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Sequential decision-making is a common type of decision-making problem with sequential and multi-stage characteristics. Among them, the learning and updating of policy are the main challenges in solving sequential decision-making problems. Unlike previous machine autonomy driven by artificial intelligence alone, we improve the control performance of sequential decision-making tasks by combining human intelligence and machine intelligence. Specifically, this paper presents a paradigm of a human-machine traded control systems based on reinforcement learning methods to optimize the solution process of sequential decision problems. By designing the idea of autonomous boundary and credibility assessment, we enable humans and machines at the decision-making level of the systems to collaborate more effectively. And the arbitration in the human-machine traded control systems introduces the Bayesian neural network and the dropout mechanism to consider the uncertainty and security constraints. Finally, experiments involving machine traded control, human traded control were implemented. The preliminary experimental results of the paper show that our traded control method improves decision-making performance and verifies the effectiveness for sequential decision-making problems.

Genomic medicine has made important strides over the past several decades, but as new insights and technologies emerge, the applications of genomics in medicine and planetary health continue to evolve and expand. An important grand challenge is harnessing and making sense of the genomic big data in ways that best serve public and planetary health. Because human health is inextricably intertwined with the health of planetary ecosystems and nonhuman animals, genomic medicine is in need of high throughput bioinformatics analyses to harness and integrate human and ecological multiomics big data. It is in this overarching context that artificial intelligence (AI), particularly machine learning and deep learning, offers enormous potentials to advance genomic medicine in a spirit of One Health. This expert review offers an analysis of the rapidly emerging role of AI in genomic medicine, including its current drivers, levers, opportunities, and challenges. The scope of AI applications in genomic medicine is broad, ranging from efficient and automated data analysis to drug repurposing and precision medicine, as with its challenges such as veracity of the big data that AI sorely depends on, social biases that the AI-driven algorithms can introduce, and how best to incorporate AI with human intelligence. The road ahead for AI in genomic medicine is complex and arduous and yet worthy of cautious optimism as we face future pandemics and ecological crises in the 21st century. Now is a good time to think about the role of AI in genomic medicine and planetary health.

Homo sapiens – ‘man, who knows’ is the name given to humankind due to the knowledge and intelligence that they possess. Science and technology have escalated aspects of this human intelligence to i...

The need for advanced automation and artificial intelligence (AI) in various fields, including text classification, has dramatically increased in the last decade, leaving us critically dependent on their performance and reliability. Yet, as we increasingly rely more on AI applications, their algorithms are becoming more nuanced, more complex, and less understandable precisely at a time we need to understand them better and trust them to perform as expected. Text classification in the medical and cybersecurity domains is a good example of a task where we may wish to keep the human in the loop. Human experts lack the capacity to deal with the high volume and velocity of data that needs to be classified, and ML techniques are often unexplainable and lack the ability to capture the required context needed to make the right decision and take action. We propose a new abstract configuration of Human-Machine Learning (HML) that focuses on reciprocal learning, where the human and the AI are collaborating partners. We employ design-science research (DSR) to learn and design an application of the HML configuration, which incorporates software to support combining human and artificial intelligences. We define the HML configuration by its conceptual components and their function. We then describe the development of a system called Fusion that supports human-machine reciprocal learning. Using two case studies of text classification from the cyber domain, we evaluate Fusion and the proposed HML approach, demonstrating benefits and challenges. Our results show a clear ability of domain experts to improve the ML classification performance over time, while both human and machine, collaboratively, develop their conceptualization, i.e., their knowledge of classification. We generalize our insights from the DSR process as actionable principles for researchers and designers of 'human in the learning loop' systems. We conclude the paper by discussing HML configurations and the challenge of capturing and representing knowledge gained jointly by human and machine, an area we feel has great potential.

Artificial intelligence (AI) makes use of technology to accomplish tasks that, in the past, might have been only done using human intelligence. AI is being used in healthcare, finance manufacturing, transport, education, and energy. First‐generation AI capable computers engaged in chess games and found solutions to puzzles and carried out other comparatively forthright roles. AI machine learning uses conventions to reconsider the model, re‐examine the data, and perhaps make a decision without interference from a human being. Deep learning is made feasible using artificial neural networks that mimic neurons and brain cells. Artificial neural networks were motivated by things that can be found in everyday biology. Commercial uses of AI are increasing in progressed and developing economies. AI has the potential to turn up the growth of GDP in progressed markets and those that are emerging. The rapid development of AI is changing multiple industries and redesigning the rules of strategy.

