No Country for Non-Graduate Men: The Childish Roots of Adult Job Tasks \& Employment

Male employment has declined across advanced economies as non-graduate men found it increasingly difficult to gain jobs in the wake of technological change and globalisation. This has led to rising earnings and, subsequently, income inequality. Female employment, by contrast, has risen in this period. Previous work has shown changing job task demands explain this pattern - with declining manual tasks penalising men and rising non-routine tasks benefiting women. In this paper, I test whether gendered differences in childhood \& adolescent cognitive, social, perseverance, and emotional-health skills can help explain why men are less adept at non-routine tasks using long-term longitudinal data from the United Kingdom. I find that childhood \& adolescent skills have a significant effect on adult job tasks and employment outcomes. Greater cognitive and childhood emotional-health skills lead to people performing more high-pay analytical and interactive job tasks as adults. Greater cognitive and non-cognitive skills are also associated with higher adult employment levels. Indicative calculations show that gendered differences in these childhood and adolescent skills explain an economically significant decline in the analytical and interactive job tasks performed by non-graduate men as well as their employment rates.

Ergonomic Evaluation of Working Conditions in Orthotists and Prosthetists by Rapid Entire Body Assessment (REBA)

Background and Objectives: This study aimed to determine the prevalence rate of musculoskeletal disorders and evaluate the body position in routine tasks among orthotists and prosthetists. Methods: Forty orthotists and prosthetists were included. The scores of the Nordic Musculoskeletal Questionnaire and the Rapid Entire Body Assessment were used to determine the prevalence rate of musculoskeletal disorders and analyze the work position of orthotists and prosthetists, respectively. An examiner evaluated 10 working postures that were dominantly used every day, in each orthotist and prosthetist. Results: Among the orthotists, 55.6% of men and 47% of women suffered from pain in the trunk, neck, and lower limbs. Nearly similar results were seen in the upper limbs (74.1% men and 45.5% women). Such high prevalence rates were not seen in prosthetists. The analysis of the Rapid Entire Body Assessment scores based on the working task and gender of the orthotist and prosthetist showed that more than 60% of the workers achieved a score of 4 to 7 approximately in half of the tasks. It shows the medium risk of musculoskeletal disorders, thus, corrective action is necessary. Conclusion: Based on the findings, musculoskeletal disorders are highly prevalent among orthotists and prosthetists, especially in the orthotist workers. To reduce these disorders, it is recommended to add ergonomic topics and training courses for working with devices to increase the knowledge of specialists and apply and select practical tools based on the principles of ergonomics.

Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems

Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers’ performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program’s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor’s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor’s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods.

The Legal Aspects of Artificial Intelligence based on the EU Experience

In the digital era, technological advances have brought innovative opportunities. Artificial intelligence is a real instrument to provide automatic routine tasks in different fields (healthcare, education, the justice system, foreign and security policies, etc.). AI is evolving very fast. More precisely, robots as re-programmable multi-purpose devices designed for the handling of materials and tools for the processing of parts or specialized devices utilizing varying programmed movements to complete a variety of tasks.1 Regardless of opportunities, artificial intelligence may pose some risks and challenges for us. Because of the nature of AI ethical and legal questions can be pondered especially in terms of protecting human rights. The power of artificial intelligence means using it more effectively in the process of analyzing big data than a human being. On the one hand, it causes loss of traditional jobs and, on the other hand, it promotes the creation of digital equivalents of workers with automatic routine task capabilities. “Artificial intelligence must serve people, and therefore artificial intelligence must always comply with people’s rights,” said Ursula von der Leyen, President of the European Commission.2 The EU has a clear vision of the development of the legal framework for AI. In the light of the above, the article aims to explore the legal aspects of artificial intelligence based on the European experience. Furthermore, it is essential in the context of Georgia’s European integration. Analyzing legal approaches of the EU will promote an approximation of the Georgian legislation to the EU standards in this field. Also, it will facilitate to define AI’s role in the effective digital transformation of public and private sectors in Georgia.

Applied Artificial Intelligence in the Subsurface

In this paper we present an example of improved approaches for how to interact with data and leverage artificial intelligence for the subsurface. Currently, subsurface workflows typically rely on a lot of time-consuming manual input and analysis, but the promise of artificial intelligence is that, once properly trained, an AI can take care of the more routine tasks, leaving the domain expert free to work on more complex and creative parts of the job. Artificial intelligence work on subsurface datasets in recent years has typically taken the form of research and proof of concept type work, with a lot of one-off solutions showing up in the literature using new and innovative ideas (e.g. Hussein et al, 2021; Misra et al, 2019). Oftentimes this work requires a good degree of data science knowledge and programming skills on the part of the scientist, putting many of the approaches outlined in these and a multitude of other papers out of reach for many subsurface experts in the Oil and Gas industry. In order for Artificial Intelligence to become applied as part of regular workflows in the subsurface, the industry needs tools built to help subsurface experts access AI techniques in a more practical, targeted way. We present herein a practical guide to help in developing applied artificial Intelligence tools to roll out within your organization or to the industry more broadly.

