Hasil untuk "artificial intelligence"

S. Makridakis

Matej Moravcík, Martin Schmid, Neil Burch et al.

Computer code based on continual problem re-solving beats human professional poker players at a two-player variant of poker. Artificial intelligence masters poker Computers can beat humans at games as complex as chess or go. In these and similar games, both players have access to the same information, as displayed on the board. Although computers have the ultimate poker face, it has been tricky to teach them to be good at poker, where players cannot see their opponents' cards. Moravčík et al. built a code dubbed DeepStack that managed to beat professional poker players at a two-player poker variant called heads-up no-limit Texas hold'em. Instead of devising its strategy beforehand, DeepStack recalculated it at each step, taking into account the current state of the game. The principles behind DeepStack may enable advances in solving real-world problems that involve information asymmetry. Science, this issue p. 508 Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker, the quintessential game of imperfect information, is a long-standing challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect-information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated, with statistical significance, professional poker players in heads-up no-limit Texas hold’em. The approach is theoretically sound and is shown to produce strategies that are more difficult to exploit than prior approaches.

W. Samek, K. Müller

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today’s ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered “black boxes”, not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.

Baobao Zhang, Allan Dafoe

This report presents a broad look at the American public’s attitudes toward artificial intelligence (AI) and AI governance, based on findings from a nationally representative survey of 2,000 American adults. As the study of the public opinion toward AI is relatively new, we aimed for breadth over depth, with our questions touching on: workplace automation; attitudes regarding international cooperation; the public’s trust in various actors to develop and regulate AI; views about the importance and likely impact of different AI governance challenges; and historical and cross-national trends in public opinion regarding AI. Our results provide preliminary insights into the character of US public opinion regarding AI.

Lin Zhao, Tianjiao Dai, Zhi Qiao et al.

Abstract Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality. The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system. These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters. Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment. In this study, we examine the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment. Furthermore, we present a systematic review of four aspects of the application of artificial intelligence to wastewater treatment: technology, economy, management, and wastewater reuse. Finally, we provide perspectives on the potential future directions of new research frontiers in the utilization of artificial intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications.

R. Confalonieri, Ludovik Çoba, Benedikt Wagner et al.

Explainability in Artificial Intelligence (AI) has been revived as a topic of active research by the need of conveying safety and trust to users in the “how” and “why” of automated decision‐making in different applications such as autonomous driving, medical diagnosis, or banking and finance. While explainability in AI has recently received significant attention, the origins of this line of work go back several decades to when AI systems were mainly developed as (knowledge‐based) expert systems. Since then, the definition, understanding, and implementation of explainability have been picked up in several lines of research work, namely, expert systems, machine learning, recommender systems, and in approaches to neural‐symbolic learning and reasoning, mostly happening during different periods of AI history. In this article, we present a historical perspective of Explainable Artificial Intelligence. We discuss how explainability was mainly conceived in the past, how it is understood in the present and, how it might be understood in the future. We conclude the article by proposing criteria for explanations that we believe will play a crucial role in the development of human‐understandable explainable systems.

Philipp Schmidt, F. Biessmann, Timm Teubner

ABSTRACT Assistive technology featuring artificial intelligence (AI) to support human decision-making has become ubiquitous. Assistive AI achieves accuracy comparable to or even surpassing that of human experts. However, often the adoption of assistive AI systems is limited by a lack of trust of humans into an AI’s prediction. This is why the AI research community has been focusing on rendering AI decisions more transparent by providing explanations of an AIs decision. To what extent these explanations really help to foster trust into an AI system remains an open question. In this paper, we report the results of a behavioural experiment in which subjects were able to draw on the support of an ML-based decision support tool for text classification. We experimentally varied the information subjects received and show that transparency can actually have a negative impact on trust. We discuss implications for decision makers employing assistive AI technology.

Hideyuki Shimizu, K. Nakayama

Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade.

Scott Thiebes, S. Lins, A. Sunyaev

Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

Ragnar Fjelland

The modern project of creating human-like artificial intelligence (AI) started after World War II, when it was discovered that electronic computers are not just number-crunching machines, but can also manipulate symbols. It is possible to pursue this goal without assuming that machine intelligence is identical to human intelligence. This is known as weak AI. However, many AI researcher have pursued the aim of developing artificial intelligence that is in principle identical to human intelligence, called strong AI. Weak AI is less ambitious than strong AI, and therefore less controversial. However, there are important controversies related to weak AI as well. This paper focuses on the distinction between artificial general intelligence (AGI) and artificial narrow intelligence (ANI). Although AGI may be classified as weak AI, it is close to strong AI because one chief characteristics of human intelligence is its generality. Although AGI is less ambitious than strong AI, there were critics almost from the very beginning. One of the leading critics was the philosopher Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus’ main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. However, today one might argue that new approaches to artificial intelligence research have made his arguments obsolete. Deep learning and Big Data are among the latest approaches, and advocates argue that they will be able to realize AGI. A closer look reveals that although development of artificial intelligence for specific purposes (ANI) has been impressive, we have not come much closer to developing artificial general intelligence (AGI). The article further argues that this is in principle impossible, and it revives Hubert Dreyfus’ argument that computers are not in the world.