In this paper, we propose a brain–computer interface (BCI)-based Human-in-the-Loop (Hit-Loop) agent for human and artificial intelligence (AI) colearning in music listening and appreciation with an Artificial Intelligence of Things (AIoT) application. The novel BCI-based Hit-Loop agent contains human intelligence with BCI-based AIoT-Fuzzy Markup Language (FML) and BCI-FML agents, as well as machine intelligence with AI-FML Hit-Loop and AIoT-FML agents. We used FML to facilitate communication between humans and the AI-FML robots through an AIoT-FML Learning Tool (AIoT-FML-LT), which was the core technology of the AI-FML Hit-Loop agent for the BCI-based music listening and appreciation application. Furthermore, the novel AIoT-FML-LT in conjunction with the BCI-FML and AIoT-FML agents was developed and presented for music listening and student learning. Moreover, the BCI-based AIoT-FML and AI-FML Hit-Loop agents were applied in English language learning, and the AIoT-FML-LT assisted in measuring student learning performance in Taiwan and Japan. The human-like high-level knowledge base and rule base were constructed by various domain experts, as well as the personalized electroencephalography (EEG) and student English learning data sets collected using the BCI device and AIoT-FML-LT, respectively, and were applied to machine learning models such as deep learning and particle swarm optimization. Additionally, the AIoT-FML-LT was connected to the AIoT-FML Hit-Loop agent for human and robot colearning. The relationship between human perceptions and the AIoT-FML Hit-Loop agent in terms of eyes, ears, nose, tongue, body, and brain corresponding to sights, sounds, smells, tastes, objects of touch, and mind are discussed. Finally, the students learned human language and AI language by using the AI-FML robots and AIoT-FML-LT together with the human English learning and AI-FML machine learning models, respectively. The experimental results reveal that the BCI-based Hit-Loop agent for human and AI-FML robot colearning in conjunction with AIoT applications can effectively facilitate music listening and appreciation application as well as English listening in Taiwan and Japan. The learning behavior and performance of the students also improved after incorporation of the human and robot colearning model.

Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning. However, previous methods require human observer to give inputs explicitly (e.g., press buttons, voice interface), burdening the human in the loop of RL agent’s learning process. Further, providing explicit human advise (feedback) continuously is not always possible or too restrictive, e.g., autonomous driving, disabled rehabilitation, etc. In this work, we investigate capturing human’s intrinsic reactions as implicit (and natural) feedback through EEG in the form of error-related potentials (ErrP), providing a natural and direct way for humans to improve the RL agent learning. As such, the human intelligence can be integrated via implicit feedback with RL algorithms to accelerate the learning of RL agent. We develop three reasonably complex 2D discrete navigational games to experimentally evaluate the overall performance of the proposed work. And the motivation of using ErrPs as feedbacks is also verified by subjective experiments. Major contributions of our work are as follows, (i) we propose and experimentally validate the zero-shot learning of ErrPs, where the ErrPs can be learned for one game, and transferred to other unseen games, (ii) we propose a novel RL framework for integrating implicit human feedbacks via ErrPs with RL agent, improving the label efficiency and robustness to human mistakes, and (iii) compared to prior works, we scale the application of ErrPs to reasonably complex environments, and demonstrate the significance of our approach for accelerated learning through real user experiments.

Artificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning. The convergence of technological advances in those fields allows to integrate massive data and build probabilistic models of a problem. The latter can be continuously updated by incorporating new data to inform decision-making and predict the future. In support of drug discovery and development, AI allows to generate disease models using data obtained following extensive molecular profiling of patients. Given its superior computational power, AI can integrate those big multimodal data to generate models allowing: (i) to represent patient heterogeneity; and (ii) identify therapeutic targets with inferences of causality in the pathophysiology. Additional computational analyses can help identifying and optimizing drugs interacting with these targets, or even repurposing existing molecules for a new indication. AI-based modeling further supports the identification of biomarkers of efficacy, the selection of appropriate combination therapies and the design of innovative clinical studies with virtual placebo groups. The convergence of biotechnologies, drug sciences and AI is fostering the emergence of a computational precision medicine predicted to yield therapies or preventive measures precisely tailored to patient characteristics in terms of their physiology, disease features and environmental risk exposure.

Coronavirus disease 2019 (COVID-19) is an infectious disease with acute intense respiratory syndrome which spread around the world for the very first time impacting the way of life with drastic uncertainty. It rapidly reached almost every nook and corner of the world and the World Health Organization (WHO) has announced COVID-19 as a pandemic. The health care institutions around the globe are looking for viable and real-time technological solutions to handle the virus for evading its spread and circumvent probable demises. Importantly, the artificial intelligence tools and techniques are playing a major role in fighting the effect of virus on the economic jolt by mimicking human intelligence by screening, analyzing, predicting and tracking the existing and likely future patients. Since the first reported case, all the government organizations in the world jumped into action to prevent it and many studies reported the role of AI in taking decisions analyzing big data available in public sphere. Thereby, this review focuses on identifying the significant implication of AI techniques used for the COVID-19 disease management in the public sphere by agglomerating the latest available information. It also discusses the pitfalls and future directions in handling sensitive big data required for advanced neural networks.