The Contemporary Significance of the Early Accounting Teaching Methods

In the light of global trends, economic operators cannot withstand change. At first, computer-driven technologies replaced only routine tasks, which were easily programmed using algorithms. However, as a result of technological development, artificial intelligence, machine learning, the internet and big data, machines have acquired an understanding of non-routine tasks. They have become autonomous, and are now capable of solving more and more complex tasks.The job opportunities offered by the new digitalized world ask for new competencies developed by the education system. Our research examines 20 th-century teaching methods based on final exams made public, then compares and contrasts them to 21 st-century teaching materials and examination methods. One of the significantobservations is that between 1900 and 1918, the final exam in mathematics contained only word problems. The majority of the 223 problems available, 57 by number, focused on “capital, interest, benefit, loan, sales and purchase.” The wording of the problemsreflects the (actual) problems and events of the time. Although case studies are still present, most problems are the “calculate, solve, circle, underline” type. Example problems are often provided; thus, problem-solving turns into a routine task. The earlier method yet based on word problems inspired by our everyday economic reality might prove helpful in developing problem-solving skills, in reducing reading comprehension difficulties present at all levels of education as well as in indirectly raising awareness of today’s environmental, personal finance, issues.

How Technology will Change Our Lives in Ten Years

Aim: An analysis of how the already mind-blowing world of tech is going to evolve not just itself but everyone’s lives in the next decade. Background: Technology has been an important part of our daily lives from centuries back with the invention of The Wheel; humans have relied on tech in an effort to make their lives easier. Data Source: A Search of published evidence from using keywords (as outlined below) was undertaken from which relevant sources were selected to build an informed discussion. Keywords: Technology, Disease Prevention, Autonomous Driving, Efficiency, Routine Tasks

Acting and Learning with Goal and Task Decomposition

<p><b>Two central problems of creating artificial intelligent agents that can operate in the human world are learning the necessary knowledge to achieve routine tasks, and using that knowledge effectively in a complex and unpredictable domain. The thesis argues that an important part of this domain knowledge should be represented in the form of decomposition rules that decompose tasks into subgoals. The thesis presents HOPPER, an implemented planning system that uses decomposition rules and a least-commitment decomposition strategy that strikes a balance between reactive and deliberative planning. Like reactive planners, HOPPER is able to robustly handle and recover from unexpected events with minimal disruption to its plan. Like deliberative planners, it is also able to plan ahead to take advantage of opportunities to interleave and shorten its sub-plans. The thesis also presents TADPOLE, an implemented learning system that learns both the structure and preconditions of new decomposition rules from a small number of lessons demonstrated by a teacher. It learns by parsing and interpreting the teacher’s behaviour in terms of decomposition rules it already knows. It extends its rule set by filling in the holes in its parses of the teacher’s lessons.</b></p> <p>Both HOPPER and TADPOLE have been evaluated together in two different domains: a kitchen domain that emphasizes complexity, and a logistics domain that emphasizes plan efficiency. Every rule used by HOPPER was learned by TADPOLE and every rule learned by TADPOLE was successfully used by HOPPER to achieve various tasks, showing that TADPOLE is able to learn effective decomposition rules from minimal lessons from a teacher, and that HOPPER is able to robustly make use of them even in the face of unexpected events.</p>

Acting and Learning with Goal and Task Decomposition

<p><b>Two central problems of creating artificial intelligent agents that can operate in the human world are learning the necessary knowledge to achieve routine tasks, and using that knowledge effectively in a complex and unpredictable domain. The thesis argues that an important part of this domain knowledge should be represented in the form of decomposition rules that decompose tasks into subgoals. The thesis presents HOPPER, an implemented planning system that uses decomposition rules and a least-commitment decomposition strategy that strikes a balance between reactive and deliberative planning. Like reactive planners, HOPPER is able to robustly handle and recover from unexpected events with minimal disruption to its plan. Like deliberative planners, it is also able to plan ahead to take advantage of opportunities to interleave and shorten its sub-plans. The thesis also presents TADPOLE, an implemented learning system that learns both the structure and preconditions of new decomposition rules from a small number of lessons demonstrated by a teacher. It learns by parsing and interpreting the teacher’s behaviour in terms of decomposition rules it already knows. It extends its rule set by filling in the holes in its parses of the teacher’s lessons.</b></p> <p>Both HOPPER and TADPOLE have been evaluated together in two different domains: a kitchen domain that emphasizes complexity, and a logistics domain that emphasizes plan efficiency. Every rule used by HOPPER was learned by TADPOLE and every rule learned by TADPOLE was successfully used by HOPPER to achieve various tasks, showing that TADPOLE is able to learn effective decomposition rules from minimal lessons from a teacher, and that HOPPER is able to robustly make use of them even in the face of unexpected events.</p>

Study on Significant Drift in the Domain of Explainable Artificial Intelligence

Artificial Intelligence (AI) is required since multiple resources are in need to complete depending on a daily basis. As a result, automating routine tasks is an excellent idea. This reduces the foundation's work schedules while also improving efficiency. Furthermore, the business can obtain talented personnel for the business strategy through Artificial Intelligence. Explainability in XAI derives from a combination of strategies that improve machine learning models' environmental flexibility and interpretability. When Artificial Intelligence is trained with a large number of variables to which we apply alterations, the entire processing is turned into a black box model which is in turn difficult to understand. The data for this research's quantitative analysis is gathered from the IEEE, Web of Science, and Scopus databases. This study looked at a variety of fields engaged in the (Explainable Artificial Intelligence) XAI trend, as well as the most commonly employed techniques in domain of XAI, the location from which these studies were conducted, the year-by-year publishing trend, and the most frequently occurring keywords in the abstract. Ultimately, the quantitative review reveals that employing Explainable Artificial Intelligence or XAI methodologies, there is plenty of opportunity for more research in this field.