The article of record as published may be found at http://dx.doi.org/10.1109/MC.1985.1663007 ; The progress, goals and techniques being used in the Japanese fifth-generation computer program are assessed. The research is being performed in three phases: tool building, construction of parallel architecture machines, and evaluation and refinement. The first phase is well under way and has yielded designs for two prototype machines: a Personal Sequential Interface (PSI) workstation and the Delta machine (DM), a relational database machine. Kernel Language 0 (KL0), used for the PSI, is being expanded to KL1. The Mandala language is being applied in the DM. Applications have not received a great deal of attention at the government-funded research center, although the techniques developed are already being implemented in industry for machine and computer design and communications systems. 18 references.
International audience ; This proposal outlines a plan for bridging the gap between technology experts and society in the domain of Artificial Intelligence (AI). The proposal focuses primarily on Natural Language Processing (NLP) technology, which is a major part of AI and offers the advantage of addressing problems that non-experts can understand. More precisely, the goal is to advance knowledge at the same time as opening new communication channels between experts and society, in a way which promotes non-expert participation in the conception of NLP technology. Such interactions can happen in the context of open-source development of languages resources, i.e. software tools and datasets; existing usages in various communities show how projects which are open to everyone can greatly benefit from the free participation of enthusiastic contributors (participation is not at all limited to software development). Because NLP research is mostly experimental and relies heavily on software tools and language datasets, this project proposes to interconnect the societal issues related to AI with the NLP research resources issue.
International audience ; This proposal outlines a plan for bridging the gap between technology experts and society in the domain of Artificial Intelligence (AI). The proposal focuses primarily on Natural Language Processing (NLP) technology, which is a major part of AI and offers the advantage of addressing problems that non-experts can understand. More precisely, the goal is to advance knowledge at the same time as opening new communication channels between experts and society, in a way which promotes non-expert participation in the conception of NLP technology. Such interactions can happen in the context of open-source development of languages resources, i.e. software tools and datasets; existing usages in various communities show how projects which are open to everyone can greatly benefit from the free participation of enthusiastic contributors (participation is not at all limited to software development). Because NLP research is mostly experimental and relies heavily on software tools and language datasets, this project proposes to interconnect the societal issues related to AI with the NLP research resources issue.
AbstractArtificial intelligence (AI) is a science and engineering discipline that is highly relevant to financial services, given the significant amount and diversity of data generated (and consumed) as those services are delivered worldwide. Global banks process billions of international payments each day, while equity exchanges handle trillions of orders and billions of transactions. All of this activity is recorded as data, and driven by exogenous information sources such as news services and social media. To address these challenges, at J.P. Morgan, we established a new group dedicated to research at the intersection of AI and finance in mid-2018 to investigate how to develop and optimize the use of AI. In this article, we introduce and discuss the directions of focus of AI Research and present a few selective projects that illustrate potential novel applications to finance.
In: The Canadian journal of economics: the journal of the Canadian Economics Association = Revue canadienne d'économique, Volume 57, Issue 2, p. 363-377
AbstractArtificial intelligence (AI) may be the next general purpose technology. General purpose technologies, such as the steam engine and computing, can have an outsized impact on productivity through a positive feedback loop between producing and application industries. Along with the discussion of AI's potential to improve productivity come a number of policy concerns related to AI's potential to automate jobs and to create existential risk for humanity. Because of these worries, in March 2023, a widely circulated petition called for a pause in AI research. That letter asked several questions about AI's potential impact on society. This paper examines those questions through an economic lens. It highlights reasons to be optimistic about the long‐run impact of AI, while underscoring short‐run risks. Economic models provide an understanding of where the ambiguity lies and where it does not. Our models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely. In contrast, there is substantial ambiguity on the implications of AI's diffusion for inequality.
AbstractIn this paper, I will identify two problems of trust in an AI-relevant context: a theoretical problem and a practical one. I will identify and address a number of skeptical challenges to an AI-relevant theory of trust. In addition, I will identify what I shall term the 'scope challenge', which I take to hold for any AI-relevant theory (or collection of theories) of trust that purports to be representationally adequate to the multifarious forms of trust and AI. Thereafter, I will suggest how trust-engineering, a position that is intermediate between the modified pure rational-choice account and an account that gives rise to trustworthy AI, might allow us to address the practical problem of trust, before identifying and critically evaluating two candidate trust-engineering approaches.
PurposeThe current evolution of artificial intelligence (AI) practices and applications is creating a disconnection between modern-day information system (IS) research and practices. The purpose of this study is to propose a classification framework that connects the IS discipline to contemporary AI practices.Design/methodology/approachWe conducted a review of practitioner literature to derive our framework's key dimensions. We reviewed 103 documents on AI published by 25 leading technology companies ranked in the 2019 list of Fortune 500 companies. After that, we reviewed and classified 110 information system (IS) publications on AI using our proposed framework to demonstrate its ability to classify IS research on AI and reveal relevant research gaps.FindingsPractitioners have adopted different definitional perspectives of AI (field of study, concept, ability, system), explaining the differences in the development, implementation and expectations from AI experienced today. All these perspectives suggest that perception, comprehension, action and learning are the four capabilities AI artifacts must possess. However, leading IS journals have mostly published research adopting the "AI as an ability" perspective of AI with limited theoretical and empirical studies on AI adoption, use and impact.Research limitations/implicationsFirst, the framework is based on the perceptions of AI by a limited number of companies, although it includes all the companies leading current AI practices. Secondly, the IS literature reviewed is limited to a handful of journals. Thus, the conclusions may not be generalizable. However, they remain true for the articles reviewed, and they all come from well-respected IS journals.Originality/valueThis is the first study to consider the practitioner's AI perspective in designing a conceptual framework for AI research classification. The proposed framework and research agenda are used to show how IS could become a reference discipline in contemporary AI research.
The COVend project aims at delivering a new effective therapy, FX06, against the SARS-CoV-2 virus infection for the management of the COVID-19 disease in hospitals. Nine of the 17 partners of the project consortium are hospitals responsible for collecting study subjects and administering the FX06 therapy to the patients. Although the clinical trial (IXION) has the main role in the project, the project has also a work package which develops and applies artificial intelligence (AI) methods to the data collected during the 28-day study period from the patients receiving the therapy. The AI work package applies exploratory data analysis methods to find patterns and profiles of the patients. Combined with the data about treatment methods and patient outcomes, the aim is to provide decision support for the therapy intervention in the later stage of the project.
This contribution is focused on relevant aspects to develop an R+D program in Artificial Intelligence (AI) under European ethical standards. Considering some initial obstacles in the educational systems, and the strengths/weaknesses of the industrial regions, the European Union's strategy should be different, in many aspects, from that followed by China or the US. To be socially acceptable and gain the support of the sectors that can most benefit from its applications, it must involve qualified state or private actors in an accountability-based scheme of collaboration.
There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.—what we collectively call "citizen forums". As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.
Purpose The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.
Design/methodology/approach This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management.
Findings Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.
Research limitations/implications This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers' perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.
Originality/value This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.
