From Brain Science to Artificial Intelligence

Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.     

A Survey of Accelerator Architectures for Deep Neural Networks

Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and stand-alone technical solution. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs.     

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbot-psychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.     

人工智能产品与服务体系研究综述

目的在新一代人工智能发展背景下,分析并明确人工智能产品及其服务体系的特征与价值,指出未来发展趋势,为相关设计、技术与应用研究提供参考。方法从人工智能的概念出发,给出人工智能产品及其服务体系的定义;收集并分析典型的人工智能产品和相关研究,总结整理人工智能产品的关键特征和支撑技术;探索人工智能产品的典型服务场景,对相关研究现状进行综述;基于前文分析对未来发展趋势及挑战进行预测。结论指明了人工智能产品具有情境感知、自适应学习、自主决策、主动交互与协同的典型特征;描绘了以数据和计算能力为基础、算法为核心、多种底层技术与通用技术为支持的场景应用的人工智能产品支撑技术框架;分析了人工智能产品的服务体系在不同场景中可以被赋予的价值;预测了由技术驱动向设计驱动转化、由单品视角向服务体系视角转变的未来发展趋势。     

Contribution of neuroimaging in the diagnosis of brain disorders: Recent findings and future applications

Brain disorders including neurological and psychiatric disorders have become a major global public health issue due to their high prevalence and burden. However, elucidating ultimate causes and improving strategies for diagnosis and treatment of these disorders remain challenging due to limited accessibility to living human brain. In addition, there has been a great need for robust biomarkers for them at a very early stage. Neuroimaging technologies have demonstrated the potential in investigating the pathophysiology and developing the biomarkers. The current review discusses the potential diagnostic applications of neuroimaging in brain disorders. We summarized major findings in recent neuroimaging meta‐analyses, which could be used as future biomarkers, in Alzheimer's disease, Parkinson's disease, central nervous system inflammation, depression, and schizophrenia. New possibilities of novel imaging techniques were also demonstrated. Finally, future directions for the imaging‐based diagnosis were suggested. In spite of promising results from preliminary studies and rapid technological advances, further studies on the reliability and validity of potential imaging biomarkers in larger patient populations and the development of new guidelines for the clinical applications would be required.     

Drivers,barriers and social considerations for AI adoption in business and management: A tertiary study

The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews.The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business and management.The methodology used for this tertiary study is based on Kitchenham and Charter's guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary studies.These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decision-support, systems management and technology adoption).While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’perspectives.Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors of the AI adoption.In addition to increased focus on social implications of AI, the reviews are recommending more rigorous evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and evaluation.Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors such as financial industry and retail.     

Artificial Intelligence in Healthcare: Review and Prediction Case Studies

Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.     

Blockchain breeding grounds: Asia's advance over the USA and Europe

Blockchain is argued to disrupt almost every industry and research field. Among other technologies, it contributes to the next fourth industrial revolution. While prominent technologies, such as AI and IoT, have already proven their potential and value in many applications, Blockchain applications have gathered only limited appeal. We ask, which countries drive inventive activity in Blockchain and to which magnitude and type. Using worldwide Blockchain patent applications from 2009 to 2020 as inventive outcome indicators the National Breeding Ground (NBG) index and the International Breeding Ground (IBG) index are calculated. The NBG index is related to the interest of a country to file Blockchain patents in its domestic market. The IBG index is related to the interest of a country to file and exploit Blockchain patents in foreign markets. We observe China and the USA as strong national and international breeding grounds. In particular, Asian countries drive Blockchain inventions. European countries contribute marginally to the Blockchain patent landscape and, together with the USA, are losing ground to Asia. Comparisons to prior work on Blockchain patenting and research patterns partially reflect our results. However, comparisons to AI suggest different patenting and exploitation strategies for Blockchain and AI in China.     

Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation

Artificial intelligence (AI) technologies and their fields of application are among the most debated developments of recent times. Although being widely discussed academically, publicly and in policy debates, certain aspects of their research, development and application are completely ignored, namely the impact AI has on animals. Animals are affected by the research on and development of this technology since it partially relies on animal testing. In addition, AI is also being applied to improve monitoring and marketing of animals in an agricultural context. We argue that it is insufficient to exclude these aspects from debates around AI. In addition to the surveillance-applications on animals, which can be evaluated as impacting them negatively, AI applications, from which individual animals can benefit, do exist. These can primarily be found in nature and wildlife conservation, as we point out at the end of the paper. By providing an overview on how these technologies are applied to animals and how this affects them, this paper aims to fill a previously existing research gap.     

Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review

Artificial intelligence (AI) researchers created new techniques, developed and applied them to solve engineering problems since two decades. Although lots of AI techniques and approaches are available in mechanical engineering, there isn’t any survey aiming to review the existing works, systems and applications in the field of fracture mechanics. In this paper, the state of the art of five AI methods which are used in the field of fracture mechanics, is surveyed. This review is performed from the technical point of view on particular applications of artificial neural networks, Bayesian networks, genetic algorithms, fuzzy logic and case-based reasoning.After an overview of AI methods, sub-domains of engineering fracture mechanics with respect to the fault and failure analysis are described. The existing works from 1990 to 2016 are analysed and discussed in four categories as sub-domains of fracture mechanics: (a) failure mode and failure mechanism identification, (b) damage and failure detection and diagnosis, (c) fault and error detection, diagnosis and (d) mechanical fracture and fracture parameters. We analyse literature based on a classification of these five AI methods in order to highlight their main concepts and explain how they are applied in these sub-domains of fracture mechanics. Our analysis and discussion in this paper shows the advantages, limitations and research gaps in this field. Finally, perspectives and future research directions are outlined.     

Fluorescent Diagnostic Probes in Neurodegenerative Diseases

Neurodegenerative diseases are debilitating disorders that feature progressive and selective loss of function or structure of anatomically or physiologically associated neuronal systems. Both chronic and acute neurodegenerative diseases are associated with high morbidity and mortality along with the death of neurons in different areas of the brain; moreover, there are few or no effective curative therapy options for treating these disorders. There is an urgent need to diagnose neurodegenerative disease as early as possible, and to distinguish between different disorders with overlapping symptoms that will help to decide the best clinical treatment. Recently, in neurodegenerative disease research, fluorescent-probe-mediated biomarker visualization techniques have been gaining increasing attention for the early diagnosis of neurodegenerative diseases. A survey of fluorescent probes for sensing and imaging biomarkers of neurodegenerative diseases is provided. These imaging probes are categorized based on the different potential biomarkers of various neurodegenerative diseases, and their advantages and disadvantages are discussed. Guides to develop new sensing strategies, recognition mechanisms, as well as the ideal features to further improve neurodegenerative disease fluorescence imaging are also explored.     

Unraveling In Vivo Brain Transport of Protein‐Coated Fluorescent Nanodiamonds

Nanotheranostics, combining diagnostics and therapy, has the potential to revolutionize treatment of neurological disorders. But one of the major obstacles for treating central nervous system diseases is the blood–brain barrier (BBB) preventing systemic delivery of drugs and optical probes into the brain. To overcome these limitations, nanodiamonds (NDs) are investigated in this study as they are a powerful sensing and imaging platform for various biological applications and possess outstanding stable far‐red fluorescence, do not photobleach, and are highly biocompatible. Herein, fluorescent NDs encapsulated by a customized human serum albumin–based biopolymer (polyethylene glycol) coating (dcHSA‐PEG) are taken up by target brain cells. In vitro BBB models reveal transcytosis and an additional direct cell–cell transport via tunneling nanotubes. Systemic application of dcHSA‐NDs confirms their ability to cross the BBB in a mouse model. Tracking of dcHSA‐NDs is possible at the single cell level and reveals their uptake into neurons and astrocytes in vivo. This study shows for the first time systemic NDs brain delivery and suggests transport mechanisms across the BBB and direct cell–cell transport. Fluorescent NDs are envisioned as traceable transporters for in vivo brain imaging, sensing, and drug delivery.     

Artificial intelligence adoption in the physical sciences,natural sciences,life sciences,social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960–2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI “springs” and “winters”. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.     

Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory

With the rapid development of artificial intelligence (AI), AI anxiety has emerged and is receiving widespread attention, but research on this topic is not comprehensive. Therefore, we investigated the dimensions of AI anxiety using the theoretical model of integrated fear acquisition and a questionnaire survey. A total of 494 valid questionnaires were recovered. Through a first-order confirmatory factor analysis (CFA), a factor model of AI anxiety was constructed, and eight factors of AI anxiety were verified. Then, a second-order CFA was applied to verify the adaptation of the factor structure of AI anxiety to fear acquisition. We identified four dimensions of AI anxiety and proposed a theory of AI anxiety acquisition that illustrates four pathways of AI anxiety acquisition. Each pathway includes two factors that cause AI anxiety. We conclude by analyzing the limitations of current AI anxiety research and proposing a broader research agenda for AI anxiety.     

Internet of Cultural Things: Current Research,Challenges and Opportunities

Driven by the visions of the Internet of Things (IoT), Artificial Intelligence (AI), and 5G communications, the Internet of Cultural Things (IoCT) realize the comprehensive interconnection among cultural products, cultural services, cultural resources, and cultural platforms, bringing individuals with richer humanistic experience, increasing economic benefits for the cultural sector, and promoting the development of cultural heritage protection and education. At present, IoCT has received widespread attention in both industry and academia. To explore new research opportunities and assist users in constructing suitable IoCT systems for specific applications, this survey provides a comprehensive overview of the IoCT components and key technologies. A comparison study of representative IoCT systems is presented according to their applicability. A general platform architecture of IoCT is proposed to link cultural objects with the internet and human. Finally, open issues for research challenges and future opportunities of IoCT are also studied in this paper.     

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.     

Overcoming blood–brain barrier transport: Advances in nanoparticle-based drug delivery strategies

The blood–brain barrier (BBB), a unique structure in the central nervous system (CNS), protects the brain from bloodborne pathogens by its excellent barrier properties. Nevertheless, this barrier limits therapeutic efficacy and becomes one of the biggest challenges in new drug development for neurodegenerative disease and brain cancer. Recent breakthroughs in nanotechnology have resulted in various nanoparticles (NPs) as drug carriers to cross the BBB by different methods. This review presents the current understanding of advanced NP-mediated non-invasive drug delivery for the treatment of neurological disorders. Herein, the complex compositions and special characteristics of BBB are elucidated exhaustively. Moreover, versatile drug nanocarriers with their recent applications and their pathways on different drug delivery strategies to overcome the formidable BBB obstacle are briefly discussed. In terms of significance, this paper provides a general understanding of how various properties of nanoparticles aid in drug delivery through BBB and usher the development of novel nanotechnology-based nanomaterials for cerebral disease therapies.     

Nanoparticulate strategies for the treatment of polyglutamine diseases by halting the protein aggregation process†

Polyglutamine (polyQ) diseases are a class of neurodegenerative disorders that cause cellular dysfunction and, eventually, neuronal death in specific regions of the brain. Neurodegeneration is linked to the misfolding and aggregation of expanded polyQ-containing proteins, and their inhibition is one of major therapeutic strategies used commonly. However, successful treatment has been limited to date because of the intrinsic properties of therapeutic agents (poor water solubility, low bioavailability, poor pharmacokinetic properties), and difficulty in crossing physiological barriers, including the blood–brain barrier (BBB). In order to solve these problems, nanoparticulate systems with dimensions of 1–1000?nm able to incorporate small and macromolecules with therapeutic value, to protect and deliver them directly to the brain, have recently been developed, but their use for targeting polyQ disease-mediated protein misfolding and aggregation remains scarce. This review provides an update of the polyQ protein aggregation process and the development of therapeutic strategies for halting it. The main features that a nanoparticulate system should possess in order to enhance brain delivery are discussed, as well as the different types of materials utilized to produce them. The final part of this review focuses on the potential application of nanoparticulate system strategies to improve the specific and efficient delivery of therapeutic agents to the brain for the treatment of polyQ diseases.     

Ethical Principles and Governance Technology Development of AI in China

Ethics and governance are vital to the healthy and sustainable development of artificial intelligence (AI). With the long-term goal of keeping AI beneficial to human society, governments, research organizations, and companies in China have published ethical guidelines and principles for AI, and have launched projects to develop AI governance technologies. This paper presents a survey of these efforts and highlights the preliminary outcomes in China. It also describes the major research challenges in AI governance research and discusses future research directions.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.