach to achieving this goal. The future success of artificial intelligence in cardiovascular medicine will depend on the degree of collaboration between all pertinent experts in this field. This will undoubtedly be a prolonged, stepwise process.

“I’m Afraid I Can’t Do That, Dave”; Getting to Know Your Buddies in a Human–Agent Team

The rapid progress in artificial intelligence enables technology to more and more become a partner of humans in a team, rather than being a tool. Even more than in human teams, partners of human–agent teams have different strengths and weaknesses, and they must acknowledge and utilize their respective capabilities. Coordinated team collaboration can be accomplished by smartly designing the interactions within human–agent teams. Such designs are called Team Design Patterns (TDPs). We investigated the effects of a specific TDP on proactive task reassignment. This TDP supports team members to dynamically allocate tasks by utilizing their knowledge about the task demands and about the capabilities of team members. In a pilot study, agent–agent teams were used to study the effectiveness of proactive task reassignment. Results showed that this TDP improves a team’s performance, provided that partners have accurate knowledge representations of each member’s skill level. The main study of this paper addresses the effects of task reassignments in a human–agent team. It was hypothesized that when agents provide explanations when issuing and responding to task reassignment requests, this will enhance the quality of the human’s mental model. Results confirmed that participants developed more accurate mental models when agent-partners provide explanations. This did not result in a higher performance of the human–agent team, however. The study contributes to our understanding of designing effective collaboration in human–agent teams.

Sequential multi‐headed attention for entity‐based relational neural networks

AbstractThe capacity of relational interaction between high‐level information and reason is the defining characteristic of human intelligence. Regardless of the remarkable progress in artificial intelligence, recent machine reading comprehension models still heavily rely on high‐dimensional word‐based distributed representations. Since these models employ statistical means to answer questions of complex textual corpus and employ an accuracy‐based metric system, their learning capacity of the required skills is not guaranteed. To ensure the capacity of MRC models to learn the desired skills, explainability has become an emerging requirement. In this paper, we propose an end‐to‐end natural language reasoning model that is based on sets of high‐level aggregated representations which promote operational explainability. To this end, sequential multi‐head attention, and a loss regularization function is proposed. We show analysis of the proposed approach on two natural language reasoning oriented question and answering datasets (bAbI and NewsQA).

Guest Editors’ Introduction: Special Issue on Autonomous Systems Design

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fueled by the</b> progress of artificial intelligence, autonomous systems become more and more integral parts of many Internet-of-Things (IoT) and cyber–physical systems (CPSs) applications, such as automated driving, robotics, avionics, and industrial automation. Autonomous systems are self-governed and self-adaptive systems that are designed to operate in an open and evolving environment that has not been completely defined at design time. However, this does not come for free. The advance from established systems technology to autonomous systems is not an issue of developing and introducing new functions in existing design processes. We firmly believe, and will elaborate, that it will have a deep impact on engineering, on the way we design, verify, test, and operate systems. We must find ways to keep high engineering standards, such as dependability and controllability, when allowing the system to change its behavior in the field. In other words, the future of autonomous systems is not only an issue of systems intelligence, but depends on our capability to design them responsibly.

Business Resilience System Integrated Artificial Intelligence System

By definition, “Business Resilience” is the ability for an organization to quickly adapt to an unexpected disruption(s) and prevent any ongoing workflow(s) to come to a halt and yet maintaining continuous business operations and safeguarding people, resources, assets, and overall barns equity. By the same talking, a Business Resilience System (BRS) is a combination of intelligent software and hardware combined in an integrated system. Such an integrated combination of Business Resilience System goes a step beyond Disaster Recovery (DR) by offering post-disaster strategies to avoid costly downtime, shore up vulnerability and maintain business operations in the face of additional, unexpected breaches of the daily operation of workflow in any enterprise or organization. With recent technical progress in Artificial Intelligence (AI) augmented with Machine Learning (ML) and Deep Learning sub-systems, they present an Artificial Intelligence System (AIS) and now integrating these two systems of BRS and AIS, one can offer the most intelligent system that an organization or an enterprise can own, in order to have the best possible solution in place to have the best possible technique of predication and consequently prevention and adverse events based on collective historical data within Deep Learning of Artificial Intelligence. In this paper we are present and introduce each of these systems i.e., BRS and AIS and how they can be beneficial to each other by their integration as a holistic system along with their sub-stems of Software, Hardware, Machine Learning and Deep Learning.

The application of artificial intelligence in nuclear cardiology.

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated a lot of interest in medical imaging research including nuclear cardiology. AI has a potential to reduce cost, save time and improve image acquisition, interpretation, and decision-making. This review summarizes recent researches and potential applications of AI in nuclear cardiology and discusses the pitfall of AI.

China Is Capable of Building an Ideal Type of Society

The four-decades long exchanges with Chinese scholars has provided me with a fresh perspective. Reading history makes me feel that the talents of the historical Chinese who created the country’s glorious ancient civilization are still being carried on today, and have become the foundation enabling China to build an ideal type of society. Indeed, China regards “becoming developed” as its ultimate goal and “reform and growth” as its guideline. The progress of artificial intelligence would not remove its own weakness of democracy. On the contrary, as the technology grows mature, it might steer development of socialism in a favourable direction.