Pavitra Dhamija, Surajit Bag

Purpose“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.Design/methodology/approachAs rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).FindingsResults obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.Originality/valueThe result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

C. Sindermann, Peng Sha, Min Zhou et al.

In the context of (digital) human–machine interaction, people are increasingly dealing with artificial intelligence in everyday life. Through this, we observe humans who embrace technological advances with a positive attitude. Others, however, are particularly sceptical and claim to foresee substantial problems arising from such uses of technology. The aim of the present study was to introduce a short measure to assess the Attitude Towards Artificial Intelligence (ATAI scale) in the German, Chinese, and English languages. Participants from Germany (N = 461; 345 females), China (N = 413; 145 females), and the UK (N = 84; 65 females) completed the ATAI scale, for which the factorial structure was tested and compared between the samples. Participants from Germany and China were additionally asked about their willingness to interact with/use self-driving cars, Siri, Alexa, the social robot Pepper, and the humanoid robot Erica, which are representatives of popular artificial intelligence products. The results showed that the five-item ATAI scale comprises two negatively associated factors assessing (1) acceptance and (2) fear of artificial intelligence. The factor structure was found to be similar across the German, Chinese, and UK samples. Additionally, the ATAI scale was validated, as the items on the willingness to use specific artificial intelligence products were positively associated with the ATAI Acceptance scale and negatively with the ATAI Fear scale, in both the German and Chinese samples. In conclusion we introduce a short, reliable, and valid measure on the attitude towards artificial intelligence in German, Chinese, and English language.

E. Huynh, A. Hosny, C. Guthier et al.

P. Phillips, Carina A. Hahn, Peter C. Fontana et al.

We introduce four principles for explainable artificial intelligence (AI) that comprise fundamental properties for explainable AI systems. We propose that explainable AI systems deliver accompanying evidence or reasons for outcomes and processes; provide explanations that are understandable to individual users; provide explanations that correctly reflect the system’s process for generating the output; and that a system only operates under conditions for which it was designed and when it reaches sufficient confidence in its output. We have termed these four principles as explanation, meaningful, explanation accuracy, and knowledge limits, respectively. Through significant stakeholder engagement, these four principles were developed to encompass the multidisciplinary nature of explainable AI, including the fields of computer science, engineering, and psychology. Because one-size-fits-all explanations do not exist, different users will require different types of explanations. We present five categories of explanation and summarize theories of explainable AI. We give an overview of the algorithms in the field that cover the major classes of explainable algorithms. As a baseline comparison, we assess how well explanations provided by people follow our four principles. This assessment provides insights to the challenges of designing explainable AI systems.

M. Woschank, E. Rauch, Helmut E. Zsifkovits

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

S. Ali, B. Choi

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.

S. Kulkarni, N. Seneviratne, Mirza Shaheer Baig et al.

Artificial intelligence in medicine has made dramatic progress in recent years. However, much of this progress is seemingly scattered, lacking a cohesive structure for the discerning observer. In this article, we will provide an up-to-date review of artificial intelligence in medicine, with a specific focus on its application to radiology, pathology, ophthalmology, and dermatology. We will discuss a range of selected papers that illustrate the potential uses of artificial intelligence in a technologically advanced future.

Xieling Chen, Haoran Xie, Gwo-jen Hwang

Abstract With the rapid development of artificial intelligence (AI) technologies and a continuously growing interest in their application in educational contexts, there has been significant growth in the scientific literature in relation to the application of AI in education (AIEd). This study aims to present multiple perspectives on the development of AIEd in terms of relevant grants, conferences, journals, software tools, article trends, top issues, institutions, and researchers to provide an overview of AIEd for its further development and implementation. With this study, we contribute to the research field by enabling educators and scholars to understand the status and development of relevant grants and publications concerning AIEd. Also, findings concerning active actors can help educators and scholars identify the active researchers and institutions in the research on AIEd. Furthermore, researchers and educators are able to identify relevant journals and be more aware of major issues in AIEd studies. In addition, we also highlight the significance and necessity of the launch of the new Elsevier journal AIEd-related journal named Computers & Education: Artificial Intelligence.

Hossein Hassani, E. Silva, Stephane Unger et al.

Artificial intelligence (AI) is a rapidly growing technological phenomenon that all industries wish to exploit to benefit from efficiency gains and cost reductions. At the macrolevel, AI appears to be capable of replacing humans by undertaking intelligent tasks that were once limited to the human mind. However, another school of thought suggests that instead of being a replacement for the human mind, AI can be used for intelligence augmentation (IA). Accordingly, our research seeks to address these different views, their implications, and potential risks in an age of increased artificial awareness. We show that the ultimate goal of humankind is to achieve IA through the exploitation of AI. Moreover, we articulate the urgent need for ethical frameworks that define how AI should be used to trigger the next level of IA.

R. Abdullah, Bahjat Fakieh

Background The advancement of health care information technology and the emergence of artificial intelligence has yielded tools to improve the quality of various health care processes. Few studies have investigated employee perceptions of artificial intelligence implementation in Saudi Arabia and the Arabian world. In addition, limited studies investigated the effect of employee knowledge and job title on the perception of artificial intelligence implementation in the workplace. Objective The aim of this study was to explore health care employee perceptions and attitudes toward the implementation of artificial intelligence technologies in health care institutions in Saudi Arabia. Methods An online questionnaire was published, and responses were collected from 250 employees, including doctors, nurses, and technicians at 4 of the largest hospitals in Riyadh, Saudi Arabia. Results The results of this study showed that 3.11 of 4 respondents feared artificial intelligence would replace employees and had a general lack of knowledge regarding artificial intelligence. In addition, most respondents were unaware of the advantages and most common challenges to artificial intelligence applications in the health sector, indicating a need for training. The results also showed that technicians were the most frequently impacted by artificial intelligence applications due to the nature of their jobs, which do not require much direct human interaction. Conclusions The Saudi health care sector presents an advantageous market potential that should be attractive to researchers and developers of artificial intelligence solutions.