scholarly journals Is there a role for statistics in artificial intelligence?

2021 ◽  
Author(s):  
Sarah Friedrich ◽  
Gerd Antes ◽  
Sigrid Behr ◽  
Harald Binder ◽  
Werner Brannath ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Research Question ◽  
Development Planning ◽  
Design Stage ◽  
Political Discussion ◽  
Methodological Development ◽  
Statistical Methodology ◽  
Core Element ◽  
Planning And Design ◽  
Specialist Knowledge

AbstractThe research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.

Big data and artificial intelligence for health system sustainability: The case of Veneto Region

2021 ◽  
pp. 31-52
Author(s):  
Grazia Dicuonzo ◽  
Francesca Donofrio ◽  
Antonio Fusco ◽  
Vittorio Dell’Atti
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Healthcare System ◽  
Care Delivery ◽  
Research Question ◽  
Health Agency ◽  
Time Frame ◽  
Structured Interviews ◽  
Case Study Methodology ◽  
The Impact

This paper investigates the digitalization challenges facing the Italian healthcare system. The aim of the paper is to support healthcare organizations as they take advantage of the potential of big data and artificial intelligence (AI) to promote sustainable healthcare systems. Both the development of innovative processes in the management of health care activities and the introduction of healthcare forecasting systems are valuable resources for clinical and care activities and enable a more efficient use of inputs in essential-level care delivery. By examining an innovative project developed by the Regional Social Health Agency (ARSS) of Veneto, this study analyses the impact of big data and AI on the sustainability of a healthcare system. In order to answer the research question, we used a case study methodology. We conducted semi-structured interviews with key members of the organizational group involved in the case. The results show that the implementation of AI algorithms based on big data in healthcare both improves the interpretation and processing of data, and reduces the time frame necessary for clinical processes, having a positive effect on sustainability.

scholarly journals Investigating Market and Regulatory Forces Shaping Artificial Intelligence Adoptions

10.28945/4644 ◽  
2020 ◽  
Vol 4 ◽  
pp. 177-192
Author(s):  
Chrissann R. Ruehle
Keyword(s):  
Artificial Intelligence ◽  
Research Question ◽  
Large Firms ◽  
High Performing ◽  
Key Stakeholders ◽  
The Media ◽  
Management Capabilities ◽  
Collection And Management ◽  
Future Growth

The Artificial Intelligence (AI) industry has experienced tremendous growth in recent years. Consequently, there has been considerable hype, interest, and even misinformation in the media regarding this emergent technology. Practitioners and academics alike are interested in learning how this market functions in order to make evidence-based decisions regarding its adoption. The purpose of this manuscript is to perform a systematic examination of the current market dynamics as well as identify future growth opportunities for the benefit of incumbents in addition to firms seeking to enter the AI market. The primary research question is: how do market and governmental forces reportedly shape AI adoptions? Drawing on predominantly practitioner focused literature, along with several seminal academic sources, the article begins by examining and mapping stakeholders in the market. This approach allows for the identification and analysis of key stakeholders. Semiconductor and cloud computing firms play a substantive role in the AI adoption ecosystem as they wield substantial power as revealed in this analysis. Subsequently, the TOE framework, which includes the technology, organization and environmental contexts, is applied in order to understand the role of these forces in shaping the AI market. This analysis demonstrates that large firms have a significant competitive advantage due to their extensive data collection and management capabilities in addition to attracting data scientists and high performing analytics professionals. Large firms are actively acquiring small and medium sized AI businesses in order to expand their offerings, particularly in dynamic emerging fields such as facial recognition technology and deep learning.

scholarly journals Corporate digital responsibility (CDR) in construction engineering—ethical guidelines for the application of digital transformation and artificial intelligence (AI) in user practice

2021 ◽  
Vol 3 (10) ◽  
Author(s):  
Bianca Weber-Lewerenz
Keyword(s):  
Artificial Intelligence ◽  
Business Models ◽  
Research Question ◽  
Transformation Process ◽  
Digital Transformation ◽  
Fundamental Rights ◽  
Ethical Framework ◽  
Ethical Guidelines ◽  
Ethical Perspective ◽  
Construction Engineering

AbstractDigitization 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.

scholarly journals New risks related to emerging technologies and reputation for corporate governance

2020 ◽  
Vol 9 (2) ◽  
pp. 64-74
Author(s):  
Hugh Grove ◽  
Mac Clouse ◽  
Tracy Xu
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Boards Of Directors ◽  
Emerging Technologies ◽  
Research Question ◽  
World Intellectual Property Organization ◽  
Global Marketplace ◽  
Challenges And Opportunities ◽  
Potential Risks ◽  
Management Issues

Artificial intelligence (AI) has moved from theory into the global marketplace. The United Nations World Intellectual Property Organization released the first report of its Technology Trends series on January 31, 2019. It considered more than 340,000 AI-related patent applications over the last 70 years. 50 percent of all AI patents have been published in just the last five years. The challenges, potential risks, and opportunities for business and corporate governance from emerging technologies, especially artificial intelligence, have been summarized as whereby machines and software can analyze, optimize, prophesize, customize, digitize and automate just about any job in every industry. Boards of directors and executives need to recognize and understand the new risks associated with these emerging technologies and related reputational risks. The major research question of this paper is how boards of directors and executives can deal with both risk challenges and opportunities to strengthen corporate governance. Accordingly, the following sections of this paper discuss key risk management issues: deep shift risks, global risks, digital risks and opportunities, AI initiatives risks, business risks from millennials, business reputational risks, and conclusions.

REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Ensemble ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 145-165
Author(s):  
Tanmoy Sarkar ◽  
◽  
Tapas Pal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Methodological Development ◽  
Advantages And Disadvantages ◽  
The Future ◽  
Soil Erosion By Water

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

A Study on Children’s Participation in Rural Primary School Campus Landscape Planning and Design: Taking the Renewal Design of Huangtu Town Center Primary School Campus Landscape as Case of Study

2013 ◽  
Vol 671-674 ◽  
pp. 2813-2818 ◽  
Author(s):  
Bo Yang Hu ◽  
Wei Wang
Keyword(s):  
Primary School ◽  
Design Principle ◽  
Landscape Planning ◽  
Cultural Construction ◽  
Design Stage ◽  
Planning And Design ◽  
Children’S Participation ◽  
Evaluation Stage ◽  
Children's Participation ◽  
Town Center

Rural primary school undertakes important cultural mission in the construction of new socialist countryside. This thesis puts forward a design of children’s involvement in the planning and design of campus landscape. Taking the renewal design of Huangtu town center primary school as an example, the thesis will draw up the renewal design principle, and discuss the involving ways of children’s participation in the investigation and analysis stage, schematic design stage, and built evaluation stage. Also, it will expound the significance of children’s participation in the planning and design to children’s development and sustainable development of rural primary school and the cultural construction of new socialist countryside.

scholarly journals Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Naseer Ahmed ◽  
Maria Shakoor Abbasi ◽  
Filza Zuberi ◽  
Warisha Qamar ◽  
Mohamad Syahrizal Bin Halim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Full Text ◽  
Patient Management ◽  
English Language ◽  
Clinical Performance ◽  
Research Question ◽  
Time Saving ◽  
Artificial Intelligence Techniques ◽  
Clinical Scenarios

Objective. The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. Materials and Methods. Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. Results. The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. Conclusion. The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

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