Digitization is developing fast and has become a powerful tool for digital planning, construction and operations, for instance digital twins. Now is the right time for constructive approaches and to apply ethics-by-design in order to develop and implement a safe and efficient artificial intelligence (AI) application. So far, no study has addressed the key research question: Where can corporate digital responsibility (CDR) be allocated, and how shall an adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Therefore, the research on how best practices meet their corporate responsibility in the digital transformation process and the requirements of the EU for trustworthy AI and its human-friendly use is essential. Its transformation bears a high potential for companies, is critical for success and thus, requires responsible handling. This study generates data by conducting case studies and interviewing experts as part of the qualitative method to win profound insights into applied practice. It provides an assessment of demands stated in the Sustainable Development Goals by the United Nations (SDGs), White Papers on AI by international institutions, European Commission and German Government requesting the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of AI in construction engineering from an ethical perspective. This research critically evaluates opportunities and risks concerning CDR in construction industry. To the author’s knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to digitization and AI, to mitigate digital transformation both in large, medium- and small-sized companies. This study applies a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation and examine benefits as well as risks of AI. Furthermore, the goal is to define ethical principles which are key for success, resource-cost-time efficiency and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. This study concludes that innovative corporate organizations starting new business models are more likely to succeed than those dominated by a more conservative, traditional attitude.

Image object classification and detection are two important basic problems in the study of computer vision. Image classification is always a challenging task for computer scientist. Classification is a well-known supervised learning technique. This is always used to extract meaningful and vital information from a large dataset. It can also be effectively used for predicting unknown classes. At present image classification accuracy is not high enough because of large number of redundant information as well as features. Primary focus should be on how human intelligence works on image classification rather than training the machine for the image classification. In this research paper a theoretical and numerical analysis of human intelligence is outlined as how human intelligence works on an image through which features and in what way other they are deciding the category of image they have perceived.

There is little research on how consumers decide whether they want to use algorithmic advice or not. In this research, we show that consumers’ lay beliefs about artificial intelligence (AI) serve as a heuristic cue to evaluate accuracy of algorithmic advice in different professional service domains. Three studies provide robust evidence that consumers who believe that AI is higher than human intelligence are more likely to adopt algorithmic advice. We also demonstrate that lay beliefs about AI only influence adoption of algorithmic advice when a decision task is perceived to be complex.

Artificial intelligence (AI) is expected to bring greater efficiency in radiology by performing tasks that would otherwise require human intelligence, also at a much faster rate than human performance. In recent years, milestone deep learning models with unprecedented low error rates and high computational efficiency have shown remarkable performance for lesion detection, classification, and segmentation tasks. However, the growing field of AI has significant implications for radiology that are not limited to visual tasks. These are essential applications for optimizing imaging workflows and improving noninterpretive tasks. This article offers an overview of the recent literature on AI, focusing on the musculoskeletal imaging chain, including initial patient scheduling, optimized protocoling, magnetic resonance imaging reconstruction, image enhancement, medical image-to-image translation, and AI-aided image interpretation. The substantial developments of advanced algorithms, the emergence of massive quantities of medical data, and the interest of researchers and clinicians reveal the potential for the growing applications of AI to augment the day-to-day efficiency of musculoskeletal radiologists.

Advanced technologies now threaten to surpass human intelligence. This watershed will radically impact the ways we currently play, view, and even understand sport. Artificial intelligence(AI) is progressing rapidly, and it remains a matter of time until the impact on sport is fully realized. Currently in sport, AI is a tool used to assist humans with performance monitoring, supporter and media engagement, and injury prediction, among other functions. This commentary argues that advanced technologies can provide unfair playing advantages resulting in unintended and negative consequences. We assert that now is the time to discuss exactly who is in control of regulating the use of these, potentially game-changing innovations in sport.

