Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia

open access articleBackground: Art enhances both physical and mental health wellbeing. The health benefits include reduction in blood pressure, heart rate, pain perception and briefer inpatient stays, as well as improvement of communication skills and self-esteem. In addition to these, people living with dementia benefit from reduction of their noncognitive, behavioural changes, enhancement of their cognitive capacities and being socially active. Methods: The current study represents a narrative general literature review on available studies and knowledge about contribution of Artificial Intelligence (AI) in creative arts. Results: We review AI visual arts technologies, and their potential for use among people with dementia and care, drawing on similar experiences to date from traditional art in dementia care. Conclusion: The virtual reality, installations and the psychedelic properties of the AI created art provide a new venue for more detailed research about its therapeutic use in dementia

What can AI do for you?

Simply put, most organizations do not know how to approach the incorporation of AI into their businesses, and few are knowledgeable enough to understand which concepts are applicable to their business models. Doing nothing and waiting is not an option: Mahidar and Davenport (2018) argue that companies that try to play catch-up will ultimately lose to those who invested and began learning early. But how do we bridge the gap between skepticism and adoption? We propose a toolkit, inclusive of people, processes, and technologies, to help companies with discovery and readiness to start their AI journey. Our toolkit will deliver specific and actionable answers to the operative question: What can AI do for you

Identifying and characterizing employee groups by turnover risk using predictive analytics

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis project presents a predictive analytics project developed in a European multinational to understand and predict the turnover of its employees. It analyses the Human Resources current challenges, such as the increasing global competition for talent, where players compete for scarce skillsets such as technology and data science, and the new strategies necessary to deal with this scenario. The study explores the literature review of these contextual matters and of the studies of variables that influence turnover, generating insights and input for applying techniques aligned with the new mindset of identifying ‘flight-risk’ groups and developing targeted actions instead of only one-size-fits-all solutions. The project gathered data from different sources of the organization, designed variables, based on a literature review and internal brainstorms, treated data quality issues, transformed the data and applied three different machine learning algorithms to develop a classification predictive model. The study evaluated 46 input variables and selected a set of 26 that had higher impact on the turnover which were used in the models. Finally, it applied clustering techniques to divide employees in clusters, and identified two containing more extreme turnover behaviors (“Loyal” and “Flight risk”) and described them accordingly to their main characteristics contributing with practical insights to support potential decisions

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Evolution of artificial intelligence research in human resources

The objective of the article is to investigate the evolution of the application of Artificial Intelligence (AI) in the area of Human Resources (HR). It presents a panorama of the research that used AI in the area of HR, through the quantitative descriptive analysis of journals and proceedings, registered in the base of the Library of Online Knowledge (B-on), in the period between the years of 2000 and 2018. 32 research publications have been identified that address the application of Artificial Intelligence in the Human Resources area. As a didactic support and aiming to facilitate the understanding of the analysis, the period of the 18 years studied was divided into 3 time periods (First decade, Reduction Period and Period of Growth). The study also raised the distribution of AI application in HR topics and the incidence in each of these themes. As a result, it was concluded that there are few researches on AI applied to Human Resources and a dispersed use behaviour. It is expected that the 9 inferences about the results mentioned in this essay will elicit future studies.info:eu-repo/semantics/publishedVersio

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy

Research on Risk Prediction and Early Warning of Human Resource Management Based on Machine Learning and Ontology Reasoning

Talent is the first resource, the development of the enterprise to retain key talent is essential, the main research is based on machine learning and ontological reasoning, human resources analysis and management risk prediction and early warning methods, first of all, according to the specific situation and the target case, through the calculation of the similarity of the concept name and attribute of the similarity assessment of the source case in the case library, the matching of knowledge-based employees of the company\u27s case for the similarity prediction and human resources management risk prediction research. Then, according to the evaluation results, we can find out the most suitable job matches in specific risk problems and situations. This is a solution to the target cases and criteria for companies to evaluate candidates. Second, we have successfully developed and implemented a prediction model that applies machine learning to the early warning study of risk prediction for HR management. The model is optimized with a cross-validation function, and the convergence of the model training is accelerated by the regularization of Newton\u27s iterative method. Finally, our prediction model achieved 82% yield. Ontological reasoning and machine learning are promising in human resource management risk prediction and warning, which is proved by the high accuracy rate verified by examples. Finally, we analyze the proposed results of HRM risk prediction and early warning to contribute to the improvement of risk control and suggest measures for possible risks

EPSRC IMPACT Exhibition

This exhibition was conceived by Dunne (PI) and comprised 16 mixed-media speculative design research projects. It marked the culmination of an EPSRC-funded initiative also partly supported by NESTA. Dunne supervised and then curated the projects by staff, graduates and students of the RCA Design Interactions programme. Each was conducted in collaboration with an external research partner organisation already supported by the EPSRC. The topics covered ranged from renewable energy devices and security technologies to the emerging fields of synthetic biology and quantum computing. Dunne and an advisory panel from EPSRC and NESTA selected themes on the basis of diversity of topic, design opportunities, intellectual and creative challenges, and public relevance. Dunne invited the designers to take a radical, interrogative approach, exploring the social, ethical and political implications of the research. Each designer visited the relevant science lab, consulted with the scientists throughout the project, and participated in a one-day workshop hosted by NESTA between scientists and designers on such forms of collaboration. Designers carried out literature, journal, and project surveys before developing their projects through iterative prototypes. The exhibition, held at the RCA in 2010, was considered by EPSRC to offer a powerful insight into how today’s research might transform our experience of the world. It was reviewed in the Guardian (2010), Wired (2010) and Design Week (2010). Dunne presented ‘IMPACT!’ in conferences including the IDA Congress, ‘Design at the Edges’, Taipei (2011) and at the Wellcome Trust, London (2011). He gave a related lecture to researchers at Microsoft Research Asia, Beijing (2011). Individual exhibits from the project featured in exhibitions: Museum of Modern Art (2011), National Museum of China (2011); Z33 (2010–11); Wellcome Trust (2010–11); Saint-Étienne International Design Biennial (2010); Ars Electronica (2010); The Times Cheltenham Science Festival (2010); and V2_, Institute for the Unstable Media (2010)