This book is a collection of selected research papers presented at the 2023 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), held in Berlin, Germany, on June 30 - July 2, 2023. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. It is a timely and up-to-date publication responsive to the rapid development of AI technologies, practices and their increasingly complex interplay with the education domain. It promotes the cross-fertilisation of knowledge and ideas from researchers in various fields to construct the interdisciplinary research area of AI in Education. These subject areas include computer science, cognitive science, education, learning sciences, educational technology, psychology, philosophy, sociology, anthropology and linguistics. The feature of this book will contribute from diverse perspectives to form a dynamic picture of AI in Education. It also includes various domain-specific areas for which AI and other education technology systems have been designed or used in an attempt to address challenges and transform educational practice. This timely publication is in line with UNESCO’s Beijing Consensus on Artificial Intelligence and Education. It is committed to exploring how AI may play a role in bringing more innovative practices, transforming education, and triggering an exponential leap towards the achievement of the Education 2030 Agenda. Providing broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.

This book constitutes the proceedings of the 24th IFIP WG 5.4 International TRIZ Future Conference on AI-Powered Innovation and Inventive Design, TFC 2024, held in Cluj-Napoca, Romania, during November 6-8, 2024. The 42 full papers presented were carefully reviewed and selected from 72 submissions. They were organized in the following topical sections: Part I - AI-Driven TRIZ and Innovation Part II - Sustainable and Industrial Design with TRIZ; Digital Transformation, Industry 4.0, and Predictive Analytics; Interdisciplinary and Cognitive Approaches in TRIZ; Customer Experience and Service Innovation with TRIZ.

This book constitutes the refereed proceedings of the 21st International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2021, held virtually in September 2021 and sponsored by IFIP WG 5.4. The 28 full papers and 8 short papers presented were carefully reviewed and selected from 48 submissions. They are organized in the following thematic sections: inventiveness and TRIZ for sustainable development; TRIZ, intellectual property and smart technologies; TRIZ: expansion in breadth and depth; TRIZ, data processing and artificial intelligence; and TRIZ use and divulgation for engineering design and beyond. Chapter ‘Domain Analysis with TRIZ to Define an Effective “Design for Excellence’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book constitutes the proceedings of the 24th IFIP WG 5.4 International TRIZ Future Conference on AI-Powered Innovation and Inventive Design, TFC 2024, held in Cluj-Napoca, Romania, during November 6-8, 2024. The 42 full papers presented were carefully reviewed and selected from 72 submissions. They were organized in the following topical sections: Part I - AI-Driven TRIZ and Innovation Part II - Sustainable and Industrial Design with TRIZ; Digital Transformation, Industry 4.0, and Predictive Analytics; Interdisciplinary and Cognitive Approaches in TRIZ; Customer Experience and Service Innovation with TRIZ.

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.

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more