Knowledge-intensive crowdsourcing (KIC) is becoming one of the most promising domains of crowdsourcing by leveraging human intelligence and building a large labor-intensive service network. In this network, the service providers (SPs) constitute the backbone of the KIC platform and play an important role in connecting the platform and service requesters. The SPs are a group of distributed crowds with a complex composition and high level of uncertainty, resulting in great challenges in service quality and platform management. Understanding the SPs’ competency is an effective way for the platform to manage them. Therefore, we attempt to connect the competency analysis to the environment of KIC to investigate and identify the criteria of SPs’ competency (i.e., the competency factors and dimensions required for being competent for the SPs’ business). To this end, we leverage the Latent Dirichlet Allocation (LDA) model to explore and extract hidden competency dimensions from online interview records. We then introduce the competency theory to identify and label the competency factors and dimensions and construct the three-level KSAT competency model, which presents a comprehensive vision of the SPs’ performance standards in the context of KIC. Given the competency criteria in the KSAT competency model, we use the Best-Worst Method (BWM) to determine their weights, which reflect their importance when evaluating the SPs’ competency from the platforms’ perspective. The results show that skill and knowledge are the two most important competency factors, and customer relationship management and communication ability are the two most valuable competency dimensions when evaluating the SPs’ competency. Furthermore, the KSAT competency model can be applied to analyze the competency of individuals or organizations in many other industries as well.

Management of solid organ recipients requires a significant amount of research and observation throughout the recipient’s life. This is associated with accumulation of large amounts of information that requires structuring and subsequent analysis. Information technologies such as machine learning, neural networks and other artificial intelligence tools make it possible to analyze the so-called ‘big data’. Machine learning technologies are based on the concept of a machine that mimics human intelligence and and makes it possible to identify patterns that are inaccessible to traditional methods. There are still few examples of the use of artificial intelligence programs in transplantology. However, their number has increased markedly in recent years. A review of modern literature on the use of artificial intelligence systems in transplantology is presented.

Combination of Internet of things (IoT), Nano-Technology, Augmented Reality (AR) and Artificial Intelligence (AI) will be the future. Lots of research and development is happening to develop the IoT based application and few of them have already developed. Home automation, self-drive cars, simulators are few examples of IoT based applications. Still, there is scope for more research and development to develop very advanced and intelligent applications, which can eliminate human intervention or provide intelligent reporting. This paper reviewed some of selected articles related to the future beyond human intelligence using technology. Current development in this sector is achievable. We sent astronauts to space for studying life on other planets and earth's movement. In the future, without sending astronauts to space, data should be available with intelligence reporting. Home automation is easily available these days, but it still requires human intervention to on and off the switches using mobile app or calls. In the future, without human intervention, it should be workable as well, we may not have to worry about odd times of water supply in housing societies and close the tap once the water tank is full, etc. I am going to focus on such a future developments through this paper. Combination of Human Intelligence and Technology will definitely be the future to come up with a solution.

Logic reasoning is a significant ability of human intelligence and also an important task in artificial intelligence. The existing logic reasoning methods, quite often, need to design some reasoning patterns beforehand. This has led to an interesting question: can logic reasoning patterns be directly learned from given data? The problem is termed as a data concept logic. In this study, a learning logic task from images, called a LiLi task, first is proposed. This task is to learn and reason the logic relation from images, without presetting any reasoning patterns. As a preliminary exploration, we design six LiLi data sets (Bitwise And, Bitwise Or, Bitwise Xor, Addition, Subtraction and Multiplication), in which each image is embedded with a n-digit number. It is worth noting that a learning model beforehand does not know the meaning of the n-digit numbers embedded in images and the relation between the input images and the output image. In order to tackle the task, in this work we use many typical neural network models and produce fruitful results. However, these models have the poor performances on the difficult logic task. For furthermore addressing this task, a novel network framework called a divide and conquer model by adding some label information is designed, achieving a high testing accuracy.

BACKGROUND AND OBJECTIVES Convolutional neural networks (CNN) enable accurate diagnosis of medical images and perform on or above the level of individual physicians. Recently, collective human intelligence (CoHI) was shown to exceed the diagnostic accuracy of individuals. Thus, diagnostic performance of CoHI (120 dermatologists) versus individual dermatologists versus two state-of-the-art CNN was investigated. PATIENTS AND METHODS Cross-sectional reader study with presentation of 30 clinical cases to 120 dermatologists. Six diagnoses were offered and votes collected via remote voting devices (quizzbox®, Quizzbox Solutions GmbH, Stuttgart, Germany). Dermatoscopic images were classified by a binary and multiclass CNN (FotoFinder Systems GmbH, Bad Birnbach, Germany). Three sets of diagnostic classifications were scored against ground truth: (1) CoHI, (2) individual dermatologists, and (3) CNN. RESULTS CoHI attained a significantly higher accuracy [95 % confidence interval] (80.0 % [62.7 %-90.5 %]) than individual dermatologists (75.7 % [73.8 %-77.5 %]) and CNN (70.0 % [52.1 %-83.3 %]; all P

The article examines some particular aspects of the analytical process within the intelligence cycle, having as reference the framework of strategic intelligence. Starting from a proposed model of analysis of the competing hypotheses using phases-tailored tools, which will improve the quality of all-source intelligence analysis and its final products, we further assess its applicability in HUMINT (Human Intelligence) analysis. The model of intelligence analysis as a problem-solving method, with a focus on predictive analysis, will serve to understand the expectations from single-source collection disciplines (in our case, HUMINT) data gathering and reporting, connected to the roles of HUMINT analysts in the specialized branches.

