Hasil untuk "artificial intelligence"

Jing Pei, Lei Deng, Sen Song et al.

Maud Chassignol, A. Khoroshavin, A. Klimova et al.

Abstract Digital technologies have already become an internal part of our life. They change the way we are looking for information, how we communicate with each other, even how we behave. This transformation applies to many areas, including education. The main objective of this article is to identify prospective impact of artificial technologies to the study process and to predict possible changes in educational landscape. In presented literature review we considered four categories: customized educational content, innovative teaching methods, technology enhanced assessment, communication between student and lecturer. Having reviewed publications on the subject we present here a possible picture of how the Artificial Intelligence (AI) will reshape education landscape.

U. Schmidt-Erfurth, A. Sadeghipour, Bianca S. Gerendas et al.

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.

V. Dunjko, H. Briegel

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research—quantum information versus machine learning (ML) and artificial intelligence (AI)—have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our ‘big data’ world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement—exploring what ML/AI can do for quantum physics and vice versa—researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.

M. Hutson

Corinne Cath

This paper is the introduction to the special issue entitled: ‘Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

T. Rajaee, H. Ebrahimi, Vahid Nourani

Abstract This study is a review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting, and presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years, and the reviewing and comparison of the obtained results. Accordingly, 67 journal papers published from 2001 to 2018 were reviewed in the terms of the features and abilities of the modeling approaches, input data consideration, prediction time steps, data division, etc. From the reviewed papers it can be concluded that despite some weaknesses, if the AI methods properly be developed, they can successfully be used to simulate and forecast the GWL time series in different aquifers. Since some of the stages of the AI modeling are based on the experience or trial-and-error procedures, it is useful to review them in the special application on GWL modeling. Many partial and general results were achieved from the reviewed papers, which can provide applicable guidelines for researchers who want to perform similar works in this field. Several new ideas in the related area of research are also presented in this study for developing innovative methods and for improving the quality of the modeling.

Pei Wang

Abstract This article systematically analyzes the problem of defining “artificial intelligence.” It starts by pointing out that a definition influences the path of the research, then establishes four criteria of a good working definition of a notion: being similar to its common usage, drawing a sharp boundary, leading to fruitful research, and as simple as possible. According to these criteria, the representative definitions in the field are analyzed. A new definition is proposed, according to it intelligence means “adaptation with insufficient knowledge and resources.” The implications of this definition are discussed, and it is compared with the other definitions. It is claimed that this definition sheds light on the solution of many existing problems and sets a sound foundation for the field.

Raquel Dias, A. Torkamani

Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

Mark Coeckelbergh

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence (AI) technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency.

Arnaud De Bruyn, Vijay Viswanathan, Y. Beh et al.

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.

Nathalie A. Smuha

A continuous journey towards an appropriate governance framework for AI As part of its European strategy for Artificial Intelligence (AI), and as a response to the increasing ethical questions raised by this technology, the European Commission established an independent High-Level Expert Group on Artificial Intelligence (AI HLEG) in June 2018. The group was tasked to draft two deliver-ables: AI Ethics Guidelines and Policy and Investment Recommendations. Nine months later, its first deliverable was published, putting forward a comprehensive framework to achieve “ Trustworthy AI ” by offering ethical guidance to AI practitioners. This paper dives into the work carried out by the group, focusing in particular on its AI Ethics Guidelines. First, this paper clarifies the context that led to the creation of the AI HLEG and its mandate (I.). Subsequently, it elaborates on the Guidelines ’ aim and purpose (II.), and analyses the Guidelines ’ drafting process (III.). Particular focus is given to the questions surrounding the respective role played by ethics and law in the AI governance landscape (IV.), as well as some of the challenges that had to be overcome throughout the process (V.). Finally, this paper places the Guidelines in an international context, and sets out the next steps (VI.) ahead on the journey towards an appropriate governance framework for AI (VII.).

R. Byrne

Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI). Counterfactuals can aid the provision of interpretable models to make the decisions of inscrutable systems intelligible to developers and users. However, not all counterfactuals are equally helpful in assisting human comprehension. Discoveries about the nature of the counterfactuals that humans create are a helpful guide to maximize the effectiveness of counterfactual use in AI.

Mark O. Riedl

Humans are increasingly coming into contact with artificial intelligence and machine learning systems. Human-centered artificial intelligence is a perspective on AI and ML that algorithms must be designed with awareness that they are part of a larger system consisting of humans. We lay forth an argument that human-centered artificial intelligence can be broken down into two aspects: (1) AI systems that understand humans from a sociocultural perspective, and (2) AI systems that help humans understand them. We further argue that issues of social responsibility such as fairness, accountability, interpretability, and transparency.

Melanie Mitchell

James Shaw, Frank Rudzicz, T. Jamieson et al.

Background Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. Objective In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). Methods After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. Results Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. Conclusions If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

Francisco-Javier Hinojo-Lucena, I. Aznar-Díaz, María-Pilar Cáceres-Reche et al.

Artificial intelligence has experienced major developments in recent years and represents an emerging technology that will revolutionize the ways in which human beings live. This technology is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of. Therefore, the purpose of this paper was to analyse the scientific production on artificial intelligence in higher education indexed in Web of Science and Scopus databases during 2007–2017. A bespoke methodology of bibliometric studies was used in the most relevant databases in social science. The sample was composed of 132 papers in total. From the results obtained, it was observed that there is a worldwide interest in the topic and that the literature on this subject is just at an incipient stage. We conclude that, although artificial intelligence is a reality, the scientific production about its application in higher education has not been consolidated.

N. C. Eli-Chukwu

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.

Anna Maria Spagnolo, Francesco Palma, Giulia Amagliani et al.

The development of antimicrobial resistance (AMR) and the emergence of multiresistant pathogens represent a growing global threat to both human and animal health. Beyond the excessive and improper use of antimicrobials in human medicine, irrational use in veterinary medicine, agriculture, and aquaculture significantly contributes to the selection and spread of resistant microorganisms, which can enter the food chain and reach humans through food consumption or handling. Based on results from a recent meta-analysis, the prevalence of antimicrobial-resistant foodborne pathogens in food samples exceeds 10%. The veterinary sector is of particular concern, as a large proportion of antimicrobials are used in animal production, generating strong selective pressure and favoring the dissemination of AMR along the food chain. In an increasingly interconnected global context, resistant pathogens and resistance determinants can disseminate rapidly across sectors and national borders, making strategies confined to a single sector insufficient; therefore, effectively addressing AMR requires a One Health approach encompassing the human, veterinary, and environmental domains. Key mitigation strategies include strengthening antimicrobial stewardship programs, also in animal production, reducing routine prophylactic use of antimicrobials, and improving surveillance, coordinated across sectors and, where possible, further supported by advanced technologies such as artificial intelligence and machine learning. Further efforts are also needed to improve microbiological diagnostics, particularly through rapid and molecular methods, to support timely, targeted therapies and reduce inappropriate empirical treatments. In parallel, investment in new therapeutic options, including innovative molecules, drug combinations, and alternative approaches, remains crucial to effectively countering the growing burden of antimicrobial resistance.

Simukayi Mutasa, Shawn H. Sun, Richard S. Ha

Artificial intelligence (AI) is a broad umbrella term used to encompass a wide variety of subfields dedicated to creating algorithms to perform tasks that mimic human intelligence. As AI development grows closer to clinical integration, radiologists will need to become familiar with the principles of artificial intelligence to properly evaluate and use this powerful tool. This series aims to explain certain basic concepts of artificial intelligence, and their applications in medical imaging starting with a concept of overfitting.