Agora

2021 ◽  
Vol 49 (4) ◽  
pp. 6-11
Author(s):  
Jonas Traub ◽  
Zoi Kaoudi ◽  
Jorge-Arnulfo Quiané-Ruiz ◽  
Volker Markl
Keyword(s):  
Artificial Intelligence ◽  
Data Management ◽  
Domain Knowledge ◽  
Data Science ◽  
Dynamic Environment ◽  
Fine Grained ◽  
Computational Resources ◽  
Diverse Data ◽  
Novel Applications ◽  
Lock In

Data science and artificial intelligence are driven by a plethora of diverse data-related assets, including datasets, data streams, algorithms, processing software, compute resources, and domain knowledge. As providing all these assets requires a huge investment, data science and artificial intelligence technologies are currently dominated by a small number of providers who can afford these investments. This leads to lock-in effects and hinders features that require a flexible exchange of assets among users. In this paper, we introduce Agora, our vision towards a unified ecosystem that brings together data, algorithms, models, and computational resources and provides them to a broad audience. Agora (i) treats assets as first-class citizens and leverages a fine-grained exchange of assets, (ii) allows for combining assets to novel applications, and (iii) flexibly executes such applications on available resources. As a result, it enables easy creation and composition of data science pipelines as well as their scalable execution. In contrast to existing data management systems, Agora operates in a heavily decentralized and dynamic environment: Data, algorithms, and even compute resources are dynamically created, modified, and removed by different stakeholders. Agora presents novel research directions for the data management community as a whole: It requires to combine our traditional expertise in scalable data processing and management with infrastructure provisioning as well as economic and application aspects of data, algorithms, and infrastructure.

scholarly journals Data management challenges for artificial intelligence in plant and agricultural research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 324
Author(s):  
Hugh F. Williamson ◽  
Julia Brettschneider ◽  
Mario Caccamo ◽  
Robert P. Davey ◽  
Carole Goble ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Management ◽  
Data Science ◽  
Agricultural Research ◽  
Plant Science ◽  
Agricultural Technologies ◽  
Novel Food ◽  
Different Sources

Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

scholarly journals Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ozan Karaca ◽  
S. Ayhan Çalışkan ◽  
Kadir Demir
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Medical Students ◽  
Data Science ◽  
Factor Model ◽  
Health Science ◽  
Measurement Tool ◽  
Validity And Reliability ◽  
Mathematical Concepts ◽  
Evaluation And Monitoring

Abstract Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students’ required competencies on AI, a diverse set of experts’ opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach’s alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

scholarly journals Using Virtual Learning Environment Data for the Development of Institutional Educational Policies

2021 ◽  
Vol 11 (15) ◽  
pp. 6811
Author(s):  
Emanuel Marques Queiroga ◽  
Carolina Rodríguez Enríquez ◽  
Cristian Cechinel ◽  
Alén Perez Casas ◽  
Virgínia Rodés Paragarino ◽  
...  
Keyword(s):  
Learning Environment ◽  
Predictive Models ◽  
Data Science ◽  
Educational Data Mining ◽  
Virtual Learning ◽  
Academic Community ◽  
Virtual Learning Environment ◽  
Educational Policies ◽  
Interdisciplinary Exchange ◽  
Computational Resources

This paper describes the application of Data Science and Educational Data Mining techniques to data from 4529 students, seeking to identify behavior patterns and generate early predictive models at the Universidad de la República del Uruguay. The paper describes the use of data from different sources (a Virtual Learning Environment, survey, and academic system) to generate predictive models and discover the most impactful variables linked to student success. The combination of different data sources demonstrated a high predictive power, achieving prediction rates with outstanding discrimination at the fourth week of a course. The analysis showed that students with more interactions inside the Virtual Learning Environment tended to have more success in their disciplines. The results also revealed some relevant attributes that influenced the students’ success, such as the number of subjects the student was enrolled in, the students’ mother’s education, and the students’ neighborhood. From the results emerged some institutional policies, such as the allocation of computational resources for the Virtual Learning Environment infrastructure and its widespread use, the development of tools for following the trajectory of students, and the detection of students at-risk of failure. The construction of an interdisciplinary exchange bridge between sociology, education, and data science is also a significant contribution to the academic community that may help in constructing university educational policies.

scholarly journals Data Science and Artificial Intelligence for Communications

2019 ◽  
Vol 57 (11) ◽  
pp. 82-83
Author(s):  
Irena Atov ◽  
Kwang-Cheng Chen ◽  
Ahmed Kamal ◽  
Shui Yu
Keyword(s):  
Artificial Intelligence ◽  
Data Science

Data Scientists’ Identity Work: Omnivorous Symbolic Boundaries in Skills Acquisition

2021 ◽  
pp. 095001702097730
Author(s):  
Netta Avnoon
Keyword(s):  
Professional Identity ◽  
Domain Knowledge ◽  
Data Science ◽  
Identity Work ◽  
Skills Acquisition ◽  
Sociology Of Culture ◽  
Sociology Of Work ◽  
Discursive Analysis ◽  
Self Learning ◽  
Depth Interviews

Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the following five resources to construct their identity: (1) ability to bridge the gap between scientist’s and engineer’s identities; (2) multiplicity of theories; (3) intensive self-learning; (4) bridging technical and social skills; and (5) acquiring domain knowledge easily. These resources diverge from former generalist-specialist identity tensions described in the literature as they attribute a higher status to the generalist-omnivore and a lower one to the specialist-snob.

Series Editorial: Artificial Intelligence and Data Science for Communications

2021 ◽  
Vol 59 (8) ◽  
pp. 42-42
Author(s):  
Yongmin Choi ◽  
Ahmed E. Kamal ◽  
Malamati Louta
Keyword(s):  
Artificial Intelligence ◽  
Data Science
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