Social sustainability aims to improve the quality of life for all segments of society in the present and the future and to provide comprehensive care by individuals and institutions in society for the benefit of society as a whole. And it requires that members of society as a whole reach a stage where they accept the differences between them and consider them as a place of strength and distinction for all. The interest in the human mind and its capabilities and methods of development is an embodiment of the interest in human wealth for the advancement of generations. Humanities scholars have been interested in measuring the human mind and searching for its nature and concepts of intelligence. Studies and scientific theories have proven that there is no single pattern of human intelligence, but rather that it is multiple and different for every person. individual alone . The types of multiple intelligences, represented in the innate biological abilities that can be observed in learners vary greatly, so the intelligent educational environment is the one that does not provide a single method for all learners with different characteristics. The study sheds light on the pattern of bodily-kinesthetic intelligence in children and how to prepare the environment of the internal educational space for these children so that they can be motivated to learn more effectively through the elements of the internal architecture that make up the educational spaces for them that correspond to their tendencies and interests, to employ their different creative abilities.

While examining the historical backdrop of clinical procedure, we can understand that medical practitioner have/had created and refined instruments for complicated surgeries. Development in clinical progressions is on a standard with that saw in the sickness causing specialists and infections. Since ancient time, clinical practices were performed using obtrusive medical procedures without sedation, which resulted in high mortality and post-surgery complications. This led to the emergence of effective, safe and user friendly medical instruments and procedures with little to moderately death rate. At present obtrusive methodologies are negligibly practiced, but provides less twisted related complexities, fast organ work return, and more limited hospitalizations. The success of these methods has prompted for higher acknowledgment of picture guided surgeries. We present an Internet Of things (IOT) and Artificial Intelligence (AI) based model that includes a computer generated experience based (VR-based) User interface and some benefits and limitations. It can be done by Raspberry pi, android application and also done by Sap cloud system.

Autor analizuje metodologiczną specyfikę takich dziedzin nauki jak kryminalistyka, kryptografia, archeologia czy program SETI, wskazując, że wbrew twierdzeniom zwolenników teorii inteligentnego projektu, którzy utrzymują, iż stosują analogiczną metodologię, teoria ta istotnie się od nich różni na płaszczyźnie metodologicznej i nie posiada znamion naukowości.

Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.

This article deals with the development of “Smart” technology, which is being developed on the basis of financial content management. Nowadays the evolution of smart technologies is impossible without the Internet and human intelligence. These two phenomena have become interconnected and formed a new definition, which was given the term “content management”. As the world began to undergo social transformation in the digital age, finance has become an integral part of it. A completely new definition of “financial content management” has appeared, which was not previously known to mankind. In the period of global digitalization society needs the development characteristic of today. In the world of finance, this is accompanied by processes that have received the term “smart technologies”. These smart technologies create new processes that are combined with the development of society, and also have a significant impact on finance as a separate industry. The article formulates the main idea of the future development of financial content management and the relationship of smart technologies with finance. It has been proven that smart technologies are an integral part of the development of the future society, and “financial content management” is a target component of finance.

La inteligencia humana es un rasgo poligénico (~1000 genes) con una influencia de cada gen aproximadamente ascendente al 0,1%. Es un atributo indispensable para el desarrollo personal, familiar, social y económico y tiene, además, una relación directamente proporcional al mantenimiento de la salud y a una mayor esperanza de vida. La discapacidad intelectual, consecuentemente, afecta todas estas áreas y constituye un problema de salud pública en varios países de Latinoamérica en los que exhibe una prevalencia mayor al 10%. La etiología de la discapacidad intelectual sea aislada o sindrómica, es genética hasta en un 85% de los casos; se diagnostica mediante las nuevas tecnologías de búsqueda en el genoma, tales como la secuenciación masiva y el análisis cromosómico por micromatrices. El diagnóstico etiológico de la discapacidad intelectual permite la selección de terapias específicas, la determinación del pronóstico y de riesgos de recurrencia familiar e individual.

