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Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern. Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy. Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry. - Looks at new techniques in artificial intelligence (AI) reducing the dimension of data coverage worldwide - Chapters include AI methodologies using enhanced hybrid swarm-based optimization algorithms - Includes flowchart diagrams for exampling optimizing techniques
Does artificial intelligence include robotics? Does artificial intelligence exist in robots? I was wondering if you could explain me how these two terms come from different places. Both artificial intelligence and robots serve various purposes. On the other hand, people often conflate the two. A significant number of individuals are uncertain as to whether artificial intelligence (AI) and robotics are distinct ideas or if they are interchangeable. Artificial Intelligence Vs Robotics The technology of computing serves as the basis for artificial intelligence (AI). The process involves the development of software that computers can use to do tasks that were previously performed by people requiring intelligence. The capabilities of artificial intelligence systems include the ability to learn, observe, solve problems, grasp language, and reason rationally. Artificial intelligence may be used in a wide variety of modern applications, ranging from personal assistants to autonomous vehicles. There is a continuous progression taking place in the area of artificial intelligence (AI). In spite of this, artificial intelligence is occasionally depicted in science fiction as being as lifelike as is physically possible in robot form. Nevertheless, robotics is a discipline of computer science that focuses on the study of robots like these. Robots, which are programmable machines, are often capable of carrying out a series of activities either entirely or partly on their own. One must possess all three of the following characteristics in order to be classified as a robot Through the use of their sensors and actuators, robots interact with the environment that they are in Automatons may be programmed by you. The vast majority of robots are capable of functioning on little or no supervision from humans. Due to the fact that certain robots do not have the capability to function alone, the word "usually" is used to characterize robots. For example, even if they are only controlled by humans, telerobots are still regarded to be a branch of robotics for the purpose of classification. Long term, robots that are driven by artificial intelligence will link the two areas. The operation of these mechanical creatures is carried out by intelligent computer software. The majority of robots do not possess any kind of artificial intelligence. Up until quite recently, all industrial robots were restricted to doing the same set of duties simultaneously. We have previously shown that artificial intelligence is not required for jobs that are routine and repetitive. There are significant limitations placed on the capabilities of robots that lack intelligence. Algorithms that are based on artificial intelligence are often necessary in order to make it possible for robots to perform more complex tasks.
This proceedings book contains papers presented at the XI International Online Forum named after A.Ya. Kibanov "Innovative Personnel Management,", which took place in Moscow, Russian Federation, 15th April-5th May 2020. Organized by Moscow State University of Management, the Forum chiefly focused on HR management issues under conditions of active penetration of IT into the management and economic sphere. The authors of contributions included in this book examine both the theoretical basis for the development of the labor landscape in our digital future, and specific practical issues related to the real business practice. The book includes results of multidisciplinary studies on the following issues: employment and the labor market: a future perspective; current trends of HR management development in digital conditions; IT for creating healthy work conditions; digital transformation and new architecture of the labor market; innovative, strategic HR management and HR analytics; leadership, etc. The book consists of six parts corresponding to thematic areas of the Forum. The first part deals with the transformation of the labor market under the influence of digitalization and international economic relations. The second part is devoted to the analysis of the current changes in the HR management caused by digitalization, as well as issues of creating a healthy work environment and managing well-being with information technology. New architecture of the labor market is considered in the third part of the book in the face of the global uncertainty and the application of digital technology in entrepreneurial activities. The fourth part investigates innovative approaches to the personnel development: from resource management to capacity management. The fifth part presents strategic HR management and HR analytics in the context of current macro-calls. And finally, the sixth part is aimed at considering leadership aspects and relations between investments in the human capital and needed business results. This book is a combination of different scientific opinions and research works of scholars from different countries and regions, offering us a colorful picture of the future labor landscape: jobs, competences and skills that will be in demand.
This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.