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This book highlights artificial intelligence algorithms used in implementation of automated pricing. It presents the process for building automated pricing models from crawl data, preprocessed data to implement models, and their applications. The book also focuses on machine learning and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are presented to illustrate the pricing processes and models.
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
This book presents Proceedings of the International Conference on Intelligent Systems and Networks (ICISN 2022), held at Hanoi in Vietnam. It includes peer reviewed high quality articles on Intelligent System and Networks. It brings together professionals and researchers in the area and presents a platform for exchange of ideas and to foster future collaboration. The topics covered in this book include- Foundations of Computer Science; Computational Intelligence Language and speech processing; Software Engineering Software development methods; Wireless Communications Signal Processing for Communications; Electronics track IoT and Sensor Systems Embedded Systems; etc. ​
This book teaches the full process of how to conduct machine learning in an organizational setting. It develops the problem-solving mind-set needed for machine learning and takes the reader through several exercises using an automated machine learning tool. To build experience with machine learning, the book provides access to the industry-leading AutoML tool, DataRobot, and provides several data sets designed to build deep hands-on knowledge of machinelearning.
Artificial intelligence is forecasted to have trillions of dollars of impact on businesses and the economy, yet many marketers struggle to understand what it is and how to apply it in their marketing efforts. The truth is, AI possesses the power to change everything. While AI-powered marketing technologies may never achieve the sci-fi vision of self-running, self-improving autonomous systems, a little bit of AI can go a long way toward dramatically increasing productivity, efficiency, and performance. Marketing AI Institute’s Founder & CEO, Paul Roetzer, and Chief Content Officer, Mike Kaput, join forces to show marketers how to embrace AI and make it their competitive advantage. Marketing Artificial Intelligence draws on years of research and dozens of interviews with AI marketers, executives, engineers, and entrepreneurs. Roetzer and Kaput present the current potential of AI, as well as a glimpse into a near future in which marketers and machines work seamlessly to run personalized campaigns of unprecedented complexity with unimaginable simplicity. As the amount of data exponentially increases, marketers’ abilities to filter through the noise and turn information into actionable intelligence remain limited. Roetzer and Kaput show you how to make breaking through that noise your superpower. So, come along on a journey of exploration and enlightenment. Marketing Artificial Intelligence is the blueprint for understanding and applying AI, giving you just the edge in your career you’ve been waiting for.
"This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.
This book constitutes the refereed proceedings of the 45th German Conference on Artificial Intelligence, KI 2022, held in September 2022. The 12 full and 5 short papers were carefully reviewed and selected from 51 submissions. Additionally, five abstracts of invited talks are included. As well-established annual conference series KI is dedicated to research on theory and applications across all methods and topic areas of AI research. Due to COVID-19 the conference was held virtually. The chapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Artificial intelligence (AI) tools are software applications or platforms that employ AI algorithms to accomplish specific tasks or solve problems. They are designed to replicate or augment human intelligence and can be utilized in numerous industries, including healthcare, finance, marketing, and e-commerce. A common application of AI tools is data analysis and forecasting. These tools are capable of analyzing massive datasets and identifying patterns and tendencies that may not be readily apparent to humans. This can be utilized to predict customer behavior, identify risk factors, and optimize business processes. In natural language processing (NLP), which involves understanding and interpreting human language, AI tools can also be utilized. NLP is implemented in numerous applications, including chatbots, translation software, and voice recognition systems. An additional common application of AI tools is image and video processing. These tools are capable of analyzing and comprehending the content of images and videos, and can be utilized for tasks such as object recognition, facial recognition, and image and video manipulation. Additionally, AI tools can be used to automate tasks and processes. For instance, they can be used to automate customer service inquiries, schedule and manage appointments, and manage inventory and supply chains. There are numerous AI tools available, and the appropriate tool for a given task will depend on the task's requirements and constraints. Some AI tools are intended for non-technical users, while others necessitate advanced programming skills. AI tools have the potential to significantly increase efficiency and productivity across a wide range of industries and can assist businesses in making better decisions and achieving their objectives more quickly and efficiently.
This book develops allocation mechanisms that aim to ensure an efficient resource allocation in modern IT-services. Recent methods of artificial intelligence, such as neural networks and reinforcement learning, and nature-oriented optimization methods, such as genetic algorithms and simulated annealing, are advanced and applied to allocation processes in distributed IT-infrastructures, or grid systems.
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.