The COVID-19 pandemic has affected many aspects of our lives, including education. Due to this unexpected catastrophe, education has shifted to virtual-learning and auto-grading models in most parts of the world. This study explores the validity and appropriateness of auto-grading-assessment for online exams by comparing students’ online exam scores where they are first auto-graded and then manually graded. Furthermore, it investigates whether the mean differences in their scores are statistically significant. The study included two calculus courses taught by the authors, during the spring semester 2019-2020 at a private university in Saudi Arabia. The online exam was performed on the WebAssign platform, which has built-in calculus questions. The sample consisted of fifty-five students who were registered on those calculus courses. The quantitative data was analysed using the SPSS statistical tool. A paired t-test at an alpha level of 0.05 was performed on differences in mean exam scores between auto-graded and manually-graded scores. The statistical analysis results revealed a statistically significant difference in students' mean scores. Our findings illustrate the importance of human intelligence, its role in assessing students' achievements and understanding of mathematical concepts, and the extent to which instructors can currently rely on auto-grading. A careful manual investigation of auto-graded exams revealed different types of mistakes committed by students. Those mistakes were characterized into two categories: non-mathematical mistakes (related to Platform Design) and minor mathematical mistakes, which might deserve partial credit. The study indicated a need to reform the auto-grading system and provided some suggestions to overcome its setbacks. https://doi.org/10.26803/ijlter.20.3.16

Artificial Intelligence (AI) is a broad discipline that uses powerful algorithms to emulate important aspects of human intelligence. Provided by the Industry 4.0 revolution, AI is increasingly appl...

I am fascinated by how rich and flexible human intelligence is. From a quick glance at the scenes in Figure 1A, we effortlessly recognize the 3D geometry and texture of the objects within, reason about how they support each other, and when they move, track and predict their trajectories. Stacking blocks, picking up fruits---we also plan and interact with scenes and objects in many ways.

Law enforcement agencies in the UK are embracing evidence-based policing and recognise the importance of human source intelligence (HUMINT) in the decision-making process. A review of the literature identified six categories likely to impact the handling of a covert human intelligence source (CHIS) or an informant: (a) handler personality traits; (b) informant motivation; (c) rapport; (d) gaining cooperation; (e) obtaining information, and (f) detecting deception. This study sought to identify which of these categories current HUMINT practitioners considered the most when planning and conducting a meeting with an informant. A bespoke online survey was designed and disseminated to 34 practitioners using purposive and snowball sampling. Directed content analysis and thematic content analysis were conducted. Results indicate that practitioners appear most concerned with gaining co-operation (d) and detecting deception (f). Results also found an inter-connectivity between the six categories, with informant handlers often having to balance competing requirements. Implications for future research are discussed.

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

The unprecedented development of the Internet of Things (IoT), artificial intelligence (AI), and Big Data has stimulated a boom of social networks such as Twitter, WeChat, Facebook, etc., generating a huge amount of social data that are worth further analysis. Social computing has an important focus on mining the deep relationships between social organizations, networks, and media. The increasing volumes and complexities make big social data mining more and more difficult. Hybrid Human–Artificial Intelligence (H-AI) is an approach combining both human intelligence and AI, so as to handle demanding problems in a harmonious way. By adopting H-AI in social computing, it would provide more possibilities for social data analysis, relationship discovery, outlier detection, and prediction, and is proving to be an emerging and promising direction for AI and big data research.

With the widespread use of online social networks, billions of pieces of information are generated every day. How to detect new topics quickly and accurately at such data scale plays a vital role in information recommendation and public opinion control. One of the basic research tasks of topic detection is how to represent a topic. The existing topic representation models do not focus on how to select better differentiated words to represent topics, are still computer-centered, and do not effectively combine human intelligence and artificial intelligence (AI). To solve these problems, this article proposes a word-distributed sensitive topic representation model (WDS-LDA) based on hybrid human-AI (H-AI). The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is a common word of all topics, and cannot be used for the purpose of distinguishing among topics, becoming less suitable to represent any topic. At the same time, the human cognitive ability and cognitive models are introduced into topic representation based on H-AI. We introduce the user's modification of topic expression words into the topic model representation so that the topic model can learn human wisdom and become more and more accurate. Therefore, three different weights are introduced: inside weight; outside weight; and manual adjustment weight. The inside weight describes the uniform distribution of a word in the given topic, the outside weight describes the uniform distribution of a word in all topics, and the manual adjustment weight reflects whether a word is suitable as a representative vocabulary in the past manual adjustment. Tests using Sina microblog's actual data sets show that the WDS-LDA algorithm makes the representative words more important, the distinction among different topic words higher, and effectively improves the precision of subsequent algorithms, such as topic detection and topic evolutionary analysis using the topic model. This work was supported in part by NSFC under Grant 61972106, Grant U1636215, Grant 61871140, Grant 61572153, and Grant 61872420, in part by the National Key research and Development Plan under Grant 2019QY1406 and Grant 2018YFB0803504, in part by the Guangdong Province Key Research and Development Plan under Grant 2019B010136003 and Grant 2019B010137004

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI’s ability to perform tasks that would normally require human intelligence varies from prediction to complex decision-making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effective to avoid socially undesirable equilibrium due to the following features of competitive crowdsourcing: in the presence of imperfect monitoring, heterogeneous workers with competing interest tend to beat their opponents for larger self-profit, and the fact that they can freely and frequently change their opponents makes the situation much more complicated. Taking these features into consideration, this article proposes a mechanism design problem to enforce cooperation and extort selfish works simultaneously, with the objective of maximizing the requester’s utility. To solve the problem, we integrate binary ratings with differential pricing to develop a novel rating protocol. By establishing a mathematical model for the problem and quantifying necessary and sufficient conditions for a sustainable social norm, we provide design guidelines for optimal rating protocols and design a low-complexity algorithm to select optimal design parameters. Finally, extensive evaluation results demonstrate the performance of our proposed rating protocol and reveal how intrinsic parameters impact on design parameters.

The ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions. We compare their behavior to two state-of-the-art algorithms for multi-task reinforcement learning, one that maps previous policies and encountered features to new reward functions and one that approximates value functions across tasks, as well as to standard model-based and model-free algorithms. Across three exploratory experiments and a large preregistered experiment, our results provide strong evidence for a strategy that maps previously learned policies to novel scenarios. These results enrich our understanding of human reinforcement learning in complex environments with changing task demands.

Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior.

Recent global trends have brought questions of intellectual property (IP) theft to the fore. The fear is that stolen IP, whether gained through hacking, human intelligence or legal means, could thr...

Artificial intelligence (AI) is nowadays an upcoming technology. It is a practice of simulating human intelligence for varied applications. When compared with the standard practices, AI is developi...

This paper concluded that parallel storage similar to the human brain must have the following characteristics: 1) The connection mode of its information storage is scattered and parallel. 2) The operating pattern of this system must be similar to that of the nervous system for memory, facilitation, and recall. Because of this similarity, we can “borrow” some concepts from neuroscience to describe Advanced intelligent basic programming the attributes of parallel storage systems. 3) The excitation mode of this system must be selective excitation. 4) Memory interference is one of the most important characteristics of parallel storage system. We can’t eliminate recall interference completely, but there are a number of steps can take to keep within the limits of our tolerance. 5) Memory interference requires only excitatory memory columns in the contact area. 6) The “memory excitation” mode of the parallel storage system for information must be the fuzzy “memory excitation”, which is inevitable of memory interference. Whereafter, This paper describes and discusses how and why to program on the theoretical basis of the first part. Includes an introduction to functions that enable memory, recall, stimulation, excitement. And the relationship between memory, memory, fuzzy excitement, excitement, reward and punishment, attention distribution and their significance in solving memory interference.

This paper presents a vision for a much-needed paradigm shift in Life Cycle Engineering (LCE), which is termed Adaptive and Intelligent Life Cycle Engineering (AI-LCE). To do so, interdisciplinary analysis of literature in domains of AI and LCE is performed. Needed concepts and methods are described: key enabling technologies are Artificial Intelligence (AI), the Internet of Things, and data lakes, which are becoming cost-effective and increasingly implemented in industry. Both artificial and human intelligence are used in combination. Its advantages compared with the conventional LCE include shorter time for changing activities in the lifecycle and the accuracy of changes made.

The article describes the author’s proposal on cognitive architecture for the development of a general-level artificial intelligent agent («strong» artificial intelligence). New principles for the development of such an architecture are proposed — a hybrid approach in artificial intelligence and bionics. The architecture diagram of the proposed solution is given and descriptions of possible areas of application are described. Strong artificial intelligence is a technical solution that can solve arbitrary cognitive tasks available to humans (human-level artificial intelligence) and even surpass the capabilities of human intelligence (artificial superintelligence). The fields of application of strong artificial intelligence are limitless — from solving current problems facing the human to completely new problems that are not yet available to human civilization or are still waiting for their discoverer. The novelty of the work lies in the author’s approach to the construction of cognitive architecture, which has absorbed the results of many years of research in the field of artificial intelligence and the results of the analysis of cognitive architectures of other researchers.

Augmented intelligence (AuI) integrates human intelligence (HI) and artificial intelligence (AI) to harness their strengths and mitigate their weaknesses. The combination of HI and AI has seen to improve both human and machine capabilities, and achieve a better performance compared to separate HI and AI approaches. In this paper, we present a survey of literature to understand how AuI has been applied in the literature, including the roles of HI and AI, AI approaches, features, and applications. Due to the limited literature related to this topic, we also present a survey of expert opinion to answer four main questions to understand the experts’ implications of AuI, including: a) the definition of AuI and the significance of HI in AuI; b) the roles of HI in AuI; c) the current and future applications of AuI in research, industry, and public, as well as the advantages and shortcomings of AuI; and d) end users’ view of the application of AuI. We also present recommendations to improve AuI, and provide a comparison between the findings from the surveys of both literature and expert opinion. The discussion of this paper shows the promising potential of AuI compared to separate HI and AI approaches.

Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were developed using baseline (eg, preoperative) data to evaluate AKI risk. Newer models which incorporated baseline as well as evolving data collected during a hospital admission have shown improved predicative abilities. In this review, we will summarize the advances made in AKI risk prediction over the last several years, including a shift toward more dynamic, real-time, electronic medical record-based models. In addition, we will be discussing the role of electronic AKI alerts and decision support tools. Recent studies have demonstrated improved patient outcomes through the use of these tools which monitor for nephrotoxin medication exposures as well as provide kidney focused care bundles for patients at high risk for severe AKI. Finally, we will briefly discuss the pitfalls and implications of implementing these scores, alerts, and support tools.

Artificial intelligence-driven cryptocurrencies are cryptocurrencies created by Artificial intelligence using the traditional human cryptocurrency development framework without human intervention. An AI explores the data from each different stream and arriving at the framework which can host these cryptocurrencies following the standards of legality. Cryptography is the encryption of specific data to conceal it and keep it a secret from unwanted third parties. Cryptocurrencies are encrypted currencies with unique keys as developed by developers. Artificial intelligence is an advanced machine programmed to simulate and emulate human intelligence by carrying tasks and reaching conclusions with little or no human intervention. This work considered the use of AI through machine learning and deep learning in the development of cryptocurrencies. The AI machine will set all the parameters and structure of the cryptocurrency. This will include how data is added, removed, and verified on the stream. Blockchain is an open ledger of a cryptocurrency's transactions. It stores files in the system, arranged in blocks, and connected on a list called chains. The article considers how AI-driven cryptocurrency will run using the blockchain network and its impact on it. Artificial intelligence and cryptocurrency are technological very essential technological development currently. The effect of the combination of both technologies would be enormous in the future as both technologies will develop each other remarkably.

Purpose This paper aims to present an exploration of possible associations between the Jungian archetypes frequently used in marketing and three well-known products based on artificial intelligence (AI), namely, Sophia, Alexa and Articoolo. Design/methodology/approach The study conducted emotionalist interviews to gather thick data from 11 participants on how they conceptualize these AI-based products. In the absence of any existing relevant hypotheses, this paper attempts to build theory using a case study approach and qualitative analysis of interview narratives. Findings Despite the human attributes ascribed to these products, participants were principally concerned with their purpose, efficiency and the degree of trust which they felt could be accorded to the product. Anthropomorphism emerged as significant with participants making some associations with common archetypes traditionally exploited in marketing and this suggests a possible means of enhancing consumer trust in AI products. Originality/value Little research has been conducted on the marketing of AI and this study presents a timely identification of some potentially significant issues. As AI is intended to mimic some aspects of human intelligence, the role of the archetype in creating a personality to enhance trust may prove crucial in securing consumer confidence.

Artificial intelligence (AI) is a sort of new technical science which can simulate, extend and expand human intelligence by developing theories, methods and application systems. In the last five years, the application of AI in medical research has become a hot topic in modern science and technology. Gynecological malignant tumors involves a wide range of knowledge, and AI can play an important part in these aspects, such as medical image recognition, auxiliary diagnosis, drug research and development, treatment scheme formulation and other fields. The purpose of this paper is to describe the progress of AI in gynecological malignant tumors and discuss some problems in its application. It is believed that AI improves the efficiency of diagnosis, reduces the burden of clinicians, and improves the effect of treatment and prognosis. AI will play an irreplaceable role in the field of gynecological malignant oncology and will promote the development of medicine and further promote the transformation from traditional medicine to precision medicine and preventive medicine. However, there are also some problems in the application of AI in gynecologic malignant tumors. For example, AI, inseparable from human participation, still needs to be more "humanized", and needs to further protect patients' privacy and health, improve legal and insurance protection, and further improve according to local ethnic conditions and national conditions. However, it is believed that with the continuous development of AI, especially ensemble classifier, and deep learning will have a profound influence on the future of medical technology, which is a powerful driving force for future medical innovation and reform.